1.4k citations · 9.3k across the 81 of their papers we have counts for
81 papers
A lightweight dual-stage framework for personalized speech enhancement based on DeepFilterNet2
Thomas Serre, Mathieu Fontaine, Éric Benhaim +2
Isolating the desired speaker's voice amidst multiplespeakers in a noisy acoustic context is a challenging task. Per-sonalized speech enhancement (PSE) endeavours to achievethis by…
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…
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…
NeuroSurgeon: A Toolkit for Subnetwork Analysis
Michael A. Lepori, Ellie Pavlick, Thomas Serre
Despite recent advances in the field of explainability, much remains unknown about the algorithms that neural networks learn to represent. Recent work has attempted to understand t…
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…