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Neural Network Optimization

3,649 papers

Papers per year

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Papers

Theory-Inspired Path-Regularized Differential Network Architecture Search NIPS 2020
Reparameterizing Mirror Descent as Gradient Descent NIPS 2020
Bad Global Minima Exist and SGD Can Reach Them NIPS 2020
A Closer Look at Accuracy vs. Robustness NIPS 2020
Curriculum Learning by Dynamic Instance Hardness NIPS 2020
AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning NIPS 2020
Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach NIPS 2020
Neural Networks Learning and Memorization with (almost) no Over-Parameterization NIPS 2020
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures NIPS 2020
Learning with Differentiable Pertubed Optimizers NIPS 2020
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NIPS 2020
The Pitfalls of Simplicity Bias in Neural Networks NIPS 2020
Provably Efficient Neural GTD for Off-Policy Learning NIPS 2020
Stable and expressive recurrent vision models NIPS 2020
Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth NIPS 2020
A mathematical model for automatic differentiation in machine learning NIPS 2020
Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks NIPS 2020
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error NIPS 2020
Learning to solve TV regularised problems with unrolled algorithms NIPS 2020
Proximal Mapping for Deep Regularization NIPS 2020
A shooting formulation of deep learning NIPS 2020
Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology NIPS 2020
Improving Neural Network Training in Low Dimensional Random Bases NIPS 2020
Escaping Saddle-Point Faster under Interpolation-like Conditions NIPS 2020
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients NIPS 2020