Roy Frostig | DeepAI

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∙ 09/22/2022

Learning Model Predictive Controllers with Real-Time Attention for Real-World Navigation

Despite decades of research, existing navigation systems still face real...

16 Xuesu Xiao, et al. ∙

∙ 04/22/2022

You Only Linearize Once: Tangents Transpose to Gradients

Automatic differentiation (AD) is conventionally understood as a family ...

0 Alexey Radul, et al. ∙

∙ 03/31/2022

Learning from many trajectories

We initiate a study of supervised learning from many independent sequenc...

0 Stephen Tu, et al. ∙

∙ 05/31/2021

Efficient and Modular Implicit Differentiation

Automatic differentiation (autodiff) has revolutionized machine learning...

0 Mathieu Blondel, et al. ∙

∙ 05/20/2021

Decomposing reverse-mode automatic differentiation

We decompose reverse-mode automatic differentiation into (forward-mode) ...

0 Roy Frostig, et al. ∙

∙ 05/24/2019

The advantages of multiple classes for reducing overfitting from test set reuse

Excessive reuse of holdout data can lead to overfitting. However, there ...

2 Vitaly Feldman, et al. ∙

∙ 11/08/2018

Measuring the Effects of Data Parallelism on Neural Network Training

Recent hardware developments have made unprecedented amounts of data par...

0 Christopher J. Shallue, et al. ∙

∙ 08/10/2016

Estimation from Indirect Supervision with Linear Moments

In structured prediction problems where we have indirect supervision of ...

0 Aditi Raghunathan, et al. ∙

∙ 02/22/2016

Principal Component Projection Without Principal Component Analysis

We show how to efficiently project a vector onto the top principal compo...

0 Roy Frostig, et al. ∙

∙ 02/18/2016

Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity

We develop a general duality between neural networks and compositional k...

0 Amit Daniely, et al. ∙

∙ 06/24/2015

Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization

We develop a family of accelerated stochastic algorithms that minimize s...

0 Roy Frostig, et al. ∙

∙ 12/20/2014

Competing with the Empirical Risk Minimizer in a Single Pass

In many estimation problems, e.g. linear and logistic regression, we wis...

0 Roy Frostig, et al. ∙

∙ 12/21/2013

Relaxations for inference in restricted Boltzmann machines

We propose a relaxation-based approximate inference algorithm that sampl...

0 Sida I. Wang, et al. ∙

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