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pro vyhledávání: '"Lampinen A"'
Recent studies suggest that deep learning models inductive bias towards favoring simpler features may be one of the sources of shortcut learning. Yet, there has been limited focus on understanding the complexity of the myriad features that models lea
Externí odkaz:
http://arxiv.org/abs/2407.06076
Representation learning, and interpreting learned representations, are key areas of focus in machine learning and neuroscience. Both fields generally use representations as a means to understand or improve a system's computations. In this work, howev
Externí odkaz:
http://arxiv.org/abs/2405.05847
Autor:
SIMA Team, Raad, Maria Abi, Ahuja, Arun, Barros, Catarina, Besse, Frederic, Bolt, Andrew, Bolton, Adrian, Brownfield, Bethanie, Buttimore, Gavin, Cant, Max, Chakera, Sarah, Chan, Stephanie C. Y., Clune, Jeff, Collister, Adrian, Copeman, Vikki, Cullum, Alex, Dasgupta, Ishita, de Cesare, Dario, Di Trapani, Julia, Donchev, Yani, Dunleavy, Emma, Engelcke, Martin, Faulkner, Ryan, Garcia, Frankie, Gbadamosi, Charles, Gong, Zhitao, Gonzales, Lucy, Gupta, Kshitij, Gregor, Karol, Hallingstad, Arne Olav, Harley, Tim, Haves, Sam, Hill, Felix, Hirst, Ed, Hudson, Drew A., Hudson, Jony, Hughes-Fitt, Steph, Rezende, Danilo J., Jasarevic, Mimi, Kampis, Laura, Ke, Rosemary, Keck, Thomas, Kim, Junkyung, Knagg, Oscar, Kopparapu, Kavya, Lampinen, Andrew, Legg, Shane, Lerchner, Alexander, Limont, Marjorie, Liu, Yulan, Loks-Thompson, Maria, Marino, Joseph, Cussons, Kathryn Martin, Matthey, Loic, Mcloughlin, Siobhan, Mendolicchio, Piermaria, Merzic, Hamza, Mitenkova, Anna, Moufarek, Alexandre, Oliveira, Valeria, Oliveira, Yanko, Openshaw, Hannah, Pan, Renke, Pappu, Aneesh, Platonov, Alex, Purkiss, Ollie, Reichert, David, Reid, John, Richemond, Pierre Harvey, Roberts, Tyson, Ruscoe, Giles, Elias, Jaume Sanchez, Sandars, Tasha, Sawyer, Daniel P., Scholtes, Tim, Simmons, Guy, Slater, Daniel, Soyer, Hubert, Strathmann, Heiko, Stys, Peter, Tam, Allison C., Teplyashin, Denis, Terzi, Tayfun, Vercelli, Davide, Vujatovic, Bojan, Wainwright, Marcus, Wang, Jane X., Wang, Zhengdong, Wierstra, Daan, Williams, Duncan, Wong, Nathaniel, York, Sarah, Young, Nick
Building embodied AI systems that can follow arbitrary language instructions in any 3D environment is a key challenge for creating general AI. Accomplishing this goal requires learning to ground language in perception and embodied actions, in order t
Externí odkaz:
http://arxiv.org/abs/2404.10179
Autor:
Sondoqah, Mousa, Abdesslem, Fehmi Ben, Popova, Kristina, McGregor, Moira, La Delfa, Joseph, Garrett, Rachael, Lampinen, Airi, Mottola, Luca, Höök, Kristina
We report on a three-day challenge during which five teams each programmed a nanodrone to be piloted through an obstacle course using bodily movement, in a 3D transposition of the '80s video-game Pacman. Using a bricolage approach to analyse intervie
Externí odkaz:
http://arxiv.org/abs/2312.09688
A common method to study deep learning systems is to use simplified model representations--for example, using singular value decomposition to visualize the model's hidden states in a lower dimensional space. This approach assumes that the results of
Externí odkaz:
http://arxiv.org/abs/2312.03656
Autor:
Carvalho, Wilka, Saraiva, Andre, Filos, Angelos, Lampinen, Andrew Kyle, Matthey, Loic, Lewis, Richard L., Lee, Honglak, Singh, Satinder, Rezende, Danilo J., Zoran, Daniel
The Option Keyboard (OK) was recently proposed as a method for transferring behavioral knowledge across tasks. OK transfers knowledge by adaptively combining subsets of known behaviors using Successor Features (SFs) and Generalized Policy Improvement
Externí odkaz:
http://arxiv.org/abs/2310.15940
Large language models (LLMs) show remarkable capabilities across a variety of tasks. Despite the models only seeing text in training, several recent studies suggest that LLM representations implicitly capture aspects of the underlying grounded concep
Externí odkaz:
http://arxiv.org/abs/2310.14540
Autor:
Sucholutsky, Ilia, Muttenthaler, Lukas, Weller, Adrian, Peng, Andi, Bobu, Andreea, Kim, Been, Love, Bradley C., Grant, Erin, Groen, Iris, Achterberg, Jascha, Tenenbaum, Joshua B., Collins, Katherine M., Hermann, Katherine L., Oktar, Kerem, Greff, Klaus, Hebart, Martin N., Jacoby, Nori, Zhang, Qiuyi, Marjieh, Raja, Geirhos, Robert, Chen, Sherol, Kornblith, Simon, Rane, Sunayana, Konkle, Talia, O'Connell, Thomas P., Unterthiner, Thomas, Lampinen, Andrew K., Müller, Klaus-Robert, Toneva, Mariya, Griffiths, Thomas L.
Biological and artificial information processing systems form representations that they can use to categorize, reason, plan, navigate, and make decisions. How can we measure the extent to which the representations formed by these diverse systems agre
Externí odkaz:
http://arxiv.org/abs/2310.13018
Autor:
Kewenig, Viktor, Lampinen, Andrew, Nastase, Samuel A., Edwards, Christopher, DEstalenx, Quitterie Lacome, Rechardt, Akilles, Skipper, Jeremy I, Vigliocco, Gabriella
The potential of multimodal generative artificial intelligence (mAI) to replicate human grounded language understanding, including the pragmatic, context-rich aspects of communication, remains to be clarified. Humans are known to use salient multimod
Externí odkaz:
http://arxiv.org/abs/2308.06035
Autor:
Muttenthaler, Lukas, Linhardt, Lorenz, Dippel, Jonas, Vandermeulen, Robert A., Hermann, Katherine, Lampinen, Andrew K., Kornblith, Simon
Deep neural networks have reached human-level performance on many computer vision tasks. However, the objectives used to train these networks enforce only that similar images are embedded at similar locations in the representation space, and do not d
Externí odkaz:
http://arxiv.org/abs/2306.04507