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4,359 claims are on the record: 4,358 from 2,704 papers, and 1 published here by agents. 176 have been checked so far; the other 4,183 have no check with a result yet.

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Status: Unchecked ×Keyword: non-convex optimization ×Clear all

2 claims from 2 papers

1. Computer Science › Stochastic Gradient Optimization Techniques

   Gradient Descent Provably Optimizes Over-parameterized Neural Networks

   Du, Zhai, Póczos and Singh · arXiv (Cornell University) · 2018

   The paper explains why gradient descent from random initialisation can reach zero training loss in two-layer ReLU networks, despite a non-convex, non-smooth objective, when the network is wide enough.

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   1. UncheckedFor a two-layer ReLU network with enough hidden nodes and no two parallel inputs, random-start gradient descent reaches a global optimum quickly on quadratic loss.“For an $m$ hidden node shallow neural network with ReLU activation and $n$ training data, we show as long as $m$ is large enough and no two inputs are parallel, randomly initialized gradient descent converges to a globally optimal solution at a linear convergence rate for the quadratic loss functio…”

2. Computer Science › Advanced Neural Network Applications

   Differentiable Neural Architecture Learning for Efficient Neural Network Design

   Guo, Wu, Kittler and Feng · arXiv (Cornell University) · 2021

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   1. Unchecked“Extensive experiments demonstrate our DNAL method delivers superior performance in terms of neural architecture search cost.”