Gal Vardi | DeepAI

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∙ 07/28/2023

Noisy Interpolation Learning with Shallow Univariate ReLU Networks

We study the asymptotic overfitting behavior of interpolation with minim...

0 Nirmit Joshi, et al. ∙

∙ 07/04/2023

Deconstructing Data Reconstruction: Multiclass, Weight Decay and General Losses

Memorization of training data is an active research area, yet our unders...

0 Gon Buzaglo, et al. ∙

∙ 06/22/2023

An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression

We study the cost of overfitting in noisy kernel ridge regression (KRR),...

0 Lijia Zhou, et al. ∙

∙ 05/25/2023

Most Neural Networks Are Almost Learnable

We present a PTAS for learning random constant-depth networks. We show t...

0 Amit Daniely, et al. ∙

∙ 05/05/2023

Reconstructing Training Data from Multiclass Neural Networks

Reconstructing samples from the training set of trained neural networks ...

0 Gon Buzaglo, et al. ∙

∙ 03/02/2023

Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization

Linear classifiers and leaky ReLU networks trained by gradient flow on t...

0 Spencer Frei, et al. ∙

∙ 03/02/2023

The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in ReLU Networks

In this work, we study the implications of the implicit bias of gradient...

0 Spencer Frei, et al. ∙

∙ 03/01/2023

Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Data Manifolds

Despite a great deal of research, it is still not well-understood why tr...

0 Odelia Melamed, et al. ∙

∙ 02/15/2023

Efficiently Learning Neural Networks: What Assumptions May Suffice?

Understanding when neural networks can be learned efficiently is a funda...

0 Amit Daniely, et al. ∙

∙ 08/26/2022

On the Implicit Bias in Deep-Learning Algorithms

Gradient-based deep-learning algorithms exhibit remarkable performance i...

0 Gal Vardi, et al. ∙

∙ 06/15/2022

Reconstructing Training Data from Trained Neural Networks

Understanding to what extent neural networks memorize training data is a...

159 Niv Haim, et al. ∙

∙ 05/18/2022

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 univari...

0 Itay Safran, et al. ∙

∙ 02/13/2022

The Sample Complexity of One-Hidden-Layer Neural Networks

We study norm-based uniform convergence bounds for neural networks, aimi...

0 Gal Vardi, et al. ∙

∙ 02/09/2022

Gradient Methods Provably Converge to Non-Robust Networks

Despite a great deal of research, it is still unclear why neural network...

0 Gal Vardi, et al. ∙

∙ 02/08/2022

Width is Less Important than Depth in ReLU Neural Networks

We solve an open question from Lu et al. (2017), by showing that any tar...

0 Gal Vardi, et al. ∙

∙ 01/30/2022

Implicit Regularization Towards Rank Minimization in ReLU Networks

We study the conjectured relationship between the implicit regularizatio...

0 Nadav Timor, et al. ∙

∙ 10/07/2021

On the Optimal Memorization Power of ReLU Neural Networks

We study the memorization power of feedforward ReLU neural networks. We ...

0 Gal Vardi, et al. ∙

∙ 10/06/2021

On Margin Maximization in Linear and ReLU Networks

The implicit bias of neural networks has been extensively studied in rec...

0 Gal Vardi, et al. ∙

∙ 06/02/2021

Learning a Single Neuron with Bias Using Gradient Descent

We theoretically study the fundamental problem of learning a single neur...

0 Gal Vardi, et al. ∙

∙ 01/30/2021

Size and Depth Separation in Approximating Natural Functions with Neural Networks

When studying the expressive power of neural networks, a main challenge ...

5 Gal Vardi, et al. ∙

∙ 01/20/2021

From Local Pseudorandom Generators to Hardness of Learning

We prove hardness-of-learning results under a well-studied assumption on...

0 Amit Daniely, et al. ∙

∙ 12/09/2020

Implicit Regularization in ReLU Networks with the Square Loss

Understanding the implicit regularization (or implicit bias) of gradient...

0 Gal Vardi, et al. ∙

∙ 06/05/2020

Hardness of Learning Neural Networks with Natural Weights

Neural networks are nowadays highly successful despite strong hardness r...

0 Amit Daniely, et al. ∙

∙ 05/31/2020

Neural Networks with Small Weights and Depth-Separation Barriers

In studying the expressiveness of neural networks, an important question...

0 Gal Vardi, et al. ∙

∙ 06/15/2018

Flow Logic

Flow networks have attracted a lot of research in computer science. Inde...

0 Orna Kupferman, et al. ∙

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