137 citations · 483 across the 17 of their papers we have counts for
6 papers · 1 filter
L-WISE: Boosting Human Visual Category Learning Through Model-Based Image Selection and Enhancement
Morgan B. Talbot, Gabriel Kreiman, James J. DiCarlo +1
The currently leading artificial neural network models of the visual ventral stream - which are derived from a combination of performance optimization and robustification methods -…
Robustified ANNs Reveal Wormholes Between Human Category Percepts
Guy Gaziv, Michael J. Lee, James J. DiCarlo
The visual object category reports of artificial neural networks (ANNs) are notoriously sensitive to tiny, adversarial image perturbations. Because human category reports (aka huma…
The ThreeDWorld Transport Challenge: A Visually Guided Task-and-Motion Planning Benchmark for Physically Realistic Embodied AI
Chuang Gan, Siyuan Zhou, Jeremy Schwartz +8
We introduce a visually-guided and physics-driven task-and-motion planning benchmark, which we call the ThreeDWorld Transport Challenge. In this challenge, an embodied agent equipp…
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation
Chuang Gan, Jeremy Schwartz, Seth Alter +21
We introduce ThreeDWorld (TDW), a platform for interactive multi-modal physical simulation. TDW enables simulation of high-fidelity sensory data and physical interactions between m…
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
Jonas Kubilius, Martin Schrimpf, Kohitij Kar +11
Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially…
Teacher Guided Architecture Search
Pouya Bashivan, Mark Tensen, James J DiCarlo
Much of the recent improvement in neural networks for computer vision has resulted from discovery of new networks architectures. Most prior work has used the performance of candida…