743 citations · 3k across the 44 of their papers we have counts for
45 papers
D4: Improving LLM Pretraining via Document De-Duplication and Diversification
Kushal Tirumala, Daniel Simig, Armen Aghajanyan +1
Over recent years, an increasing amount of compute and data has been poured into training large language models (LLMs), usually by doing one-pass learning on as many tokens as poss…
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Florian Bordes, Shashank Shekhar, Mark Ibrahim +3
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) prec…
On the special role of class-selective neurons in early training
Omkar Ranadive, Nikhil Thakurdesai, Ari S Morcos +2
It is commonly observed that deep networks trained for classification exhibit class-selective neurons in their early and intermediate layers. Intriguingly, recent studies have show…
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations
Shashank Shekhar, Florian Bordes, Pascal Vincent +1
Joint-embedding based learning (e.g., SimCLR, MoCo, DINO) and reconstruction-based learning (e.g., BEiT, SimMIM, MAE) are the two leading paradigms for self-supervised learning of…
Stable and low-precision training for large-scale vision-language models
Mitchell Wortsman, Tim Dettmers, Luke Zettlemoyer +3
We introduce new methods for 1) accelerating and 2) stabilizing training for large language-vision models. 1) For acceleration, we introduce SwitchBack, a linear layer for int8 qua…
A Cookbook of Self-Supervised Learning
Randall Balestriero, Mark Ibrahim, Vlad Sobal +16
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning. Yet, much like cooking, training SSL methods is a delicate art wi…
SemDeDup: Data-efficient learning at web-scale through semantic deduplication
Amro Abbas, Kushal Tirumala, Dániel Simig +2
Progress in machine learning has been driven in large part by massive increases in data. However, large web-scale datasets such as LAION are largely uncurated beyond searches for e…
Emergence of Maps in the Memories of Blind Navigation Agents
Erik Wijmans, Manolis Savva, Irfan Essa +3
Animal navigation research posits that organisms build and maintain internal spatial representations, or maps, of their environment. We ask if machines -- specifically, artificial…
lo-fi: distributed fine-tuning without communication
Mitchell Wortsman, Suchin Gururangan, Shen Li +4
When fine-tuning large neural networks, it is common to use multiple nodes and to communicate gradients at each optimization step. By contrast, we investigate completely local fine…
The Robustness Limits of SoTA Vision Models to Natural Variation
Mark Ibrahim, Quentin Garrido, Ari Morcos +1
Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classificati…
Robust Self-Supervised Learning with Lie Groups
Mark Ibrahim, Diane Bouchacourt, Ari Morcos
Deep learning has led to remarkable advances in computer vision. Even so, today's best models are brittle when presented with variations that differ even slightly from those seen d…
Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar +2
Widely observed neural scaling laws, in which error falls off as a power of the training set size, model size, or both, have driven substantial performance improvements in deep lea…
Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time
Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre +8
The conventional recipe for maximizing model accuracy is to (1) train multiple models with various hyperparameters and (2) pick the individual model which performs best on a held-o…
Trade-offs of Local SGD at Scale: An Empirical Study
Jose Javier Gonzalez Ortiz, Jonathan Frankle, Mike Rabbat +2
As datasets and models become increasingly large, distributed training has become a necessary component to allow deep neural networks to train in reasonable amounts of time. Howeve…
Transformed CNNs: recasting pre-trained convolutional layers with self-attention
Stéphane d'Ascoli, Levent Sagun, Giulio Biroli +1
Vision Transformers (ViT) have recently emerged as a powerful alternative to convolutional networks (CNNs). Although hybrid models attempt to bridge the gap between these two archi…
Grounding inductive biases in natural images:invariance stems from variations in data
Diane Bouchacourt, Mark Ibrahim, Ari S. Morcos
To perform well on unseen and potentially out-of-distribution samples, it is desirable for machine learning models to have a predictable response with respect to transformations af…
Width Transfer: On the (In)variance of Width Optimization
Ting-Wu Chin, Diana Marculescu, Ari S. Morcos
Optimizing the channel counts for different layers of a CNN has shown great promise in improving the efficiency of CNNs at test-time. However, these methods often introduce large c…
Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations
Chaitanya K. Ryali, David J. Schwab, Ari S. Morcos
Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the u…
ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases
Stéphane d'Ascoli, Hugo Touvron, Matthew Leavitt +3
Convolutional architectures have proven extremely successful for vision tasks. Their hard inductive biases enable sample-efficient learning, but come at the cost of a potentially l…
Reservoir Transformers
Sheng Shen, Alexei Baevski, Ari S. Morcos +3
We demonstrate that transformers obtain impressive performance even when some of the layers are randomly initialized and never updated. Inspired by old and well-established ideas i…
Are all negatives created equal in contrastive instance discrimination?
Tiffany Tianhui Cai, Jonathan Frankle, David J. Schwab +1
Self-supervised learning has recently begun to rival supervised learning on computer vision tasks. Many of the recent approaches have been based on contrastive instance discriminat…
Towards falsifiable interpretability research
Matthew L. Leavitt, Ari Morcos
Methods for understanding the decisions of and mechanisms underlying deep neural networks (DNNs) typically rely on building intuition by emphasizing sensory or semantic features of…
CURI: A Benchmark for Productive Concept Learning Under Uncertainty
Ramakrishna Vedantam, Arthur Szlam, Maximilian Nickel +2
Humans can learn and reason under substantial uncertainty in a space of infinitely many concepts, including structured relational concepts ("a scene with objects that have the same…
Linking average- and worst-case perturbation robustness via class selectivity and dimensionality
Matthew L. Leavitt, Ari Morcos
Representational sparsity is known to affect robustness to input perturbations in deep neural networks (DNNs), but less is known about how the semantic content of representations a…