Optimization & Theory

OMPQ: Orthogonal Mixed Precision Quantization AAAI 2023 OMS-DPM: Optimizing the Model Schedule for Diffusion Probabilistic Models ICML 2023 On amortizing convex conjugates for optimal transport ICLR 2023 On Bridging the Gap between Mean Field and Finite Width Deep Random Multilayer Perceptron with Batch Normalization ICML 2023 Once is Enough: A Light-Weight Cross-Attention for Fast Sentence Pair Modeling EMNLP 2023 On Data Scaling in Masked Image Modeling CVPR 2023 On double-descent in uncertainty quantification in overparametrized models AISTATS 2023 One Network, Many Masks: Towards More Parameter-Efficient Transfer Learning ACL 2023 On Enhancing Expressive Power via Compositions of Single Fixed-Size ReLU Network ICML 2023 On Enhancing Fine-Tuning for Pre-trained Language Models EMNLP 2023 One-Shot Model for Mixed-Precision Quantization CVPR 2023 One-Step Diffusion Distillation via Deep Equilibrium Models NeurIPS 2023 On Learning Rates and Schrödinger Operators JMLR 2023 On quantum backpropagation, information reuse, and cheating measurement collapse NeurIPS 2023 On “Scientific Debt” in NLP: A Case for More Rigour in Language Model Pre-Training Research ACL 2023 On Sparse Modern Hopfield Model NeurIPS 2023 On the Ability of Graph Neural Networks to Model Interactions Between Vertices NeurIPS 2023 On the Accelerated Noise-Tolerant Power Method AISTATS 2023 On the Blind Spots of Model-Based Evaluation Metrics for Text Generation ACL 2023 On the Calibration of Large Language Models and Alignment EMNLP 2023 On the Convergence of AdaGrad(Norm) on R^d: Beyond Convexity, Non-Asymptotic Rate and Acceleration ICLR 2023 On the Convergence of Gradient Flow on Multi-layer Linear Models ICML 2023 On the Convergence of Stochastic Gradient Descent with Bandwidth-based Step Size JMLR 2023 On the Correctness of Automatic Differentiation for Neural Networks with Machine-Representable Parameters ICML 2023 On the Dimensionality of Sentence Embeddings EMNLP 2023