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Statistically meaningful approximation: a case study on approximating turing machines with transformers
A common lens to theoretically study neural net architectures is to analyze the functions they
can approximate. However, the constructions from approximation theory often have …
can approximate. However, the constructions from approximation theory often have …
A function space view of bounded norm infinite width relu nets: The multivariate case
A key element of understanding the efficacy of overparameterized neural networks is
characterizing how they represent functions as the number of weights in the network …
characterizing how they represent functions as the number of weights in the network …
On the effective number of linear regions in shallow univariate relu networks: Convergence guarantees and implicit bias
We study the dynamics and implicit bias of gradient flow (GF) on univariate ReLU neural
networks with a single hidden layer in a binary classification setting. We show that when the …
networks with a single hidden layer in a binary classification setting. We show that when the …
A novel framework for policy mirror descent with general parameterization and linear convergence
Modern policy optimization methods in reinforcement learning, such as TRPO and PPO, owe
their success to the use of parameterized policies. However, while theoretical guarantees …
their success to the use of parameterized policies. However, while theoretical guarantees …
Early-stopped neural networks are consistent
This work studies the behavior of shallow ReLU networks trained with the logistic loss via
gradient descent on binary classification data where the underlying data distribution is …
gradient descent on binary classification data where the underlying data distribution is …
Mean-field multiagent reinforcement learning: A decentralized network approach
One of the challenges for multiagent reinforcement learning (MARL) is designing efficient
learning algorithms for a large system in which each agent has only limited or partial …
learning algorithms for a large system in which each agent has only limited or partial …
[HTML][HTML] Provable multi-task representation learning by two-layer relu neural networks
An increasingly popular machine learning paradigm is to pretrain a neural network (NN) on
many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear …
many tasks offline, then adapt it to downstream tasks, often by re-training only the last linear …
Network size and size of the weights in memorization with two-layers neural networks
Abstract In 1988, Eric B. Baum showed that two-layers neural networks with threshold
activation function can perfectly memorize the binary labels of $ n $ points in general …
activation function can perfectly memorize the binary labels of $ n $ points in general …
Feature selection with gradient descent on two-layer networks in low-rotation regimes
M Telgarsky - arxiv preprint arxiv:2208.02789, 2022 - arxiv.org
This work establishes low test error of gradient flow (GF) and stochastic gradient descent
(SGD) on two-layer ReLU networks with standard initialization, in three regimes where key …
(SGD) on two-layer ReLU networks with standard initialization, in three regimes where key …
[HTML][HTML] Variational temporal convolutional networks for I-FENN thermoelasticity
Abstract Machine learning (ML) has been used to solve multiphysics problems like
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …
thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high …