Search Results for author: Vaishaal Shankar

Found 16 papers, 8 papers with code

Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)

1 code implementation3 May 2022 Alex Fang, Gabriel Ilharco, Mitchell Wortsman, Yuhao Wan, Vaishaal Shankar, Achal Dave, Ludwig Schmidt

Contrastively trained image-text models such as CLIP, ALIGN, and BASIC have demonstrated unprecedented robustness to multiple challenging natural distribution shifts.

Predicting with Confidence on Unseen Distributions

no code implementations ICCV 2021 Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, Ludwig Schmidt

Our work connects techniques from domain adaptation and predictive uncertainty literature, and allows us to predict model accuracy on challenging unseen distributions without access to labeled data.

Domain Adaptation

A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery

no code implementations16 Oct 2020 Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht, Solomon Hsiang

Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use.

Super-Resolution

Neural Kernels Without Tangents

1 code implementation ICML 2020 Vaishaal Shankar, Alex Fang, Wenshuo Guo, Sara Fridovich-Keil, Ludwig Schmidt, Jonathan Ragan-Kelley, Benjamin Recht

We investigate the connections between neural networks and simple building blocks in kernel space.

Serverless Straggler Mitigation using Local Error-Correcting Codes

1 code implementation21 Jan 2020 Vipul Gupta, Dominic Carrano, Yaoqing Yang, Vaishaal Shankar, Thomas Courtade, Kannan Ramchandran

Inexpensive cloud services, such as serverless computing, are often vulnerable to straggling nodes that increase end-to-end latency for distributed computation.

Distributed, Parallel, and Cluster Computing Information Theory Information Theory

A Meta-Analysis of Overfitting in Machine Learning

no code implementations NeurIPS 2019 Rebecca Roelofs, Vaishaal Shankar, Benjamin Recht, Sara Fridovich-Keil, Moritz Hardt, John Miller, Ludwig Schmidt

By systematically comparing the public ranking with the final ranking, we assess how much participants adapted to the holdout set over the course of a competition.

Do Image Classifiers Generalize Across Time?

1 code implementation ICCV 2021 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

Additionally, we evaluate three detection models and show that natural perturbations induce both classification as well as localization errors, leading to a median drop in detection mAP of 14 points.

14 General Classification +1

A Systematic Framework for Natural Perturbations from Videos

no code implementations ICML Workshop Deep_Phenomen 2019 Vaishaal Shankar, Achal Dave, Rebecca Roelofs, Deva Ramanan, Benjamin Recht, Ludwig Schmidt

We introduce a systematic framework for quantifying the robustness of classifiers to naturally occurring perturbations of images found in videos.

14 Video Object Detection

Cloud Programming Simplified: A Berkeley View on Serverless Computing

no code implementations9 Feb 2019 Eric Jonas, Johann Schleier-Smith, Vikram Sreekanti, Chia-Che Tsai, Anurag Khandelwal, Qifan Pu, Vaishaal Shankar, Joao Carreira, Karl Krauth, Neeraja Yadwadkar, Joseph E. Gonzalez, Raluca Ada Popa, Ion Stoica, David A. Patterson

Serverless cloud computing handles virtually all the system administration operations needed to make it easier for programmers to use the cloud.

Operating Systems

Do CIFAR-10 Classifiers Generalize to CIFAR-10?

3 code implementations1 Jun 2018 Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, Vaishaal Shankar

Although we ensure that the new test set is as close to the original data distribution as possible, we find a large drop in accuracy (4% to 10%) for a broad range of deep learning models.

Flare Prediction Using Photospheric and Coronal Image Data

no code implementations3 Aug 2017 Eric Jonas, Monica G. Bobra, Vaishaal Shankar, J. Todd Hoeksema, Benjamin Recht

This is the first attempt to predict solar flares using photospheric vector magnetic field data as well as multiple wavelengths of image data from the chromosphere, transition region, and corona.

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