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1. Gemma 2: Improving Open Language Models at a Practical Size

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

3. Gemini: A Family of Highly Capable Multimodal Models

4. Beyond Human Data: Scaling Self-Training for Problem-Solving with Language Models

5. Convexifying Transformers: Improving optimization and understanding of transformer networks

6. Layer-Stack Temperature Scaling

7. Teaching Algorithmic Reasoning via In-context Learning

8. REPAIR: REnormalizing Permuted Activations for Interpolation Repair

9. Revisiting Neural Scaling Laws in Language and Vision

10. Exploring Length Generalization in Large Language Models

11. Solving Quantitative Reasoning Problems with Language Models

12. Long Range Language Modeling via Gated State Spaces

13. Understanding the effect of sparsity on neural networks robustness

14. Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

15. Block-Recurrent Transformers

16. Data Scaling Laws in NMT: The Effect of Noise and Architecture

17. Leveraging Unlabeled Data to Predict Out-of-Distribution Performance

18. The Role of Permutation Invariance in Linear Mode Connectivity of Neural Networks

19. A Loss Curvature Perspective on Training Instability in Deep Learning

20. Exploring the Limits of Large Scale Pre-training

21. The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning

22. Deep Learning Through the Lens of Example Difficulty

23. NeurIPS 2020 Competition: Predicting Generalization in Deep Learning

24. When Do Curricula Work?

25. Understanding the Failure Modes of Out-of-Distribution Generalization

26. Are wider nets better given the same number of parameters?

27. The Deep Bootstrap Framework: Good Online Learners are Good Offline Generalizers

28. Sharpness-Aware Minimization for Efficiently Improving Generalization

29. Extreme Memorization via Scale of Initialization

30. What is being transferred in transfer learning?

31. Towards Learning Convolutions from Scratch

32. Observational Overfitting in Reinforcement Learning

33. Fantastic Generalization Measures and Where to Find Them

34. The intriguing role of module criticality in the generalization of deep networks

35. Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks

36. Stronger generalization bounds for deep nets via a compression approach

37. Implicit Regularization in Deep Learning

38. A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks

39. Exploring Generalization in Deep Learning

40. Implicit Regularization in Matrix Factorization

41. Stabilizing GAN Training with Multiple Random Projections

42. Geometry of Optimization and Implicit Regularization in Deep Learning

43. Corralling a Band of Bandit Algorithms

44. Path-Normalized Optimization of Recurrent Neural Networks with ReLU Activations

45. Global Optimality of Local Search for Low Rank Matrix Recovery

46. Data-Dependent Path Normalization in Neural Networks

47. Path-SGD: Path-Normalized Optimization in Deep Neural Networks

48. Norm-Based Capacity Control in Neural Networks

49. In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning

50. On Symmetric and Asymmetric LSHs for Inner Product Search

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