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pro vyhledávání: '"Lindsay Grace"'
Learning-based approaches to quadruped locomotion commonly adopt generic policy architectures like fully connected MLPs. As such architectures contain few inductive biases, it is common in practice to incorporate priors in the form of rewards, traini
Externí odkaz:
http://arxiv.org/abs/2410.07174
Autor:
Jindgar, Kartik, Lindsay, Grace W.
In recent years, analysis of remote sensing data has benefited immensely from borrowing techniques from the broader field of computer vision, such as the use of shared models pre-trained on large and diverse datasets. However, satellite imagery has u
Externí odkaz:
http://arxiv.org/abs/2409.17363
Autor:
He, Zhonghao, Achterberg, Jascha, Collins, Katie, Nejad, Kevin, Akarca, Danyal, Yang, Yinzhu, Gurnee, Wes, Sucholutsky, Ilia, Tang, Yuhan, Ianov, Rebeca, Ogden, George, Li, Chole, Sandbrink, Kai, Casper, Stephen, Ivanova, Anna, Lindsay, Grace W.
As deep learning systems are scaled up to many billions of parameters, relating their internal structure to external behaviors becomes very challenging. Although daunting, this problem is not new: Neuroscientists and cognitive scientists have accumul
Externí odkaz:
http://arxiv.org/abs/2408.12664
Autor:
Marien, Stacey
Publikováno v:
Reference & User Services Quarterly, 2019 Dec 01. 59(2), 142-142.
Externí odkaz:
https://www.jstor.org/stable/26952306
Autor:
Lindsay, Grace W.
Connecting neural activity to function is a common aim in neuroscience. How to define and conceptualize function, however, can vary. Here I focus on grounding this goal in the specific question of how a given change in behavior is produced by a chang
Externí odkaz:
http://arxiv.org/abs/2311.07526
Autor:
Butlin, Patrick, Long, Robert, Elmoznino, Eric, Bengio, Yoshua, Birch, Jonathan, Constant, Axel, Deane, George, Fleming, Stephen M., Frith, Chris, Ji, Xu, Kanai, Ryota, Klein, Colin, Lindsay, Grace, Michel, Matthias, Mudrik, Liad, Peters, Megan A. K., Schwitzgebel, Eric, Simon, Jonathan, VanRullen, Rufin
Whether current or near-term AI systems could be conscious is a topic of scientific interest and increasing public concern. This report argues for, and exemplifies, a rigorous and empirically grounded approach to AI consciousness: assessing existing
Externí odkaz:
http://arxiv.org/abs/2308.08708
Autor:
Doerig, Adrien, Sommers, Rowan, Seeliger, Katja, Richards, Blake, Ismael, Jenann, Lindsay, Grace, Kording, Konrad, Konkle, Talia, Van Gerven, Marcel A. J., Kriegeskorte, Nikolaus, Kietzmann, Tim C.
Artificial Neural Networks (ANNs) inspired by biology are beginning to be widely used to model behavioral and neural data, an approach we call neuroconnectionism. ANNs have been lauded as the current best models of information processing in the brain
Externí odkaz:
http://arxiv.org/abs/2209.03718
Autor:
Lindsay, Grace W.
Neuroscientists apply a range of common analysis tools to recorded neural activity in order to glean insights into how neural circuits implement computations. Despite the fact that these tools shape the progress of the field as a whole, we have littl
Externí odkaz:
http://arxiv.org/abs/2202.07035
Artificial neural systems trained using reinforcement, supervised, and unsupervised learning all acquire internal representations of high dimensional input. To what extent these representations depend on the different learning objectives is largely u
Externí odkaz:
http://arxiv.org/abs/2112.02027