Theory

Provable Dynamic Fusion for Low-Quality Multimodal Data ICML 2023 Provable Memorization Capacity of Transformers ICLR 2023 Provably and Practically Efficient Neural Contextual Bandits ICML 2023 Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation ICML 2023 Randomized Positional Encodings Boost Length Generalization of Transformers ACL 2023 Representational Strengths and Limitations of Transformers NeurIPS 2023 Reversible and irreversible bracket-based dynamics for deep graph neural networks NeurIPS 2023 Robust Counterfactual Explanations for Neural Networks With Probabilistic Guarantees ICML 2023 Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models ICML 2023 Scalable Transformer for PDE Surrogate Modeling NeurIPS 2023 Scaling Law for Document Neural Machine Translation EMNLP 2023 Scaling Laws for Discriminative Speech Recognition Rescoring Models Interspeech 2023 Scaling Laws for Generative Mixed-Modal Language Models ICML 2023 Scaling Laws for Multilingual Neural Machine Translation ICML 2023 Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? EMNLP 2023 Second-order regression models exhibit progressive sharpening to the edge of stability ICML 2023 Sequential Integrated Gradients: a simple but effective method for explaining language models ACL 2023 SGD learning on neural networks: leap complexity and saddle-to-saddle dynamics COLT 2023 Sharpness-Aware Minimization Leads to Low-Rank Features NeurIPS 2023 Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions ACL 2023 SmoothHess: ReLU Network Feature Interactions via Stein's Lemma NeurIPS 2023 Smoothing the Landscape Boosts the Signal for SGD: Optimal Sample Complexity for Learning Single Index Models NeurIPS 2023 Some Might Say All You Need Is Sum IJCAI 2023 SplineCam: Exact Visualization and Characterization of Deep Network Geometry and Decision Boundaries CVPR 2023 Spread Flows for Manifold Modelling AISTATS 2023