438 citations · 1.5k across the 48 of their papers we have counts for
9 papers · 2 filters
Layer-Wise Data-Free CNN Compression
Maxwell Horton, Yanzi Jin, Ali Farhadi +1
We present a computationally efficient method for compressing a trained neural network without using real data. We break the problem of data-free network compression into independe…
What Can You Learn from Your Muscles? Learning Visual Representation from Human Interactions
Kiana Ehsani, Daniel Gordon, Thomas Nguyen +2
Learning effective representations of visual data that generalize to a variety of downstream tasks has been a long quest for computer vision. Most representation learning approache…
A Cordial Sync: Going Beyond Marginal Policies for Multi-Agent Embodied Tasks
Unnat Jain, Luca Weihs, Eric Kolve +4
Autonomous agents must learn to collaborate. It is not scalable to develop a new centralized agent every time a task's difficulty outpaces a single agent's abilities. While multi-a…
FLUID: A Unified Evaluation Framework for Flexible Sequential Data
Matthew Wallingford, Aditya Kusupati, Keivan Alizadeh-Vahid +3
Modern ML methods excel when training data is IID, large-scale, and well labeled. Learning in less ideal conditions remains an open challenge. The sub-fields of few-shot, continual…
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
Matt Deitke, Winson Han, Alvaro Herrasti +10
Visual recognition ecosystems (e.g. ImageNet, Pascal, COCO) have undeniably played a prevailing role in the evolution of modern computer vision. We argue that interactive and embod…
VisualCOMET: Reasoning about the Dynamic Context of a Still Image
Jae Sung Park, Chandra Bhagavatula, Roozbeh Mottaghi +2
Even from a single frame of a still image, people can reason about the dynamic story of the image before, after, and beyond the frame. For example, given an image of a man struggli…