Search

Your search keyword '"Yang, Sherry"' showing total 386 results
Start Over Author "Yang, Sherry"
386 results on '"Yang, Sherry"'

1. VideoAgent: Self-Improving Video Generation

Author

Soni, Achint, Venkataraman, Sreyas, Chandra, Abhranil, Fischmeister, Sebastian, Liang, Percy, Dai, Bo, and Yang, Sherry

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Video generation has been used to generate visual plans for controlling robotic systems. Given an image observation and a language instruction, previous work has generated video plans which are then converted to robot controls to be executed. However, a major bottleneck in leveraging video generation for control lies in the quality of the generated videos, which often suffer from hallucinatory content and unrealistic physics, resulting in low task success when control actions are extracted from the generated videos. While scaling up dataset and model size provides a partial solution, integrating external feedback is both natural and essential for grounding video generation in the real world. With this observation, we propose VideoAgent for self-improving generated video plans based on external feedback. Instead of directly executing the generated video plan, VideoAgent first refines the generated video plans using a novel procedure which we call self-conditioning consistency, utilizing feedback from a pretrained vision-language model (VLM). As the refined video plan is being executed, VideoAgent collects additional data from the environment to further improve video plan generation. Experiments in simulated robotic manipulation from MetaWorld and iTHOR show that VideoAgent drastically reduces hallucination, thereby boosting success rate of downstream manipulation tasks. We further illustrate that VideoAgent can effectively refine real-robot videos, providing an early indicator that robotics can be an effective tool in grounding video generation in the physical world.

Published
2024

2. Generative Hierarchical Materials Search

Author

Yang, Sherry, Batzner, Simon, Gao, Ruiqi, Aykol, Muratahan, Gaunt, Alexander L., McMorrow, Brendan, Rezende, Danilo J., Schuurmans, Dale, Mordatch, Igor, and Cubuk, Ekin D.

Subjects
Condensed Matter - Materials Science, Computer Science - Artificial Intelligence
Abstract

Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from the domain expert in the form of high-level instructions can be essential for an automated system to output candidate crystals that are viable for downstream research. In this work, we formulate end-to-end language-to-structure generation as a multi-objective optimization problem, and propose Generative Hierarchical Materials Search (GenMS) for controllable generation of crystal structures. GenMS consists of (1) a language model that takes high-level natural language as input and generates intermediate textual information about a crystal (e.g., chemical formulae), and (2) a diffusion model that takes intermediate information as input and generates low-level continuous value crystal structures. GenMS additionally uses a graph neural network to predict properties (e.g., formation energy) from the generated crystal structures. During inference, GenMS leverages all three components to conduct a forward tree search over the space of possible structures. Experiments show that GenMS outperforms other alternatives of directly using language models to generate structures both in satisfying user request and in generating low-energy structures. We confirm that GenMS is able to generate common crystal structures such as double perovskites, or spinels, solely from natural language input, and hence can form the foundation for more complex structure generation in near future., Comment: https://generative-materials.github.io/

Published
2024

3. UQE: A Query Engine for Unstructured Databases

Author

Dai, Hanjun, Wang, Bethany Yixin, Wan, Xingchen, Dai, Bo, Yang, Sherry, Nova, Azade, Yin, Pengcheng, Phothilimthana, Phitchaya Mangpo, Sutton, Charles, and Schuurmans, Dale

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Analytics on structured data is a mature field with many successful methods. However, most real world data exists in unstructured form, such as images and conversations. We investigate the potential of Large Language Models (LLMs) to enable unstructured data analytics. In particular, we propose a new Universal Query Engine (UQE) that directly interrogates and draws insights from unstructured data collections. This engine accepts queries in a Universal Query Language (UQL), a dialect of SQL that provides full natural language flexibility in specifying conditions and operators. The new engine leverages the ability of LLMs to conduct analysis of unstructured data, while also allowing us to exploit advances in sampling and optimization techniques to achieve efficient and accurate query execution. In addition, we borrow techniques from classical compiler theory to better orchestrate the workflow between sampling methods and foundation model calls. We demonstrate the efficiency of UQE on data analytics across different modalities, including images, dialogs and reviews, across a range of useful query types, including conditional aggregation, semantic retrieval and abstraction aggregation.

Published
2024

4. Value-Incentivized Preference Optimization: A Unified Approach to Online and Offline RLHF

Author

Cen, Shicong, Mei, Jincheng, Goshvadi, Katayoon, Dai, Hanjun, Yang, Tong, Yang, Sherry, Schuurmans, Dale, Chi, Yuejie, and Dai, Bo

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Statistics - Machine Learning
Abstract

Reinforcement learning from human feedback (RLHF) has demonstrated great promise in aligning large language models (LLMs) with human preference. Depending on the availability of preference data, both online and offline RLHF are active areas of investigation. A key bottleneck is understanding how to incorporate uncertainty estimation in the reward function learned from the preference data for RLHF, regardless of how the preference data is collected. While the principles of optimism or pessimism under uncertainty are well-established in standard reinforcement learning (RL), a practically-implementable and theoretically-grounded form amenable to large language models is not yet available, as standard techniques for constructing confidence intervals become intractable under arbitrary policy parameterizations. In this paper, we introduce a unified approach to online and offline RLHF -- value-incentivized preference optimization (VPO) -- which regularizes the maximum-likelihood estimate of the reward function with the corresponding value function, modulated by a $\textit{sign}$ to indicate whether the optimism or pessimism is chosen. VPO also directly optimizes the policy with implicit reward modeling, and therefore shares a simpler RLHF pipeline similar to direct preference optimization. Theoretical guarantees of VPO are provided for both online and offline settings, matching the rates of their standard RL counterparts. Moreover, experiments on text summarization and dialog verify the practicality and effectiveness of VPO.

Published
2024

5. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Author

Gemini Team, Georgiev, Petko, Lei, Ving Ian, Burnell, Ryan, Bai, Libin, Gulati, Anmol, Tanzer, Garrett, Vincent, Damien, Pan, Zhufeng, Wang, Shibo, Mariooryad, Soroosh, Ding, Yifan, Geng, Xinyang, Alcober, Fred, Frostig, Roy, Omernick, Mark, Walker, Lexi, Paduraru, Cosmin, Sorokin, Christina, Tacchetti, Andrea, Gaffney, Colin, Daruki, Samira, Sercinoglu, Olcan, Gleicher, Zach, Love, Juliette, Voigtlaender, Paul, Jain, Rohan, Surita, Gabriela, Mohamed, Kareem, Blevins, Rory, Ahn, Junwhan, Zhu, Tao, Kawintiranon, Kornraphop, Firat, Orhan, Gu, Yiming, Zhang, Yujing, Rahtz, Matthew, Faruqui, Manaal, Clay, Natalie, Gilmer, Justin, Co-Reyes, JD, Penchev, Ivo, Zhu, Rui, Morioka, Nobuyuki, Hui, Kevin, Haridasan, Krishna, Campos, Victor, Mahdieh, Mahdis, Guo, Mandy, Hassan, Samer, Kilgour, Kevin, Vezer, Arpi, Cheng, Heng-Tze, de Liedekerke, Raoul, Goyal, Siddharth, Barham, Paul, Strouse, DJ, Noury, Seb, Adler, Jonas, Sundararajan, Mukund, Vikram, Sharad, Lepikhin, Dmitry, Paganini, Michela, Garcia, Xavier, Yang, Fan, Valter, Dasha, Trebacz, Maja, Vodrahalli, Kiran, Asawaroengchai, Chulayuth, Ring, Roman, Kalb, Norbert, Soares, Livio Baldini, Brahma, Siddhartha, Steiner, David, Yu, Tianhe, Mentzer, Fabian, He, Antoine, Gonzalez, Lucas, Xu, Bibo, Kaufman, Raphael Lopez, Shafey, Laurent El, Oh, Junhyuk, Hennigan, Tom, Driessche, George van den, Odoom, Seth, Lucic, Mario, Roelofs, Becca, Lall, Sid, Marathe, Amit, Chan, Betty, Ontanon, Santiago, He, Luheng, Teplyashin, Denis, Lai, Jonathan, Crone, Phil, Damoc, Bogdan, Ho, Lewis, Riedel, Sebastian, Lenc, Karel, Yeh, Chih-Kuan, Chowdhery, Aakanksha, Xu, Yang, Kazemi, Mehran, Amid, Ehsan, Petrushkina, Anastasia, Swersky, Kevin, Khodaei, Ali, Chen, Gowoon, Larkin, Chris, Pinto, Mario, Yan, Geng, Badia, Adria Puigdomenech, Patil, Piyush, Hansen, Steven, Orr, Dave, Arnold, Sebastien M. R., Grimstad, Jordan, Dai, Andrew, Douglas, Sholto, Sinha, Rishika, Yadav, Vikas, Chen, Xi, Gribovskaya, Elena, Austin, Jacob, Zhao, Jeffrey, Patel, Kaushal, Komarek, Paul, Austin, Sophia, Borgeaud, Sebastian, Friso, Linda, Goyal, Abhimanyu, Caine, Ben, Cao, Kris, Chung, Da-Woon, Lamm, Matthew, Barth-Maron, Gabe, Kagohara, Thais, Olszewska, Kate, Chen, Mia, Shivakumar, Kaushik, Agarwal, Rishabh, Godhia, Harshal, Rajwar, Ravi, Snaider, Javier, Dotiwalla, Xerxes, Liu, Yuan, Barua, Aditya, Ungureanu, Victor, Zhang, Yuan, Batsaikhan, Bat-Orgil, Wirth, Mateo, Qin, James, Danihelka, Ivo, Doshi, Tulsee, Chadwick, Martin, Chen, Jilin, Jain, Sanil, Le, Quoc, Kar, Arjun, Gurumurthy, Madhu, Li, Cheng, Sang, Ruoxin, Liu, Fangyu, Lamprou, Lampros, Munoz, Rich, Lintz, Nathan, Mehta, Harsh, Howard, Heidi, Reynolds, Malcolm, Aroyo, Lora, Wang, Quan, Blanco, Lorenzo, Cassirer, Albin, Griffith, Jordan, Das, Dipanjan, Lee, Stephan, Sygnowski, Jakub, Fisher, Zach, Besley, James, Powell, Richard, Ahmed, Zafarali, Paulus, Dominik, Reitter, David, Borsos, Zalan, Joshi, Rishabh, Pope, Aedan, Hand, Steven, Selo, Vittorio, Jain, Vihan, Sethi, Nikhil, Goel, Megha, Makino, Takaki, May, Rhys, Yang, Zhen, Schalkwyk, Johan, Butterfield, Christina, Hauth, Anja, Goldin, Alex, Hawkins, Will, Senter, Evan, Brin, Sergey, Woodman, Oliver, Ritter, Marvin, Noland, Eric, Giang, Minh, Bolina, Vijay, Lee, Lisa, Blyth, Tim, Mackinnon, Ian, Reid, Machel, Sarvana, Obaid, Silver, David, Chen, Alexander, Wang, Lily, Maggiore, Loren, Chang, Oscar, Attaluri, Nithya, Thornton, Gregory, Chiu, Chung-Cheng, Bunyan, Oskar, Levine, Nir, Chung, Timothy, Eltyshev, Evgenii, Si, Xiance, Lillicrap, Timothy, Brady, Demetra, Aggarwal, Vaibhav, Wu, Boxi, Xu, Yuanzhong, McIlroy, Ross, Badola, Kartikeya, Sandhu, Paramjit, Moreira, Erica, Stokowiec, Wojciech, Hemsley, Ross, Li, Dong, Tudor, Alex, Shyam, Pranav, Rahimtoroghi, Elahe, Haykal, Salem, Sprechmann, Pablo, Zhou, Xiang, Mincu, Diana, Li, Yujia, Addanki, Ravi, Krishna, Kalpesh, Wu, Xiao, Frechette, Alexandre, Eyal, Matan, Dafoe, Allan, Lacey, Dave, Whang, Jay, Avrahami, Thi, Zhang, Ye, Taropa, Emanuel, Lin, Hanzhao, Toyama, Daniel, Rutherford, Eliza, Sano, Motoki, Choe, HyunJeong, Tomala, Alex, Safranek-Shrader, Chalence, Kassner, Nora, Pajarskas, Mantas, Harvey, Matt, Sechrist, Sean, Fortunato, Meire, Lyu, Christina, Elsayed, Gamaleldin, Kuang, Chenkai, Lottes, James, Chu, Eric, Jia, Chao, Chen, Chih-Wei, Humphreys, Peter, Baumli, Kate, Tao, Connie, Samuel, Rajkumar, Santos, Cicero Nogueira dos, Andreassen, Anders, Rakićević, Nemanja, Grewe, Dominik, Kumar, Aviral, Winkler, Stephanie, Caton, Jonathan, Brock, Andrew, Dalmia, Sid, Sheahan, Hannah, Barr, Iain, Miao, Yingjie, Natsev, Paul, Devlin, Jacob, Behbahani, Feryal, Prost, Flavien, Sun, Yanhua, Myaskovsky, Artiom, Pillai, Thanumalayan Sankaranarayana, Hurt, Dan, Lazaridou, Angeliki, Xiong, Xi, Zheng, Ce, Pardo, Fabio, Li, Xiaowei, Horgan, Dan, Stanton, Joe, Ambar, Moran, Xia, Fei, Lince, Alejandro, Wang, Mingqiu, Mustafa, Basil, Webson, Albert, Lee, Hyo, Anil, Rohan, Wicke, Martin, Dozat, Timothy, Sinha, Abhishek, Piqueras, Enrique, Dabir, Elahe, Upadhyay, Shyam, Boral, Anudhyan, Hendricks, Lisa Anne, Fry, Corey, Djolonga, Josip, Su, Yi, Walker, Jake, Labanowski, Jane, Huang, Ronny, Misra, Vedant, Chen, Jeremy, Skerry-Ryan, RJ, Singh, Avi, Rijhwani, Shruti, Yu, Dian, Castro-Ros, Alex, Changpinyo, Beer, Datta, Romina, Bagri, Sumit, Hrafnkelsson, Arnar Mar, Maggioni, Marcello, Zheng, Daniel, Sulsky, Yury, Hou, Shaobo, Paine, Tom Le, Yang, Antoine, Riesa, Jason, Rogozinska, Dominika, Marcus, Dror, Badawy, Dalia El, Zhang, Qiao, Wang, Luyu, Miller, Helen, Greer, Jeremy, Sjos, Lars Lowe, Nova, Azade, Zen, Heiga, Chaabouni, Rahma, Rosca, Mihaela, Jiang, Jiepu, Chen, Charlie, Liu, Ruibo, Sainath, Tara, Krikun, Maxim, Polozov, Alex, Lespiau, Jean-Baptiste, Newlan, Josh, Cankara, Zeyncep, Kwak, Soo, Xu, Yunhan, Chen, Phil, Coenen, Andy, Meyer, Clemens, Tsihlas, Katerina, Ma, Ada, Gottweis, Juraj, Xing, Jinwei, Gu, Chenjie, Miao, Jin, Frank, Christian, Cankara, Zeynep, Ganapathy, Sanjay, Dasgupta, Ishita, Hughes-Fitt, Steph, Chen, Heng, Reid, David, Rong, Keran, Fan, Hongmin, van Amersfoort, Joost, Zhuang, Vincent, Cohen, Aaron, Gu, Shixiang Shane, Mohananey, Anhad, Ilic, Anastasija, Tobin, Taylor, Wieting, John, Bortsova, Anna, Thacker, Phoebe, Wang, Emma, Caveness, Emily, Chiu, Justin, Sezener, Eren, Kaskasoli, Alex, Baker, Steven, Millican, Katie, Elhawaty, Mohamed, Aisopos, Kostas, Lebsack, Carl, Byrd, Nathan, Dai, Hanjun, Jia, Wenhao, Wiethoff, Matthew, Davoodi, Elnaz, Weston, Albert, Yagati, Lakshman, Ahuja, Arun, Gao, Isabel, Pundak, Golan, Zhang, Susan, Azzam, Michael, Sim, Khe Chai, Caelles, Sergi, Keeling, James, Sharma, Abhanshu, Swing, Andy, Li, YaGuang, Liu, Chenxi, Bostock, Carrie Grimes, Bansal, Yamini, Nado, Zachary, Anand, Ankesh, Lipschultz, Josh, Karmarkar, Abhijit, Proleev, Lev, Ittycheriah, Abe, Yeganeh, Soheil Hassas, Polovets, George, Faust, Aleksandra, Sun, Jiao, Rrustemi, Alban, Li, Pen, Shivanna, Rakesh, Liu, Jeremiah, Welty, Chris, Lebron, Federico, Baddepudi, Anirudh, Krause, Sebastian, Parisotto, Emilio, Soricut, Radu, Xu, Zheng, Bloxwich, Dawn, Johnson, Melvin, Neyshabur, Behnam, Mao-Jones, Justin, Wang, Renshen, Ramasesh, Vinay, Abbas, Zaheer, Guez, Arthur, Segal, Constant, Nguyen, Duc Dung, Svensson, James, Hou, Le, York, Sarah, Milan, Kieran, Bridgers, Sophie, Gworek, Wiktor, Tagliasacchi, Marco, Lee-Thorp, James, Chang, Michael, Guseynov, Alexey, Hartman, Ale Jakse, Kwong, Michael, Zhao, Ruizhe, Kashem, Sheleem, Cole, Elizabeth, Miech, Antoine, Tanburn, Richard, Phuong, Mary, Pavetic, Filip, Cevey, Sebastien, Comanescu, Ramona, Ives, Richard, Yang, Sherry, Du, Cosmo, Li, Bo, Zhang, Zizhao, Iinuma, Mariko, Hu, Clara Huiyi, Roy, Aurko, Bijwadia, Shaan, Zhu, Zhenkai, Martins, Danilo, Saputro, Rachel, Gergely, Anita, Zheng, Steven, Jia, Dawei, Antonoglou, Ioannis, Sadovsky, Adam, Gu, Shane, Bi, Yingying, Andreev, Alek, Samangooei, Sina, Khan, Mina, Kocisky, Tomas, Filos, Angelos, Kumar, Chintu, Bishop, Colton, Yu, Adams, Hodkinson, Sarah, Mittal, Sid, Shah, Premal, Moufarek, Alexandre, Cheng, Yong, Bloniarz, Adam, Lee, Jaehoon, Pejman, Pedram, Michel, Paul, Spencer, Stephen, Feinberg, Vladimir, Xiong, Xuehan, Savinov, Nikolay, Smith, Charlotte, Shakeri, Siamak, Tran, Dustin, Chesus, Mary, Bohnet, Bernd, Tucker, George, von Glehn, Tamara, Muir, Carrie, Mao, Yiran, Kazawa, Hideto, Slone, Ambrose, Soparkar, Kedar, Shrivastava, Disha, Cobon-Kerr, James, Sharman, Michael, Pavagadhi, Jay, Araya, Carlos, Misiunas, Karolis, Ghelani, Nimesh, Laskin, Michael, Barker, David, Li, Qiujia, Briukhov, Anton, Houlsby, Neil, Glaese, Mia, Lakshminarayanan, Balaji, Schucher, Nathan, Tang, Yunhao, Collins, Eli, Lim, Hyeontaek, Feng, Fangxiaoyu, Recasens, Adria, Lai, Guangda, Magni, Alberto, De Cao, Nicola, Siddhant, Aditya, Ashwood, Zoe, Orbay, Jordi, Dehghani, Mostafa, Brennan, Jenny, He, Yifan, Xu, Kelvin, Gao, Yang, Saroufim, Carl, Molloy, James, Wu, Xinyi, Arnold, Seb, Chang, Solomon, Schrittwieser, Julian, Buchatskaya, Elena, Radpour, Soroush, Polacek, Martin, Giordano, Skye, Bapna, Ankur, Tokumine, Simon, Hellendoorn, Vincent, Sottiaux, Thibault, Cogan, Sarah, Severyn, Aliaksei, Saleh, Mohammad, Thakoor, Shantanu, Shefey, Laurent, Qiao, Siyuan, Gaba, Meenu, Chang, Shuo-yiin, Swanson, Craig, Zhang, Biao, Lee, Benjamin, Rubenstein, Paul Kishan, Song, Gan, Kwiatkowski, Tom, Koop, Anna, Kannan, Ajay, Kao, David, Schuh, Parker, Stjerngren, Axel, Ghiasi, Golnaz, Gibson, Gena, Vilnis, Luke, Yuan, Ye, Ferreira, Felipe Tiengo, Kamath, Aishwarya, Klimenko, Ted, Franko, Ken, Xiao, Kefan, Bhattacharya, Indro, Patel, Miteyan, Wang, Rui, Morris, Alex, Strudel, Robin, Sharma, Vivek, Choy, Peter, Hashemi, Sayed Hadi, Landon, Jessica, Finkelstein, Mara, Jhakra, Priya, Frye, Justin, Barnes, Megan, Mauger, Matthew, Daun, Dennis, Baatarsukh, Khuslen, Tung, Matthew, Farhan, Wael, Michalewski, Henryk, Viola, Fabio, Quitry, Felix de Chaumont, Lan, Charline Le, Hudson, Tom, Wang, Qingze, Fischer, Felix, Zheng, Ivy, White, Elspeth, Dragan, Anca, Alayrac, Jean-baptiste, Ni, Eric, Pritzel, Alexander, Iwanicki, Adam, Isard, Michael, Bulanova, Anna, Zilka, Lukas, Dyer, Ethan, Sachan, Devendra, Srinivasan, Srivatsan, Muckenhirn, Hannah, Cai, Honglong, Mandhane, Amol, Tariq, Mukarram, Rae, Jack W., Wang, Gary, Ayoub, Kareem, FitzGerald, Nicholas, Zhao, Yao, Han, Woohyun, Alberti, Chris, Garrette, Dan, Krishnakumar, Kashyap, Gimenez, Mai, Levskaya, Anselm, Sohn, Daniel, Matak, Josip, Iturrate, Inaki, Chang, Michael B., Xiang, Jackie, Cao, Yuan, Ranka, Nishant, Brown, Geoff, Hutter, Adrian, Mirrokni, Vahab, Chen, Nanxin, Yao, Kaisheng, Egyed, Zoltan, Galilee, Francois, Liechty, Tyler, Kallakuri, Praveen, Palmer, Evan, Ghemawat, Sanjay, Liu, Jasmine, Tao, David, Thornton, Chloe, Green, Tim, Jasarevic, Mimi, Lin, Sharon, Cotruta, Victor, Tan, Yi-Xuan, Fiedel, Noah, Yu, Hongkun, Chi, Ed, Neitz, Alexander, Heitkaemper, Jens, Sinha, Anu, Zhou, Denny, Sun, Yi, Kaed, Charbel, Hulse, Brice, Mishra, Swaroop, Georgaki, Maria, Kudugunta, Sneha, Farabet, Clement, Shafran, Izhak, Vlasic, Daniel, Tsitsulin, Anton, Ananthanarayanan, Rajagopal, Carin, Alen, Su, Guolong, Sun, Pei, V, Shashank, Carvajal, Gabriel, Broder, Josef, Comsa, Iulia, Repina, Alena, Wong, William, Chen, Warren Weilun, Hawkins, Peter, Filonov, Egor, Loher, Lucia, Hirnschall, Christoph, Wang, Weiyi, Ye, Jingchen, Burns, Andrea, Cate, Hardie, Wright, Diana Gage, Piccinini, Federico, Zhang, Lei, Lin, Chu-Cheng, Gog, Ionel, Kulizhskaya, Yana, Sreevatsa, Ashwin, Song, Shuang, Cobo, Luis C., Iyer, Anand, Tekur, Chetan, Garrido, Guillermo, Xiao, Zhuyun, Kemp, Rupert, Zheng, Huaixiu Steven, Li, Hui, Agarwal, Ananth, Ngani, Christel, Goshvadi, Kati, Santamaria-Fernandez, Rebeca, Fica, Wojciech, Chen, Xinyun, Gorgolewski, Chris, Sun, Sean, Garg, Roopal, Ye, Xinyu, Eslami, S. M. Ali, Hua, Nan, Simon, Jon, Joshi, Pratik, Kim, Yelin, Tenney, Ian, Potluri, Sahitya, Thiet, Lam Nguyen, Yuan, Quan, Luisier, Florian, Chronopoulou, Alexandra, Scellato, Salvatore, Srinivasan, Praveen, Chen, Minmin, Koverkathu, Vinod, Dalibard, Valentin, Xu, Yaming, Saeta, Brennan, Anderson, Keith, Sellam, Thibault, Fernando, Nick, Huot, Fantine, Jung, Junehyuk, Varadarajan, Mani, Quinn, Michael, Raul, Amit, Le, Maigo, Habalov, Ruslan, Clark, Jon, Jalan, Komal, Bullard, Kalesha, Singhal, Achintya, Luong, Thang, Wang, Boyu, Rajayogam, Sujeevan, Eisenschlos, Julian, Jia, Johnson, Finchelstein, Daniel, Yakubovich, Alex, Balle, Daniel, Fink, Michael, Agarwal, Sameer, Li, Jing, Dvijotham, Dj, Pal, Shalini, Kang, Kai, Konzelmann, Jaclyn, Beattie, Jennifer, Dousse, Olivier, Wu, Diane, Crocker, Remi, Elkind, Chen, Jonnalagadda, Siddhartha Reddy, Lee, Jong, Holtmann-Rice, Dan, Kallarackal, Krystal, Liu, Rosanne, Vnukov, Denis, Vats, Neera, Invernizzi, Luca, Jafari, Mohsen, Zhou, Huanjie, Taylor, Lilly, Prendki, Jennifer, Wu, Marcus, Eccles, Tom, Liu, Tianqi, Kopparapu, Kavya, Beaufays, Francoise, Angermueller, Christof, Marzoca, Andreea, Sarcar, Shourya, Dib, Hilal, Stanway, Jeff, Perbet, Frank, Trdin, Nejc, Sterneck, Rachel, Khorlin, Andrey, Li, Dinghua, Wu, Xihui, Goenka, Sonam, Madras, David, Goldshtein, Sasha, Gierke, Willi, Zhou, Tong, Liu, Yaxin, Liang, Yannie, White, Anais, Li, Yunjie, Singh, Shreya, Bahargam, Sanaz, Epstein, Mark, Basu, Sujoy, Lao, Li, Ozturel, Adnan, Crous, Carl, Zhai, Alex, Lu, Han, Tung, Zora, Gaur, Neeraj, Walton, Alanna, Dixon, Lucas, Zhang, Ming, Globerson, Amir, Uy, Grant, Bolt, Andrew, Wiles, Olivia, Nasr, Milad, Shumailov, Ilia, Selvi, Marco, Piccinno, Francesco, Aguilar, Ricardo, McCarthy, Sara, Khalman, Misha, Shukla, Mrinal, Galic, Vlado, Carpenter, John, Villela, Kevin, Zhang, Haibin, Richardson, Harry, Martens, James, Bosnjak, Matko, Belle, Shreyas Rammohan, Seibert, Jeff, Alnahlawi, Mahmoud, McWilliams, Brian, Singh, Sankalp, Louis, Annie, Ding, Wen, Popovici, Dan, Simicich, Lenin, Knight, Laura, Mehta, Pulkit, Gupta, Nishesh, Shi, Chongyang, Fatehi, Saaber, Mitrovic, Jovana, Grills, Alex, Pagadora, Joseph, Petrova, Dessie, Eisenbud, Danielle, Zhang, Zhishuai, Yates, Damion, Mittal, Bhavishya, Tripuraneni, Nilesh, Assael, Yannis, Brovelli, Thomas, Jain, Prateek, Velimirovic, Mihajlo, Akbulut, Canfer, Mu, Jiaqi, Macherey, Wolfgang, Kumar, Ravin, Xu, Jun, Qureshi, Haroon, Comanici, Gheorghe, Wiesner, Jeremy, Gong, Zhitao, Ruddock, Anton, Bauer, Matthias, Felt, Nick, GP, Anirudh, Arnab, Anurag, Zelle, Dustin, Rothfuss, Jonas, Rosgen, Bill, Shenoy, Ashish, Seybold, Bryan, Li, Xinjian, Mudigonda, Jayaram, Erdogan, Goker, Xia, Jiawei, Simsa, Jiri, Michi, Andrea, Yao, Yi, Yew, Christopher, Kan, Steven, Caswell, Isaac, Radebaugh, Carey, Elisseeff, Andre, Valenzuela, Pedro, McKinney, Kay, Paterson, Kim, Cui, Albert, Latorre-Chimoto, Eri, Kim, Solomon, Zeng, William, Durden, Ken, Ponnapalli, Priya, Sosea, Tiberiu, Choquette-Choo, Christopher A., Manyika, James, Robenek, Brona, Vashisht, Harsha, Pereira, Sebastien, Lam, Hoi, Velic, Marko, Owusu-Afriyie, Denese, Lee, Katherine, Bolukbasi, Tolga, Parrish, Alicia, Lu, Shawn, Park, Jane, Venkatraman, Balaji, Talbert, Alice, Rosique, Lambert, Cheng, Yuchung, Sozanschi, Andrei, Paszke, Adam, Kumar, Praveen, Austin, Jessica, Li, Lu, Salama, Khalid, Kim, Wooyeol, Dukkipati, Nandita, Baryshnikov, Anthony, Kaplanis, Christos, Sheng, XiangHai, Chervonyi, Yuri, Unlu, Caglar, Casas, Diego de Las, Askham, Harry, Tunyasuvunakool, Kathryn, Gimeno, Felix, Poder, Siim, Kwak, Chester, Miecnikowski, Matt, Dimitriev, Alek, Parisi, Aaron, Liu, Dangyi, Tsai, Tomy, Shevlane, Toby, Kouridi, Christina, Garmon, Drew, Goedeckemeyer, Adrian, Brown, Adam R., Vijayakumar, Anitha, Elqursh, Ali, Jazayeri, Sadegh, Huang, Jin, Carthy, Sara Mc, Hoover, Jay, Kim, Lucy, Kumar, Sandeep, Chen, Wei, Biles, Courtney, Bingham, Garrett, Rosen, Evan, Wang, Lisa, Tan, Qijun, Engel, David, Pongetti, Francesco, de Cesare, Dario, Hwang, Dongseong, Yu, Lily, Pullman, Jennifer, Narayanan, Srini, Levin, Kyle, Gopal, Siddharth, Li, Megan, Aharoni, Asaf, Trinh, Trieu, Lo, Jessica, Casagrande, Norman, Vij, Roopali, Matthey, Loic, Ramadhana, Bramandia, Matthews, Austin, Carey, CJ, Johnson, Matthew, Goranova, Kremena, Shah, Rohin, Ashraf, Shereen, Dasgupta, Kingshuk, Larsen, Rasmus, Wang, Yicheng, Vuyyuru, Manish Reddy, Jiang, Chong, Ijazi, Joana, Osawa, Kazuki, Smith, Celine, Boppana, Ramya Sree, Bilal, Taylan, Koizumi, Yuma, Xu, Ying, Altun, Yasemin, Shabat, Nir, Bariach, Ben, Korchemniy, Alex, Choo, Kiam, Ronneberger, Olaf, Iwuanyanwu, Chimezie, Zhao, Shubin, Soergel, David, Hsieh, Cho-Jui, Cai, Irene, Iqbal, Shariq, Sundermeyer, Martin, Chen, Zhe, Bursztein, Elie, Malaviya, Chaitanya, Biadsy, Fadi, Shroff, Prakash, Dhillon, Inderjit, Latkar, Tejasi, Dyer, Chris, Forbes, Hannah, Nicosia, Massimo, Nikolaev, Vitaly, Greene, Somer, Georgiev, Marin, Wang, Pidong, Martin, Nina, Sedghi, Hanie, Zhang, John, Banzal, Praseem, Fritz, Doug, Rao, Vikram, Wang, Xuezhi, Zhang, Jiageng, Patraucean, Viorica, Du, Dayou, Mordatch, Igor, Jurin, Ivan, Liu, Lewis, Dubey, Ayush, Mohan, Abhi, Nowakowski, Janek, Ion, Vlad-Doru, Wei, Nan, Tojo, Reiko, Raad, Maria Abi, Hudson, Drew A., Keshava, Vaishakh, Agrawal, Shubham, Ramirez, Kevin, Wu, Zhichun, Nguyen, Hoang, Liu, Ji, Sewak, Madhavi, Petrini, Bryce, Choi, DongHyun, Philips, Ivan, Wang, Ziyue, Bica, Ioana, Garg, Ankush, Wilkiewicz, Jarek, Agrawal, Priyanka, Guo, Danhao, Xue, Emily, Shaik, Naseer, Leach, Andrew, Khan, Sadh MNM, Wiesinger, Julia, Jerome, Sammy, Chakladar, Abhishek, Wang, Alek Wenjiao, Ornduff, Tina, Abu, Folake, Ghaffarkhah, Alireza, Wainwright, Marcus, Cortes, Mario, Liu, Frederick, Maynez, Joshua, Terzis, Andreas, Samangouei, Pouya, Mansour, Riham, Kępa, Tomasz, Aubet, François-Xavier, Algymr, Anton, Banica, Dan, Weisz, Agoston, Orban, Andras, Senges, Alexandre, Andrejczuk, Ewa, Geller, Mark, Santo, Niccolo Dal, Anklin, Valentin, Merey, Majd Al, Baeuml, Martin, Strohman, Trevor, Bai, Junwen, Petrov, Slav, Wu, Yonghui, Hassabis, Demis, Kavukcuoglu, Koray, Dean, Jeffrey, and Vinyals, Oriol

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

In this report, we introduce the Gemini 1.5 family of models, representing the next generation of highly compute-efficient multimodal models capable of recalling and reasoning over fine-grained information from millions of tokens of context, including multiple long documents and hours of video and audio. The family includes two new models: (1) an updated Gemini 1.5 Pro, which exceeds the February version on the great majority of capabilities and benchmarks; (2) Gemini 1.5 Flash, a more lightweight variant designed for efficiency with minimal regression in quality. Gemini 1.5 models achieve near-perfect recall on long-context retrieval tasks across modalities, improve the state-of-the-art in long-document QA, long-video QA and long-context ASR, and match or surpass Gemini 1.0 Ultra's state-of-the-art performance across a broad set of benchmarks. Studying the limits of Gemini 1.5's long-context ability, we find continued improvement in next-token prediction and near-perfect retrieval (>99%) up to at least 10M tokens, a generational leap over existing models such as Claude 3.0 (200k) and GPT-4 Turbo (128k). Finally, we highlight real-world use cases, such as Gemini 1.5 collaborating with professionals on completing their tasks achieving 26 to 75% time savings across 10 different job categories, as well as surprising new capabilities of large language models at the frontier; when given a grammar manual for Kalamang, a language with fewer than 200 speakers worldwide, the model learns to translate English to Kalamang at a similar level to a person who learned from the same content.

Published
2024

6. Video as the New Language for Real-World Decision Making

Author

Yang, Sherry, Walker, Jacob, Parker-Holder, Jack, Du, Yilun, Bruce, Jake, Barreto, Andre, Abbeel, Pieter, and Schuurmans, Dale

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Both text and video data are abundant on the internet and support large-scale self-supervised learning through next token or frame prediction. However, they have not been equally leveraged: language models have had significant real-world impact, whereas video generation has remained largely limited to media entertainment. Yet video data captures important information about the physical world that is difficult to express in language. To address this gap, we discuss an under-appreciated opportunity to extend video generation to solve tasks in the real world. We observe how, akin to language, video can serve as a unified interface that can absorb internet knowledge and represent diverse tasks. Moreover, we demonstrate how, like language models, video generation can serve as planners, agents, compute engines, and environment simulators through techniques such as in-context learning, planning and reinforcement learning. We identify major impact opportunities in domains such as robotics, self-driving, and science, supported by recent work that demonstrates how such advanced capabilities in video generation are plausibly within reach. Lastly, we identify key challenges in video generation that mitigate progress. Addressing these challenges will enable video generation models to demonstrate unique value alongside language models in a wider array of AI applications.

Published
2024

7. Code as Reward: Empowering Reinforcement Learning with VLMs

Author

Venuto, David, Islam, Sami Nur, Klissarov, Martin, Precup, Doina, Yang, Sherry, and Anand, Ankit

Subjects
Computer Science - Machine Learning
Abstract

Pre-trained Vision-Language Models (VLMs) are able to understand visual concepts, describe and decompose complex tasks into sub-tasks, and provide feedback on task completion. In this paper, we aim to leverage these capabilities to support the training of reinforcement learning (RL) agents. In principle, VLMs are well suited for this purpose, as they can naturally analyze image-based observations and provide feedback (reward) on learning progress. However, inference in VLMs is computationally expensive, so querying them frequently to compute rewards would significantly slowdown the training of an RL agent. To address this challenge, we propose a framework named Code as Reward (VLM-CaR). VLM-CaR produces dense reward functions from VLMs through code generation, thereby significantly reducing the computational burden of querying the VLM directly. We show that the dense rewards generated through our approach are very accurate across a diverse set of discrete and continuous environments, and can be more effective in training RL policies than the original sparse environment rewards.

Published
2024

8. Scalable Diffusion for Materials Generation

Author

Yang, Sherry, Cho, KwangHwan, Merchant, Amil, Abbeel, Pieter, Schuurmans, Dale, Mordatch, Igor, and Cubuk, Ekin Dogus

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advance science, for example by generating novel stable materials. Traditionally, models with explicit structures (e.g., graphs) have been used in modeling structural relationships in scientific data (e.g., atoms and bonds in crystals), but generating structures can be difficult to scale to large and complex systems. Another challenge in generating materials is the mismatch between standard generative modeling metrics and downstream applications. For instance, common metrics such as the reconstruction error do not correlate well with the downstream goal of discovering stable materials. In this work, we tackle the scalability challenge by developing a unified crystal representation that can represent any crystal structure (UniMat), followed by training a diffusion probabilistic model on these UniMat representations. Our empirical results suggest that despite the lack of explicit structure modeling, UniMat can generate high fidelity crystal structures from larger and more complex chemical systems, outperforming previous graph-based approaches under various generative modeling metrics. To better connect the generation quality of materials to downstream applications, such as discovering novel stable materials, we propose additional metrics for evaluating generative models of materials, including per-composition formation energy and stability with respect to convex hulls through decomposition energy from Density Function Theory (DFT). Lastly, we show that conditional generation with UniMat can scale to previously established crystal datasets with up to millions of crystals structures, outperforming random structure search (the current leading method for structure discovery) in discovering new stable materials., Comment: https://unified-materials.github.io/

Published
2023

9. Learning Interactive Real-World Simulators

Author

Yang, Sherry, Du, Yilun, Ghasemipour, Kamyar, Tompson, Jonathan, Kaelbling, Leslie, Schuurmans, Dale, and Abbeel, Pieter

Subjects
Computer Science - Artificial Intelligence
Abstract

Generative models trained on internet data have revolutionized how text, image, and video content can be created. Perhaps the next milestone for generative models is to simulate realistic experience in response to actions taken by humans, robots, and other interactive agents. Applications of a real-world simulator range from controllable content creation in games and movies, to training embodied agents purely in simulation that can be directly deployed in the real world. We explore the possibility of learning a universal simulator (UniSim) of real-world interaction through generative modeling. We first make the important observation that natural datasets available for learning a real-world simulator are often rich along different dimensions (e.g., abundant objects in image data, densely sampled actions in robotics data, and diverse movements in navigation data). With careful orchestration of diverse datasets, each providing a different aspect of the overall experience, we can simulate the visual outcome of both high-level instructions such as "open the drawer" and low-level controls from otherwise static scenes and objects. We use the simulator to train both high-level vision-language policies and low-level reinforcement learning policies, each of which can be deployed in the real world in zero shot after training purely in simulation. We also show that other types of intelligence such as video captioning models can benefit from training with simulated experience, opening up even wider applications. Video demos can be found at https://universal-simulator.github.io., Comment: https://universal-simulator.github.io

Published
2023

10. Foundation Models for Decision Making: Problems, Methods, and Opportunities

Author

Yang, Sherry, Nachum, Ofir, Du, Yilun, Wei, Jason, Abbeel, Pieter, and Schuurmans, Dale

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Foundation models pretrained on diverse data at scale have demonstrated extraordinary capabilities in a wide range of vision and language tasks. When such models are deployed in real world environments, they inevitably interface with other entities and agents. For example, language models are often used to interact with human beings through dialogue, and visual perception models are used to autonomously navigate neighborhood streets. In response to these developments, new paradigms are emerging for training foundation models to interact with other agents and perform long-term reasoning. These paradigms leverage the existence of ever-larger datasets curated for multimodal, multitask, and generalist interaction. Research at the intersection of foundation models and decision making holds tremendous promise for creating powerful new systems that can interact effectively across a diverse range of applications such as dialogue, autonomous driving, healthcare, education, and robotics. In this manuscript, we examine the scope of foundation models for decision making, and provide conceptual tools and technical background for understanding the problem space and exploring new research directions. We review recent approaches that ground foundation models in practical decision making applications through a variety of methods such as prompting, conditional generative modeling, planning, optimal control, and reinforcement learning, and discuss common challenges and open problems in the field.

Published
2023

11. Multi-Environment Pretraining Enables Transfer to Action Limited Datasets

Author

Venuto, David, Yang, Sherry, Abbeel, Pieter, Precup, Doina, Mordatch, Igor, and Nachum, Ofir

Subjects
Computer Science - Machine Learning
Abstract

Using massive datasets to train large-scale models has emerged as a dominant approach for broad generalization in natural language and vision applications. In reinforcement learning, however, a key challenge is that available data of sequential decision making is often not annotated with actions - for example, videos of game-play are much more available than sequences of frames paired with their logged game controls. We propose to circumvent this challenge by combining large but sparsely-annotated datasets from a \emph{target} environment of interest with fully-annotated datasets from various other \emph{source} environments. Our method, Action Limited PreTraining (ALPT), leverages the generalization capabilities of inverse dynamics modelling (IDM) to label missing action data in the target environment. We show that utilizing even one additional environment dataset of labelled data during IDM pretraining gives rise to substantial improvements in generating action labels for unannotated sequences. We evaluate our method on benchmark game-playing environments and show that we can significantly improve game performance and generalization capability compared to other approaches, using annotated datasets equivalent to only $12$ minutes of gameplay. Highlighting the power of IDM, we show that these benefits remain even when target and source environments share no common actions.

Published
2022

15. Foundation Models for Decision Making: Algorithms, Frameworks, and Applications

Author

Yang, Sherry

Subjects
Artificial intelligence, Foundation Models, Machine Learning, Reinforcement Learning
Abstract

AlphaGo and ChatGPT are perhaps two most significant breakthroughs in artificial intelligence in the past decade. These technologies were empowered by research in sequential decision making (e.g., planning, search, and reinforcement learning) and foundation models (e.g., language and video generation model trained on internet data). This thesis proposes new techniques, algorithms, and frameworks of leveraging foundation models with broad knowledge in the context of real-world decision making tasks, impacting applications such as building dialogue agent, controlling robots, and making scientific discoveries. This thesis starts with traditional decision making in offline settings and progressively incorporating broader, internet-scale data through representation learning and generative modeling. Emphasis is placed on both theoretical foundations and practical implications. Key contributions of this thesis include algorithmic advancements of offline reinforcement learning, improved representation learning for decision making, novel generative modeling techniques as an alternative to reinforcement learning, and generative agents and generative simulators at internet scale, all aimed at equipping foundation models with enhanced decision-making capabilities and vice versa. Through extensive empirical and theoretical analysis, this thesis demonstrates that foundation models, when properly leveraged, can significantly improve decision-making tasks. The findings offer new directions for integrating machine learning models with real-world applications, paving the way for more intelligent, adaptable, and efficient systems.

Published
2024

16. PD1 inhibitor induced inverse lichenoid eruption: a case series

Author

Sethi, Mansha, Garg, Vaibhav, Lee, Jason B, and Yang, Sherry

Subjects
PD1 inhibitor, inverse lichenoid eruption, immunotherapy
Abstract

The increased use of monoclonal antibodies that target the immune checkpoint T cell receptor programmed death-1 (PD1) to treat numerous solid tumors has led to several reports describing associated cutaneous adverse events. Although lichenoid reactions have been well described, we propose that PD1 inhibitor-induced inverse lichenoid eruption (PILE) is a distinct variant. We describe two patients who presented with nearly identical deeply erythematous, malodorous, eroded anogenital plaques with focal crusting. Diagnosis of PILE was established given the biopsy findings and temporal association with PD1 inhibitor therapy. Treatment with clobetasol ointment was successful without necessitating discontinuation of immunotherapy. The findings were consistent with the only other previously published case of inverse lichenoid eruption in the groin secondary to PD1 inhibitors.

Published
2020

20. Upregulation of TET2 and Resistance to DNA Methyltransferase (DNMT) Inhibitors in DNMT1 -Deleted Cancer Cells.

Author

Laranjeira, Angelo B. A., Nguyen, Dat, Pelosof, Lorraine C., Doroshow, James H., and Yang, Sherry X.

Subjects
TUMOR suppressor proteins, GENE expression, SUPPRESSOR cells, GENETIC regulation, RENAL cancer, P16 gene
Abstract

Simple Summary: Ten-eleven-translocation (TET) 2 is implicated in human cancers such as lung, breast, skin, and kidney cancers, T-cell lymphomas, and leukemia. It is unclear whether TET2 is involved in the re-expression of the p16 tumor suppressor, which was partially silenced by CDKN2A gene methylation. The effect of DNA methyltransferase (DNMT) 1 gene status on TET2 expression following DNMT inhibitor treatment remains unknown in cancer. We found that deleting the DNMT1 gene made cancer cells prone to increased TET2 expression and re-expression of p16 after drug treatment. TET2 is involved in the demethylation of the CDKN2A gene and activation of p16 expression, and concomitant resistance to DNMT inhibitors when DNMT1 is obliterated. The long-sought data for the first time revealed a link between TET2 expression and the activation of p16 expression. The findings should have an impact on reactivating tumor suppressors and cancer treatment. Background: Ten-eleven-translocation (TET) 2 is a member of the TET family of proteins (TET1-3). DNMT1 gene deletion confers resistance to DNA methyltransferase (DNMT) inhibitors in colorectal, breast, and ovarian cancer cells. Currently, the effect of DNMT1 gene status on TET2 phenotype following DNMT inhibitor treatment is unclear in human malignancies. Methods: Human colorectal carcinoma HCT116 cells (DNMT+/+) and their isogenic DNMT1 knockout (DNMT1–/–) counterpart were treated with DNMT inhibitors. Expression of TET2 and tumor suppressor (p16ink4A and p15ink4B) proteins were examined by Western blot. Apoptosis and CDKN2A promoter demethylation following drug treatment were detected by Annexin-V apoptosis assay and methylation-specific PCR. Results: TET2 expression was robustly increased in DNMT1−/− cells by 0.5 µM and 5 µM decitabine and azacitidine treatment. Augmentation of TET2 expression was accompanied by re-expression of p16ink4A and p15ink4B proteins and CDKN2A promoter demethylation. TET2 upregulation and tumor suppressor re-expression were associated with resistance conferred by DNMT1 deletion. Treatment with 5-aza-4′-thio-2′-deoxycytidine at a low 0.5 µM dose only upregulated TET2 and reduced CDKN2A promoter methylation, and re-expression of p16ink4A in DNMT1−/− cells. DNMT inhibitors showed minimal effects on TET2 upregulation and re-expression of tumor suppressor proteins in cells with intact DNMT1. Conclusions: DNMT1 gene deletion made cancer cells prone to TET2 upregulation and activation of tumor suppressor expression upon DNMT inhibitor challenge. TET2 augmentation is concomitant with resistance to DNMT inhibitors in a DNMT1-deleted state. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

26. Hidradenitis suppurativa disease control associated with type of hormonal contraceptive use.

Author

Huang, Charles Y, Shah, Saloni A, Cochrane, Mikayla, Douglas, Annyella, and Yang, Sherry

Subjects
HIDRADENITIS suppurativa, CONTRACEPTION, PREVENTIVE medicine, APOCRINE glands, ODDS ratio
Abstract

Hidradenitis suppurativa (HS) is a painful chronic skin condition of apocrine gland regions. This retrospective cohort study aimed to assess the impact of hormonal contraception type on HS disease control in adult women. In total, 160 patients were included, with the majority identifying as Black or African American (73.1%). Multivariate logistic regression showed that oestrogen–progesterone users were 3.14 times more likely to experience stable or improved HS than progesterone-only users (adjusted odds ratio 3.14, 95% CI 1.18–8.35; P = 0.02). Further investigation is needed to elucidate the antiandrogenic mechanisms affecting HS symptom response to hormonal contraceptives. [ABSTRACT FROM AUTHOR]

Published
2024
Full Text
View/download PDF

32. Data from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

33. Supplementary Table 2 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

34. Supplementary Table 7 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

35. Supplementary Table 5 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

36. Supplementary Table 1 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

37. Supplementary Table 3 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

38. Supplementary Table 4 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

39. Supplementary Table 6 from DNA Methyltransferase Inhibitor, Zebularine, Delays Tumor Growth and Induces Apoptosis in a Genetically Engineered Mouse Model of Breast Cancer

Author

Chen, Min, primary, Shabashvili, Daniel, primary, Nawab, Akbar, primary, Yang, Sherry X., primary, Dyer, Lisa M., primary, Brown, Kevin D., primary, Hollingshead, Melinda, primary, Hunter, Kent W., primary, Kaye, Frederic J., primary, Hochwald, Steven N., primary, Marquez, Victor E., primary, Steeg, Patricia, primary, and Zajac-Kaye, Maria, primary

Published
2023
Full Text
View/download PDF

40. Supplementary Figure 3 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

42. Supplementary Figure 1 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

43. Supplementary Methods, Tables 1 - 3 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

44. Supplementary Figure 2 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

47. Supplementary Figure 6 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

48. Supplementary Figure 5 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

49. Supplementary Figure 4 from Identification and Analysis of In Vivo VEGF Downstream Markers Link VEGF Pathway Activity with Efficacy of Anti-VEGF Therapies

Author

Brauer, Matthew J., primary, Zhuang, Guanglei, primary, Schmidt, Maike, primary, Yao, Jenny, primary, Wu, Xiumin, primary, Kaminker, Joshua S., primary, Jurinka, Stefanie S., primary, Kolumam, Ganesh, primary, Chung, Alicia S., primary, Jubb, Adrian, primary, Modrusan, Zora, primary, Ozawa, Tomoko, primary, James, C. David, primary, Phillips, Heidi, primary, Haley, Benjamin, primary, Tam, Rachel N.W., primary, Clermont, Anne C., primary, Cheng, Jason H., primary, Yang, Sherry X., primary, Swain, Sandra M., primary, Chen, Daniel, primary, Scherer, Stefan J., primary, Koeppen, Hartmut, primary, Yeh, Ru-Fang, primary, Yue, Peng, primary, Stephan, Jean-Philippe, primary, Hegde, Priti, primary, Ferrara, Napoleone, primary, Singh, Mallika, primary, and Bais, Carlos, primary

Published
2023
Full Text
View/download PDF

50. Supplementary Figure 2 from Wnt5A Regulates Expression of Tumor-Associated Antigens in Melanoma via Changes in Signal Transducers and Activators of Transcription 3 Phosphorylation

Author

Dissanayake, Samudra K., primary, Olkhanud, Purevdorj B., primary, O'Connell, Michael P., primary, Carter, Arnell, primary, French, Amanda D., primary, Camilli, Tura C., primary, Emeche, Chineye D., primary, Hewitt, Kyle J., primary, Rosenthal, Devin T., primary, Leotlela, Poloko D., primary, Wade, Michael S., primary, Yang, Sherry W., primary, Brant, Larry, primary, Nickoloff, Brian J., primary, Messina, Jane L., primary, Biragyn, Arya, primary, Hoek, Keith S., primary, Taub, Dennis D., primary, Longo, Dan L., primary, Sondak, Vernon K., primary, Hewitt, Stephen M., primary, and Weeraratna, Ashani T., primary

Published
2023
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results