Zobrazeno 1 - 10
of 335
pro vyhledávání: '"Tolias, Andreas S"'
Autor:
Turishcheva, Polina, Fahey, Paul G., Vystrčilová, Michaela, Hansel, Laura, Froebe, Rachel, Ponder, Kayla, Qiu, Yongrong, Willeke, Konstantin F., Bashiri, Mohammad, Baikulov, Ruslan, Zhu, Yu, Ma, Lei, Yu, Shan, Huang, Tiejun, Li, Bryan M., De Wulf, Wolf, Kudryashova, Nina, Hennig, Matthias H., Rochefort, Nathalie L., Onken, Arno, Wang, Eric, Ding, Zhiwei, Tolias, Andreas S., Sinz, Fabian H., Ecker, Alexander S
Understanding how biological visual systems process information is challenging because of the nonlinear relationship between visual input and neuronal responses. Artificial neural networks allow computational neuroscientists to create predictive mode
Externí odkaz:
http://arxiv.org/abs/2407.09100
Recent work on object-centric world models aim to factorize representations in terms of objects in a completely unsupervised or self-supervised manner. Such world models are hypothesized to be a key component to address the generalization problem. Wh
Externí odkaz:
http://arxiv.org/abs/2401.00057
Autor:
Burg, Max F., Zenkel, Thomas, Vystrčilová, Michaela, Oesterle, Jonathan, Höfling, Larissa, Willeke, Konstantin F., Lause, Jan, Müller, Sarah, Fahey, Paul G., Ding, Zhiwei, Restivo, Kelli, Sridhar, Shashwat, Gollisch, Tim, Berens, Philipp, Tolias, Andreas S., Euler, Thomas, Bethge, Matthias, Ecker, Alexander S.
Identifying cell types and understanding their functional properties is crucial for unraveling the mechanisms underlying perception and cognition. In the retina, functional types can be identified by carefully selected stimuli, but this requires expe
Externí odkaz:
http://arxiv.org/abs/2401.05342
Autor:
Verwimp, Eli, Aljundi, Rahaf, Ben-David, Shai, Bethge, Matthias, Cossu, Andrea, Gepperth, Alexander, Hayes, Tyler L., Hüllermeier, Eyke, Kanan, Christopher, Kudithipudi, Dhireesha, Lampert, Christoph H., Mundt, Martin, Pascanu, Razvan, Popescu, Adrian, Tolias, Andreas S., van de Weijer, Joost, Liu, Bing, Lomonaco, Vincenzo, Tuytelaars, Tinne, van de Ven, Gido M.
Publikováno v:
Transactions on Machine Learning Research (TMLR), 2024
Continual learning is a subfield of machine learning, which aims to allow machine learning models to continuously learn on new data, by accumulating knowledge without forgetting what was learned in the past. In this work, we take a step back, and ask
Externí odkaz:
http://arxiv.org/abs/2311.11908
Autor:
Turishcheva, Polina, Fahey, Paul G., Hansel, Laura, Froebe, Rachel, Ponder, Kayla, Vystrčilová, Michaela, Willeke, Konstantin F., Bashiri, Mohammad, Wang, Eric, Ding, Zhiwei, Tolias, Andreas S., Sinz, Fabian H., Ecker, Alexander S.
Understanding how biological visual systems process information is challenging due to the complex nonlinear relationship between neuronal responses and high-dimensional visual input. Artificial neural networks have already improved our understanding
Externí odkaz:
http://arxiv.org/abs/2305.19654
Autor:
Zador, Anthony, Escola, Sean, Richards, Blake, Ölveczky, Bence, Bengio, Yoshua, Boahen, Kwabena, Botvinick, Matthew, Chklovskii, Dmitri, Churchland, Anne, Clopath, Claudia, DiCarlo, James, Ganguli, Surya, Hawkins, Jeff, Koerding, Konrad, Koulakov, Alexei, LeCun, Yann, Lillicrap, Timothy, Marblestone, Adam, Olshausen, Bruno, Pouget, Alexandre, Savin, Cristina, Sejnowski, Terrence, Simoncelli, Eero, Solla, Sara, Sussillo, David, Tolias, Andreas S., Tsao, Doris
Neuroscience has long been an essential driver of progress in artificial intelligence (AI). We propose that to accelerate progress in AI, we must invest in fundamental research in NeuroAI. A core component of this is the embodied Turing test, which c
Externí odkaz:
http://arxiv.org/abs/2210.08340
Autor:
Willeke, Konstantin F., Fahey, Paul G., Bashiri, Mohammad, Pede, Laura, Burg, Max F., Blessing, Christoph, Cadena, Santiago A., Ding, Zhiwei, Lurz, Konstantin-Klemens, Ponder, Kayla, Muhammad, Taliah, Patel, Saumil S., Ecker, Alexander S., Tolias, Andreas S., Sinz, Fabian H.
The neural underpinning of the biological visual system is challenging to study experimentally, in particular as the neuronal activity becomes increasingly nonlinear with respect to visual input. Artificial neural networks (ANNs) can serve a variety
Externí odkaz:
http://arxiv.org/abs/2206.08666
Autor:
Karantzas, Nikos, Besier, Emma, Caro, Josue Ortega, Pitkow, Xaq, Tolias, Andreas S., Patel, Ankit B., Anselmi, Fabio
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests
Externí odkaz:
http://arxiv.org/abs/2203.08822
Many complex systems are composed of interacting parts, and the underlying laws are usually simple and universal. While graph neural networks provide a useful relational inductive bias for modeling such systems, generalization to new system instances
Externí odkaz:
http://arxiv.org/abs/2202.10996
Autor:
Fu, Jiakun, Pierzchlewicz, Paweł A., Willeke, Konstantin F., Bashiri, Mohammad, Muhammad, Taliah, Diamantaki, Maria, Froudarakis, Emmanouil, Restivo, Kelli, Ponder, Kayla, Denfield, George H., Sinz, Fabian, Tolias, Andreas S., Franke, Katrin
Publikováno v:
In Cell Reports 27 August 2024 43(8)
