Zobrazeno 1 - 10
of 53
pro vyhledávání: '"Pehle, Christian"'
A natural strategy for continual learning is to weigh a Bayesian ensemble of fixed functions. This suggests that if a (single) neural network could be interpreted as an ensemble, one could design effective algorithms that learn without forgetting. To
Externí odkaz:
http://arxiv.org/abs/2408.17394
Autor:
Müller, Eric, Althaus, Moritz, Arnold, Elias, Spilger, Philipp, Pehle, Christian, Schemmel, Johannes
Traditional neuromorphic hardware architectures rely on event-driven computation, where the asynchronous transmission of events, such as spikes, triggers local computations within synapses and neurons. While machine learning frameworks are commonly u
Externí odkaz:
http://arxiv.org/abs/2401.16841
Autor:
Schreiber, Korbinian, Wunderlich, Timo, Spilger, Philipp, Billaudelle, Sebastian, Cramer, Benjamin, Stradmann, Yannik, Pehle, Christian, Müller, Eric, Petrovici, Mihai A., Schemmel, Johannes, Meier, Karlheinz
Bees display the remarkable ability to return home in a straight line after meandering excursions to their environment. Neurobiological imaging studies have revealed that this capability emerges from a path integration mechanism implemented within th
Externí odkaz:
http://arxiv.org/abs/2401.00473
Autor:
Göltz, Julian, Billaudelle, Sebastian, Kriener, Laura, Blessing, Luca, Pehle, Christian, Müller, Eric, Schemmel, Johannes, Petrovici, Mihai A.
Recent efforts have fostered significant progress towards deep learning in spiking networks, both theoretical and in silico. Here, we discuss several different approaches, including a tentative comparison of the results on BrainScaleS-2, and hint tow
Externí odkaz:
http://arxiv.org/abs/2309.10823
Autor:
Yik, Jason, Berghe, Korneel Van den, Blanken, Douwe den, Bouhadjar, Younes, Fabre, Maxime, Hueber, Paul, Kleyko, Denis, Pacik-Nelson, Noah, Sun, Pao-Sheng Vincent, Tang, Guangzhi, Wang, Shenqi, Zhou, Biyan, Ahmed, Soikat Hasan, Joseph, George Vathakkattil, Leto, Benedetto, Micheli, Aurora, Mishra, Anurag Kumar, Lenz, Gregor, Sun, Tao, Ahmed, Zergham, Akl, Mahmoud, Anderson, Brian, Andreou, Andreas G., Bartolozzi, Chiara, Basu, Arindam, Bogdan, Petrut, Bohte, Sander, Buckley, Sonia, Cauwenberghs, Gert, Chicca, Elisabetta, Corradi, Federico, de Croon, Guido, Danielescu, Andreea, Daram, Anurag, Davies, Mike, Demirag, Yigit, Eshraghian, Jason, Fischer, Tobias, Forest, Jeremy, Fra, Vittorio, Furber, Steve, Furlong, P. Michael, Gilpin, William, Gilra, Aditya, Gonzalez, Hector A., Indiveri, Giacomo, Joshi, Siddharth, Karia, Vedant, Khacef, Lyes, Knight, James C., Kriener, Laura, Kubendran, Rajkumar, Kudithipudi, Dhireesha, Liu, Yao-Hong, Liu, Shih-Chii, Ma, Haoyuan, Manohar, Rajit, Margarit-Taulé, Josep Maria, Mayr, Christian, Michmizos, Konstantinos, Muir, Dylan, Neftci, Emre, Nowotny, Thomas, Ottati, Fabrizio, Ozcelikkale, Ayca, Panda, Priyadarshini, Park, Jongkil, Payvand, Melika, Pehle, Christian, Petrovici, Mihai A., Pierro, Alessandro, Posch, Christoph, Renner, Alpha, Sandamirskaya, Yulia, Schaefer, Clemens JS, van Schaik, André, Schemmel, Johannes, Schmidgall, Samuel, Schuman, Catherine, Seo, Jae-sun, Sheik, Sadique, Shrestha, Sumit Bam, Sifalakis, Manolis, Sironi, Amos, Stewart, Matthew, Stewart, Kenneth, Stewart, Terrence C., Stratmann, Philipp, Timcheck, Jonathan, Tömen, Nergis, Urgese, Gianvito, Verhelst, Marian, Vineyard, Craig M., Vogginger, Bernhard, Yousefzadeh, Amirreza, Zohora, Fatima Tuz, Frenkel, Charlotte, Reddi, Vijay Janapa
Neuromorphic computing shows promise for advancing computing efficiency and capabilities of AI applications using brain-inspired principles. However, the neuromorphic research field currently lacks standardized benchmarks, making it difficult to accu
Externí odkaz:
http://arxiv.org/abs/2304.04640
Neuromorphic computing aims to incorporate lessons from studying biological nervous systems in the design of computer architectures. While existing approaches have successfully implemented aspects of those computational principles, such as sparse spi
Externí odkaz:
http://arxiv.org/abs/2302.07141
Autor:
Spilger, Philipp, Arnold, Elias, Blessing, Luca, Mauch, Christian, Pehle, Christian, Müller, Eric, Schemmel, Johannes
Neuromorphic systems require user-friendly software to support the design and optimization of experiments. In this work, we address this need by presenting our development of a machine learning-based modeling framework for the BrainScaleS-2 neuromorp
Externí odkaz:
http://arxiv.org/abs/2212.12210
Autor:
Mavor-Parker, Augustine N., Sargent, Matthew J., Pehle, Christian, Banino, Andrea, Griffin, Lewis D., Barry, Caswell
Reinforcement learning agents must painstakingly learn through trial and error what sets of state-action pairs are value equivalent -- requiring an often prohibitively large amount of environment experience. MDP homomorphisms have been proposed that
Externí odkaz:
http://arxiv.org/abs/2209.06356
Autor:
Müller, Eric, Arnold, Elias, Breitwieser, Oliver, Czierlinski, Milena, Emmel, Arne, Kaiser, Jakob, Mauch, Christian, Schmitt, Sebastian, Spilger, Philipp, Stock, Raphael, Stradmann, Yannik, Weis, Johannes, Baumbach, Andreas, Billaudelle, Sebastian, Cramer, Benjamin, Ebert, Falk, Göltz, Julian, Ilmberger, Joscha, Karasenko, Vitali, Kleider, Mitja, Leibfried, Aron, Pehle, Christian, Schemmel, Johannes
Neuromorphic systems open up opportunities to enlarge the explorative space for computational research. However, it is often challenging to unite efficiency and usability. This work presents the software aspects of this endeavor for the BrainScaleS-2
Externí odkaz:
http://arxiv.org/abs/2203.11102
Autor:
Pehle, Christian, Billaudelle, Sebastian, Cramer, Benjamin, Kaiser, Jakob, Schreiber, Korbinian, Stradmann, Yannik, Weis, Johannes, Leibfried, Aron, Müller, Eric, Schemmel, Johannes
Since the beginning of information processing by electronic components, the nervous system has served as a metaphor for the organization of computational primitives. Brain-inspired computing today encompasses a class of approaches ranging from using
Externí odkaz:
http://arxiv.org/abs/2201.11063