11 citations · 13 across the 5 of their papers we have counts for
5 papers
Fixing the problems of deep neural networks will require better training data and learning algorithms
Drew Linsley, Thomas Serre
Bowers and colleagues argue that DNNs are poor models of biological vision because they often learn to rival human accuracy by relying on strategies that differ markedly from those…
Neural scaling laws for phenotypic drug discovery
Drew Linsley, John Griffin, Jason Parker Brown +5
Recent breakthroughs by deep neural networks (DNNs) in natural language processing (NLP) and computer vision have been driven by a scale-up of models and data rather than the disco…
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…
Adversarial alignment: Breaking the trade-off between the strength of an attack and its relevance to human perception
Drew Linsley, Pinyuan Feng, Thibaut Boissin +4
Deep neural networks (DNNs) are known to have a fundamental sensitivity to adversarial attacks, perturbations of the input that are imperceptible to humans yet powerful enough to c…