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08/02/2023
OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models
We introduce OpenFlamingo, a family of autoregressive vision-language mo...
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07/24/2023
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness
Pre-training has been widely adopted in deep learning to improve model p...
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07/19/2023
Improving Multimodal Datasets with Image Captioning
Massive web datasets play a key role in the success of large vision-lang...
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07/11/2023
Objaverse-XL: A Universe of 10M+ 3D Objects
Natural language processing and 2D vision models have attained remarkabl...
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06/26/2023
Are aligned neural networks adversarially aligned?
Large language models are now tuned to align with the goals of their cre...
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06/16/2023
Neural Priming for Sample-Efficient Adaptation
We propose Neural Priming, a technique for adapting large pretrained mod...
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04/27/2023
DataComp: In search of the next generation of multimodal datasets
Large multimodal datasets have been instrumental in recent breakthroughs...
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04/25/2023
Stable and low-precision training for large-scale vision-language models
We introduce new methods for 1) accelerating and 2) stabilizing training...
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03/13/2023
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images
Weird, unusual, and uncanny images pique the curiosity of observers beca...
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02/27/2023
The Role of Pre-training Data in Transfer Learning
The transfer learning paradigm of model pre-training and subsequent fine...
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02/02/2023
Effective Robustness against Natural Distribution Shifts for Models with Different Training Data
“Effective robustness” measures the extra out-of-distribution (OOD) robu...
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01/11/2023
Does progress on ImageNet transfer to real-world datasets?
Does progress on ImageNet transfer to real-world datasets? We investigat...
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12/15/2022
Objaverse: A Universe of Annotated 3D Objects
Massive data corpora like WebText, Wikipedia, Conceptual Captions, WebIm...
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12/14/2022
Reproducible scaling laws for contrastive language-image learning
Scaling up neural networks has led to remarkable performance across a wi...
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12/08/2022
Editing Models with Task Arithmetic
Changing how pre-trained models behave – e.g., improving their performan...
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11/23/2022
Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation
Researchers have proposed many methods for fair and robust machine learn...
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10/22/2022
Exploring The Landscape of Distributional Robustness for Question Answering Models
We conduct a large empirical evaluation to investigate the landscape of ...
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10/19/2022
lo-fi: distributed fine-tuning without communication
When fine-tuning large neural networks, it is common to use multiple nod...
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10/16/2022
LAION-5B: An open large-scale dataset for training next generation image-text models
Groundbreaking language-vision architectures like CLIP and DALL-E proved...
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10/07/2022
Measuring and Narrowing the Compositionality Gap in Language Models
We investigate the ability of language models to perform compositional r...
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08/10/2022
Patching open-vocabulary models by interpolating weights
Open-vocabulary models like CLIP achieve high accuracy across many image...
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08/10/2022
Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP
Web-crawled datasets have enabled remarkable generalization capabilities...
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06/19/2022
Adversarial Scrutiny of Evidentiary Statistical Software
The U.S. criminal legal system increasingly relies on software output to...
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05/03/2022
Data Determines Distributional Robustness in Contrastive Language Image Pre-training (CLIP)
Contrastively trained image-text models such as CLIP, ALIGN, and BASIC h...
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03/20/2022
CLIP on Wheels: Zero-Shot Object Navigation as Object Localization and Exploration
Households across the world contain arbitrary objects: from mate gourds ...
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03/10/2022
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
The conventional recipe for maximizing model accuracy is to (1) train mu...
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09/04/2021
Robust fine-tuning of zero-shot models
Large pre-trained models such as CLIP offer consistent accuracy across a...
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08/10/2021
Retiring Adult: New Datasets for Fair Machine Learning
Although the fairness community has recognized the importance of data, r...
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07/20/2021
Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning
Deep Metric Learning (DML) aims to find representations suitable for zer...
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07/09/2021
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
For machine learning systems to be reliable, we must understand their pe...
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07/07/2021
Predicting with Confidence on Unseen Distributions
Recent work has shown that the performance of machine learning models ca...
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03/25/2021
Contrasting Contrastive Self-Supervised Representation Learning Models
In the past few years, we have witnessed remarkable breakthroughs in sel...
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07/01/2020
Measuring Robustness to Natural Distribution Shifts in Image Classification
We study how robust current ImageNet models are to distribution shifts a...
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04/29/2020
The Effect of Natural Distribution Shift on Question Answering Models
We build four new test sets for the Stanford Question Answering Dataset ...
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03/04/2020
Neural Kernels Without Tangents
We investigate the connections between neural networks and simple buildi...
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06/05/2019
A systematic framework for natural perturbations from videos
We introduce a systematic framework for quantifying the robustness of cl...
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05/31/2019
Unlabeled Data Improves Adversarial Robustness
We demonstrate, theoretically and empirically, that adversarial robustne...
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05/29/2019
Model Similarity Mitigates Test Set Overuse
Excessive reuse of test data has become commonplace in today's machine l...
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07/13/2018
Model Reconstruction from Model Explanations
We show through theory and experiment that gradient-based explanations o...
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06/01/2018
Do CIFAR-10 Classifiers Generalize to CIFAR-10?
Machine learning is currently dominated by largely experimental work foc...
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04/30/2018
Adversarially Robust Generalization Requires More Data
Machine learning models are often susceptible to adversarial perturbatio...
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02/23/2018
Fast and Sample Near-Optimal Algorithms for Learning Multidimensional Histograms
We study the problem of robustly learning multi-dimensional histograms. ...
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12/21/2017
A Fast Algorithm for Separated Sparsity via Perturbed Lagrangians
Sparsity-based methods are widely used in machine learning, statistics, ...
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12/15/2017
Graph-Sparse Logistic Regression
We introduce Graph-Sparse Logistic Regression, a new algorithm for class...
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12/07/2017
A Rotation and a Translation Suffice: Fooling CNNs with Simple Transformations
Recent work has shown that neural network-based vision classifiers exhib...
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11/02/2017
A Classification-Based Perspective on GAN Distributions
A fundamental, and still largely unanswered, question in the context of ...
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06/19/2017
Towards Deep Learning Models Resistant to Adversarial Attacks
Recent work has demonstrated that neural networks are vulnerable to adve...
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04/10/2017
