Vaishaal Shankar

According to our database1, Vaishaal Shankar authored at least 34 papers between 2015 and 2024.

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Bibliography

2024
Scalable Pre-training of Large Autoregressive Image Models.
CoRR, 2024

2023
Pre-trained Language Models Do Not Help Auto-regressive Text-to-Image Generation.
CoRR, 2023

TiC-CLIP: Continual Training of CLIP Models.
CoRR, 2023

Robust multimodal models have outlier features and encode more concepts.
CoRR, 2023

Data Filtering Networks.
CoRR, 2023

On Robustness in Multimodal Learning.
CoRR, 2023

Self Supervision Does Not Help Natural Language Supervision at Scale.
CoRR, 2023


Robustness in Multimodal Learning under Train-Test Modality Mismatch.
Proceedings of the International Conference on Machine Learning, 2023

Masked Autoencoding Does Not Help Natural Language Supervision at Scale.
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP).
Proceedings of the International Conference on Machine Learning, 2022

2021
Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization.
Proceedings of the 38th International Conference on Machine Learning, 2021

Do Image Classifiers Generalize Across Time?
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

Predicting with Confidence on Unseen Distributions.
Proceedings of the 2021 IEEE/CVF International Conference on Computer Vision, 2021

2020
A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery.
CoRR, 2020

Serverless Straggler Mitigation using Local Error-Correcting Codes.
CoRR, 2020

Measuring Robustness to Natural Distribution Shifts in Image Classification.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Evaluating Machine Accuracy on ImageNet.
Proceedings of the 37th International Conference on Machine Learning, 2020

Neural Kernels Without Tangents.
Proceedings of the 37th International Conference on Machine Learning, 2020

Serverless Straggler Mitigation using Error-Correcting Codes.
Proceedings of the 40th IEEE International Conference on Distributed Computing Systems, 2020

Serverless linear algebra.
Proceedings of the SoCC '20: ACM Symposium on Cloud Computing, 2020

2019
A systematic framework for natural perturbations from videos.
CoRR, 2019

Cloud Programming Simplified: A Berkeley View on Serverless Computing.
CoRR, 2019

A Meta-Analysis of Overfitting in Machine Learning.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Do ImageNet Classifiers Generalize to ImageNet?
Proceedings of the 36th International Conference on Machine Learning, 2019

2018
numpywren: serverless linear algebra.
CoRR, 2018

Do CIFAR-10 Classifiers Generalize to CIFAR-10?
CoRR, 2018

2017
Ground Control to Major Tom: the importance of field surveys in remotely sensed data analysis.
CoRR, 2017

Flare Prediction Using Photospheric and Coronal Image Data.
CoRR, 2017

2016
Approximate Subgraph Isomorphism for Image Localization.
Proceedings of the Image Processing: Algorithms and Systems XIV, 2016

Reviewer Integration and Performance Measurement for Malware Detection.
Proceedings of the Detection of Intrusions and Malware, and Vulnerability Assessment, 2016

2015
Back to the Future: Malware Detection with Temporally Consistent Labels.
CoRR, 2015

A Modern Student Experience inSystems Programming.
Proceedings of the Second ACM Conference on Learning @ Scale, 2015

Better Malware Ground Truth: Techniques for Weighting Anti-Virus Vendor Labels.
Proceedings of the 8th ACM Workshop on Artificial Intelligence and Security, 2015


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