906 citations · 944 across the 9 of their papers we have counts for
9 papers
Data curation via joint example selection further accelerates multimodal learning
Talfan Evans, Nikhil Parthasarathy, Hamza Merzic +1
Data curation is an essential component of large-scale pretraining. In this work, we demonstrate that jointly selecting batches of data is more effective for learning than selectin…
Layerwise complexity-matched learning yields an improved model of cortical area V2
Nikhil Parthasarathy, Olivier J. Hénaff, Eero P. Simoncelli
Human ability to recognize complex visual patterns arises through transformations performed by successive areas in the ventral visual cortex. Deep neural networks trained end-to-en…
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding
Talfan Evans, Shreya Pathak, Hamza Merzic +3
Power-law scaling indicates that large-scale training with uniform sampling is prohibitively slow. Active learning methods aim to increase data efficiency by prioritizing learning…
Towards In-context Scene Understanding
Ivana Balažević, David Steiner, Nikhil Parthasarathy +2
In-context learning$\unicode{x2013}$the ability to configure a model's behavior with different prompts$\unicode{x2013}$has revolutionized the field of natural language processing,…
Self-supervised video pretraining yields robust and more human-aligned visual representations
Nikhil Parthasarathy, S. M. Ali Eslami, João Carreira +1
Humans learn powerful representations of objects and scenes by observing how they evolve over time. Yet, outside of specific tasks that require explicit temporal understanding, sta…
Divide and Contrast: Self-supervised Learning from Uncurated Data
Yonglong Tian, Olivier J. Henaff, Aaron van den Oord
Self-supervised learning holds promise in leveraging large amounts of unlabeled data, however much of its progress has thus far been limited to highly curated pre-training data suc…
Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw +4
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-ef…
Geodesics of learned representations
Olivier J. Hénaff, Eero P. Simoncelli
We develop a new method for visualizing and refining the invariances of learned representations. Specifically, we test for a general form of invariance, linearization, in which the…
The local low-dimensionality of natural images
Olivier J. Hénaff, Johannes Ballé, Neil C. Rabinowitz +1
We develop a new statistical model for photographic images, in which the local responses of a bank of linear filters are described as jointly Gaussian, with zero mean and a covaria…