← Optimization & Theory
Machine Learning › Optimization & Theory ›

Neural Network Optimization

3648 directly classified papers

Papers per year

Papers

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters ICML 2020
Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy NIPS 2020
Linearly Converging Error Compensated SGD NIPS 2020
Unfolding recurrence by Green’s functions for optimized reservoir computing NIPS 2020
Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks NIPS 2020
Landscape Connectivity and Dropout Stability of SGD Solutions for Over-parameterized Neural Networks ICML 2020
Biologically Inspired Sleep Algorithm for Reducing Catastrophic Forgetting in Neural Networks AAAI 2020
A Study on Encodings for Neural Architecture Search NIPS 2020
Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality NIPS 2020
Adaptive Gradient Quantization for Data-Parallel SGD NIPS 2020
Collegial Ensembles NIPS 2020
Sparsity-Inducing Binarized Neural Networks AAAI 2020
ConvBERT: Improving BERT with Span-based Dynamic Convolution NIPS 2020
Beyond Dropout: Feature Map Distortion to Regularize Deep Neural Networks AAAI 2020
New Interpretations of Normalization Methods in Deep Learning AAAI 2020
AdaTune: Adaptive Tensor Program Compilation Made Efficient NIPS 2020
Overcoming Catastrophic Forgetting by Neuron-Level Plasticity Control AAAI 2020
The Statistical Complexity of Early-Stopped Mirror Descent NIPS 2020
The HSIC Bottleneck: Deep Learning without Back-Propagation AAAI 2020
Stochastic Loss Function AAAI 2020
Retiarii: A Deep Learning Exploratory-Training Framework OSDI 2020
Adaptive Convolutional ReLUs AAAI 2020
A Constraint-Based Approach to Learning and Explanation AAAI 2020
Biologically Plausible Sequence Learning with Spiking Neural Networks AAAI 2020
On Warm-Starting Neural Network Training NIPS 2020