6 citations · 18 across the 9 of their papers we have counts for
9 papers
Recurrent Video Masked Autoencoders
Daniel Zoran, Nikhil Parthasarathy, Yi Yang +3
We present Recurrent Video Masked-Autoencoders (RVM): a novel approach to video representation learning that leverages recurrent computation to model the temporal structure of vide…
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…
Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds
Michael Kuoch, Chi-Ning Chou, Nikhil Parthasarathy +4
Recently, growth in our understanding of the computations performed in both biological and artificial neural networks has largely been driven by either low-level mechanistic studie…
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…
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,…
Where Should I Spend My FLOPS? Efficiency Evaluations of Visual Pre-training Methods
Skanda Koppula, Yazhe Li, Evan Shelhamer +5
Self-supervised methods have achieved remarkable success in transfer learning, often achieving the same or better accuracy than supervised pre-training. Most prior work has done so…
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…
Self-Supervised Learning of a Biologically-Inspired Visual Texture Model
Nikhil Parthasarathy, Eero P. Simoncelli
We develop a model for representing visual texture in a low-dimensional feature space, along with a novel self-supervised learning objective that is used to train it on an unlabele…
A Linear Systems Theory of Normalizing Flows
Reuben Feinman, Nikhil Parthasarathy
Normalizing Flows are a promising new class of algorithms for unsupervised learning based on maximum likelihood optimization with change of variables. They offer to learn a factori…