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pro vyhledávání: '"Linsley, A"'
Autor:
Muzellec, Sabine, Linsley, Drew, Ashok, Alekh K., Mingolla, Ennio, Malik, Girik, VanRullen, Rufin, Serre, Thomas
Objects we encounter often change appearance as we interact with them. Changes in illumination (shadows), object pose, or movement of nonrigid objects can drastically alter available image features. How do biological visual systems track objects as t
Externí odkaz:
http://arxiv.org/abs/2410.02094
Autor:
Linsley, Drew, Zhou, Peisen, Ashok, Alekh Karkada, Nagaraj, Akash, Gaonkar, Gaurav, Lewis, Francis E, Pizlo, Zygmunt, Serre, Thomas
Visual perspective taking (VPT) is the ability to perceive and reason about the perspectives of others. It is an essential feature of human intelligence, which develops over the first decade of life and requires an ability to process the 3D structure
Externí odkaz:
http://arxiv.org/abs/2406.04138
Autor:
Linsley, Drew, Griffin, John, Brown, Jason Parker, Roose, Adam N, Frank, Michael, Linsley, Peter, Finkbeiner, Steven, Linsley, Jeremy
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 discovery of novel computing paradigms. Here, we investigate if scale can h
Externí odkaz:
http://arxiv.org/abs/2309.16773
Autor:
Linsley, Drew, Serre, Thomas
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 of humans. We show that this problem is worsening as DNNs are becomin
Externí odkaz:
http://arxiv.org/abs/2311.12819
Autor:
Govindarajan, Lakshmi Narasimhan, Liu, Rex G, Linsley, Drew, Ashok, Alekh Karkada, Reuter, Max, Frank, Michael J, Serre, Thomas
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 reinforcement learning (dRL) algorithms capable of doing the same in video games,
Externí odkaz:
http://arxiv.org/abs/2309.13181
Autor:
Fel, Thomas, Boissin, Thibaut, Boutin, Victor, Picard, Agustin, Novello, Paul, Colin, Julien, Linsley, Drew, Rousseau, Tom, Cadène, Rémi, Gardes, Laurent, Serre, Thomas
Publikováno v:
Conference on Neural Information Processing Systems (NeurIPS), 2023
Feature visualization has gained substantial popularity, particularly after the influential work by Olah et al. in 2017, which established it as a crucial tool for explainability. However, its widespread adoption has been limited due to a reliance on
Externí odkaz:
http://arxiv.org/abs/2306.06805
Autor:
Linsley, Drew, Rodriguez, Ivan F., Fel, Thomas, Arcaro, Michael, Sharma, Saloni, Livingstone, Margaret, Serre, Thomas
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 ability to predict neural responses to natural images in the inferotemp
Externí odkaz:
http://arxiv.org/abs/2306.03779
Autor:
Linsley, Drew, Feng, Pinyuan, Boissin, Thibaut, Ashok, Alekh Karkada, Fel, Thomas, Olaiya, Stephanie, Serre, Thomas
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 change the visual decision of a model. Adversarial attacks have long be
Externí odkaz:
http://arxiv.org/abs/2306.03229
Autor:
Jackson Griffith-Linsley, William Robert Bell, Aaron Cohen-Gadol, Diane Donegan, Angela Richardson, Michael Robertson, Kevin Shiue, Kathryn Nevel
Publikováno v:
CNS Oncology, Vol 13, Iss 1 (2024)
Aim: Atypical teratoid rhabdoid tumor (ATRT) is a rare and highly aggressive primary CNS neoplasm, predominantly observed in children. The use of autologous stem cell transplantation (ASCT) in pediatric ATRT has shown promise; however, its utility in
Externí odkaz:
https://doaj.org/article/a02ff59be0144b56b83a1c61d7e80578
The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we explore if these trends have also carried concomitant improvements in e
Externí odkaz:
http://arxiv.org/abs/2211.04533