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Optimization & Theory

27 directly classified papers

Subtopics

Model Compression (1674) Optimization (1638) Efficient Computing (1253) Theory (1072) Neural Network Optimization (902) Evaluation (345) Loss Functions (122) Stochastic Methods (97) Regularization (54) Interpretability (49) Robustness (38) Uncertainty Quantification (21) Neural Architecture Search (20) Generalization (17) Sparse Optimization (7) Representation Learning (6) Distributed Learning (6)

Papers per year

Papers

“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization ACL 2025
Sticking to the Mean: Detecting Sticky Tokens in Text Embedding Models ACL 2025
Circuit Complexity Bounds for RoPE-based Transformer Architecture EMNLP 2025
StitchLLM: Serving LLMs, One Block at a Time ACL 2025
Emergence of Hidden Capabilities: Exploring Learning Dynamics in Concept Space NIPS 2024
Can Biases in ImageNet Models Explain Generalization? CVPR 2024
Understanding Generalizability of Diffusion Models Requires Rethinking the Hidden Gaussian Structure NIPS 2024
A Canonicalization Perspective on Invariant and Equivariant Learning NIPS 2024
The Closeness of In-Context Learning and Weight Shifting for Softmax Regression NIPS 2024
Induced Model Matching: Restricted Models Help Train Full-Featured Models NIPS 2024
Nonparametric Classification on Low Dimensional Manifolds using Overparameterized Convolutional Residual Networks NIPS 2024
Learning Infinitesimal Generators of Continuous Symmetries from Data NIPS 2024
Scaling Laws vs Model Architectures: How does Inductive Bias Influence Scaling? EMNLP 2023
Re-Basin via Implicit Sinkhorn Differentiation CVPR 2023
Reliable Measures of Spread in High Dimensional Latent Spaces ICML 2023
SLACK: Stable Learning of Augmentations With Cold-Start and KL Regularization CVPR 2023
Deep Generalized Unfolding Networks for Image Restoration CVPR 2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations NIPS 2022
Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent NIPS 2022
Automatic Relation-Aware Graph Network Proliferation CVPR 2022
Scalable Verification of Quantized Neural Networks AAAI 2021
Coupled Gradient Estimators for Discrete Latent Variables NIPS 2021
Physics-Informed Deep Learning for Traffic State Estimation: A Hybrid Paradigm Informed By Second-Order Traffic Models AAAI 2021
New Interpretations of Normalization Methods in Deep Learning AAAI 2020
Sensitivity Analysis of Deep Neural Networks AAAI 2019