16 citations · 67 across the 9 of their papers we have counts for
9 papers
Diagnosing and exploiting the computational demands of videos games for deep reinforcement learning
Lakshmi Narasimhan Govindarajan, Rex G Liu, Drew Linsley +4
Humans learn by interacting with their environments and perceiving the outcomes of their actions. A landmark in artificial intelligence has been the development of deep reinforceme…
Performance-optimized deep neural networks are evolving into worse models of inferotemporal visual cortex
Drew Linsley, Ivan F. Rodriguez, Thomas Fel +4
One of the most impactful findings in computational neuroscience over the past decade is that the object recognition accuracy of deep neural networks (DNNs) correlates with their a…
The Challenge of Appearance-Free Object Tracking with Feedforward Neural Networks
Girik Malik, Drew Linsley, Thomas Serre +1
Nearly all models for object tracking with artificial neural networks depend on appearance features extracted from a "backbone" architecture, designed for object recognition. Indee…
Understanding the computational demands underlying visual reasoning
Mohit Vaishnav, Remi Cadene, Andrea Alamia +3
Visual understanding requires comprehending complex visual relations between objects within a scene. Here, we seek to characterize the computational demands for abstract visual rea…
Tracking Without Re-recognition in Humans and Machines
Drew Linsley, Girik Malik, Junkyung Kim +3
Imagine trying to track one particular fruitfly in a swarm of hundreds. Higher biological visual systems have evolved to track moving objects by relying on both appearance and moti…
Disentangling neural mechanisms for perceptual grouping
Junkyung Kim, Drew Linsley, Kalpit Thakkar +1
Forming perceptual groups and individuating objects in visual scenes is an essential step towards visual intelligence. This ability is thought to arise in the brain from computatio…