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Optimization & Theory

6,829 papers (27 classified directly here)

Subtopics

Model Compression (1,785) Optimization (1,664) Efficient Computing (1,579) Theory (1,082) Neural Network Optimization (903) Evaluation (509) Loss Functions (123) Interpretability (105) Stochastic Methods (97) Regularization (77) Generalization (24) Sparse Optimization (7)

Papers per year

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Papers (including subtopics)

On the Frequency Bias of Generative Models NIPS 2021
Training Over-parameterized Models with Non-decomposable Objectives NIPS 2021
Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices NIPS 2021
Training Neural Networks is ER-complete NIPS 2021
Fast Certified Robust Training with Short Warmup NIPS 2021
Relative Flatness and Generalization NIPS 2021
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining NIPS 2021
Heavy Ball Neural Ordinary Differential Equations NIPS 2021
On the Sample Complexity of Learning under Geometric Stability NIPS 2021
Efficient and Accurate Gradients for Neural SDEs NIPS 2021
Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms NIPS 2021
Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics NIPS 2021
Fast Axiomatic Attribution for Neural Networks NIPS 2021
Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis NIPS 2021
Only Train Once: A One-Shot Neural Network Training And Pruning Framework NIPS 2021
Fast and Memory Efficient Differentially Private-SGD via JL Projections NIPS 2021
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations NIPS 2021
Boost Neural Networks by Checkpoints NIPS 2021
Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates NIPS 2021
Bounds all around: training energy-based models with bidirectional bounds NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights NIPS 2021
Adder Attention for Vision Transformer NIPS 2021
Reverse engineering learned optimizers reveals known and novel mechanisms NIPS 2021
Chasing Sparsity in Vision Transformers: An End-to-End Exploration NIPS 2021
Hessian Eigenspectra of More Realistic Nonlinear Models NIPS 2021