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Neural Network Optimization

3,649 papers

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Papers

Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient NIPS 2020
Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function NIPS 2020
Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes NIPS 2020
ShiftAddNet: A Hardware-Inspired Deep Network NIPS 2020
Logarithmic Pruning is All You Need NIPS 2020
Triple descent and the two kinds of overfitting: where & why do they appear? NIPS 2020
Adaptive Gradient Quantization for Data-Parallel SGD NIPS 2020
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval NIPS 2020
Ode to an ODE NIPS 2020
Self-Distillation Amplifies Regularization in Hilbert Space NIPS 2020
On Warm-Starting Neural Network Training NIPS 2020
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses NIPS 2020
On the training dynamics of deep networks with $L_2$ regularization NIPS 2020
Parabolic Approximation Line Search for DNNs NIPS 2020
Agnostic Learning of a Single Neuron with Gradient Descent NIPS 2020
Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry NIPS 2020
On Second Order Behaviour in Augmented Neural ODEs NIPS 2020
Predicting Training Time Without Training NIPS 2020
Neural Power Units NIPS 2020
On Correctness of Automatic Differentiation for Non-Differentiable Functions NIPS 2020
Regularized linear autoencoders recover the principal components, eventually NIPS 2020
Training Linear Finite-State Machines NIPS 2020
Understanding the Role of Training Regimes in Continual Learning NIPS 2020
Exactly Computing the Local Lipschitz Constant of ReLU Networks NIPS 2020
Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains NIPS 2020