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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)

Understanding Sparse JL for Feature Hashing NIPS 2019
SpiderBoost and Momentum: Faster Variance Reduction Algorithms NIPS 2019
High-Dimensional Optimization in Adaptive Random Subspaces NIPS 2019
Cold Case: The Lost MNIST Digits NIPS 2019
Demystifying Black-box Models with Symbolic Metamodels NIPS 2019
What Can ResNet Learn Efficiently, Going Beyond Kernels? NIPS 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient NIPS 2019
Focused Quantization for Sparse CNNs NIPS 2019
Implicit Regularization for Optimal Sparse Recovery NIPS 2019
Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks NIPS 2019
Are deep ResNets provably better than linear predictors? NIPS 2019
E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings NIPS 2019
A Solvable High-Dimensional Model of GAN NIPS 2019
Efficient Forward Architecture Search NIPS 2019
Sobolev Independence Criterion NIPS 2019
Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions NIPS 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
Approximate Feature Collisions in Neural Nets NIPS 2019
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback NIPS 2019
Coresets for Clustering with Fairness Constraints NIPS 2019
You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle NIPS 2019
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel NIPS 2019
Memory Efficient Adaptive Optimization NIPS 2019
Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time NIPS 2019
Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks NIPS 2019