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Neural Network Optimization

3648 directly classified papers

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Papers

Explicit loss asymptotics in the gradient descent training of neural networks NIPS 2021
Continuous vs. Discrete Optimization of Deep Neural Networks NIPS 2021
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work NIPS 2021
A single gradient step finds adversarial examples on random two-layers neural networks NIPS 2021
The Implicit Bias of Minima Stability: A View from Function Space NIPS 2021
Convergence and Alignment of Gradient Descent with Random Backpropagation Weights NIPS 2021
A Convergence Analysis of Gradient Descent on Graph Neural Networks NIPS 2021
Deep Networks Provably Classify Data on Curves NIPS 2021
Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity NIPS 2021
On the Role of Optimization in Double Descent: A Least Squares Study NIPS 2021
Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness NIPS 2021
Credit Assignment in Neural Networks through Deep Feedback Control NIPS 2021
What training reveals about neural network complexity NIPS 2021
Understanding How Encoder-Decoder Architectures Attend NIPS 2021
Efficient Neural Network Training via Forward and Backward Propagation Sparsification NIPS 2021
Understanding How Over-Parametrization Leads to Acceleration: A case of learning a single teacher neuron ACML 2021
calibrated adversarial training ACML 2021
Revisiting Weight Initialization of Deep Neural Networks ACML 2021
The Power of Factorial Powers: New Parameter settings for (Stochastic) Optimization ACML 2021
An Optimistic Acceleration of AMSGrad for Nonconvex Optimization ACML 2021
On the distributional properties of adaptive gradients UAI 2021
Generalization error bounds for deep unfolding RNNs UAI 2021
An optimization and generalization analysis for max-pooling networks UAI 2021
Finite-time theory for momentum Q-learning UAI 2021
A Low-Cost Compliant Gripper Using Cooperative Mini-Delta Robots for Dexterous Manipulation RSS 2021