489 results on '"Tommi S. Jaakkola"'
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2. Prediction of Organic Reaction Outcomes Using Machine Learning
3. Revisiting Who's Harry Potter: Towards Targeted Unlearning from a Causal Intervention Perspective.
4. Correcting Diffusion Generation Through Resampling.
5. Virtual node graph neural network for full phonon prediction.
6. AlphaFold Meets Flow Matching for Generating Protein Ensembles.
7. DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents.
8. Dirichlet Flow Matching with Applications to DNA Sequence Design.
9. Generative Flows on Discrete State-Spaces: Enabling Multimodal Flows with Applications to Protein Co-Design.
10. Harmonic Self-Conditioned Flow Matching for joint Multi-Ligand Docking and Binding Site Design.
11. Removing Biases from Molecular Representations via Information Maximization.
12. MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design.
13. Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.
14. Deep Confident Steps to New Pockets: Strategies for Docking Generalization.
15. Conformal Language Modeling.
16. Improving protein optimization with smoothed fitness landscapes.
17. Equivariant Scalar Fields for Molecular Docking with Fast Fourier Transforms.
18. SE(3) diffusion model with application to protein backbone generation.
19. PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.
20. Towards Coherent Image Inpainting Using Denoising Diffusion Implicit Models.
21. A Cosmic-Scale Benchmark for Symmetry-Preserving Data Processing.
22. Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning.
23. An Information Criterion for Controlled Disentanglement of Multimodal Data.
24. Hamiltonian Score Matching and Generative Flows.
25. Generator Matching: Generative modeling with arbitrary Markov processes.
26. Generative Modeling of Molecular Dynamics Trajectories.
27. Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein Design.
28. Predicting perturbation targets with causal differential networks.
29. Think While You Generate: Discrete Diffusion with Planned Denoising.
30. Structural Constraint Integration in Generative Model for Discovery of Quantum Material Candidates.
31. Improved motif-scaffolding with SE(3) flow matching.
32. Sample, estimate, aggregate: A recipe for causal discovery foundation models.
33. A Recipe for Charge Density Prediction.
34. Verlet Flows: Exact-Likelihood Integrators for Flow-Based Generative Models.
35. In-Context Symmetries: Self-Supervised Learning through Contextual World Models.
36. Subspace Diffusion Generative Models.
37. EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction.
38. Antibody-Antigen Docking and Design via Hierarchical Structure Refinement.
39. Conformal Prediction Sets with Limited False Positives.
40. Stable Target Field for Reduced Variance Score Estimation in Diffusion Models.
41. Efficiently Controlling Multiple Risks with Pareto Testing.
42. Diffusion Probabilistic Modeling of Protein Backbones in 3D for the motif-scaffolding problem.
43. Is Conditional Generative Modeling all you need for Decision Making?
44. DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.
45. Compositional Sculpting of Iterative Generative Processes.
46. Compositional Foundation Models for Hierarchical Planning.
47. Restart Sampling for Improving Generative Processes.
48. Fundamental Limits and Tradeoffs in Invariant Representation Learning.
49. Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-scale Graph Networks.
50. Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations.
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