Zobrazeno 1 - 10
of 134
pro vyhledávání: '"Kudithipudi, Dhireesha"'
Pushing the frontiers of time-series information processing in ever-growing edge devices with stringent resources has been impeded by the system's ability to process information and learn locally on the device. Local processing and learning typically
Externí odkaz:
http://arxiv.org/abs/2405.13347
Autor:
Hickok, Truman, Kudithipudi, Dhireesha
In continual learning, a model learns incrementally over time while minimizing interference between old and new tasks. One of the most widely used approaches in continual learning is referred to as replay. Replay methods support interleaved learning
Externí odkaz:
http://arxiv.org/abs/2404.10758
Edge devices operating in dynamic environments critically need the ability to continually learn without catastrophic forgetting. The strict resource constraints in these devices pose a major challenge to achieve this, as continual learning entails me
Externí odkaz:
http://arxiv.org/abs/2403.08718
This book chapter delves into the dynamics of continual learning, which is the process of incrementally learning from a non-stationary stream of data. Although continual learning is a natural skill for the human brain, it is very challenging for arti
Externí odkaz:
http://arxiv.org/abs/2403.05175
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:
Kudithipudi, Dhireesha, Daram, Anurag, Zyarah, Abdullah M., Zohora, Fatima Tuz, Aimone, James B., Yanguas-Gil, Angel, Soures, Nicholas, Neftci, Emre, Mattina, Matthew, Lomonaco, Vincenzo, Thiem, Clare D., Epstein, Benjamin
Lifelong learning - an agent's ability to learn throughout its lifetime - is a hallmark of biological learning systems and a central challenge for artificial intelligence (AI). The development of lifelong learning algorithms could lead to a range of
Externí odkaz:
http://arxiv.org/abs/2310.04467
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
Autor:
Baker, Megan M., New, Alexander, Aguilar-Simon, Mario, Al-Halah, Ziad, Arnold, Sébastien M. R., Ben-Iwhiwhu, Ese, Brna, Andrew P., Brooks, Ethan, Brown, Ryan C., Daniels, Zachary, Daram, Anurag, Delattre, Fabien, Dellana, Ryan, Eaton, Eric, Fu, Haotian, Grauman, Kristen, Hostetler, Jesse, Iqbal, Shariq, Kent, Cassandra, Ketz, Nicholas, Kolouri, Soheil, Konidaris, George, Kudithipudi, Dhireesha, Learned-Miller, Erik, Lee, Seungwon, Littman, Michael L., Madireddy, Sandeep, Mendez, Jorge A., Nguyen, Eric Q., Piatko, Christine D., Pilly, Praveen K., Raghavan, Aswin, Rahman, Abrar, Ramakrishnan, Santhosh Kumar, Ratzlaff, Neale, Soltoggio, Andrea, Stone, Peter, Sur, Indranil, Tang, Zhipeng, Tiwari, Saket, Vedder, Kyle, Wang, Felix, Xu, Zifan, Yanguas-Gil, Angel, Yedidsion, Harel, Yu, Shangqun, Vallabha, Gautam K.
Despite the advancement of machine learning techniques in recent years, state-of-the-art systems lack robustness to "real world" events, where the input distributions and tasks encountered by the deployed systems will not be limited to the original t
Externí odkaz:
http://arxiv.org/abs/2301.07799
In this research, we propose a new low-precision framework, TENT, to leverage the benefits of a tapered fixed-point numerical format in TinyML models. We introduce a tapered fixed-point quantization algorithm that matches the numerical format's dynam
Externí odkaz:
http://arxiv.org/abs/2104.02233
Neuromorphic systems that learn and predict from streaming inputs hold significant promise in pervasive edge computing and its applications. In this paper, a neuromorphic system that processes spatio-temporal information on the edge is proposed. Algo
Externí odkaz:
http://arxiv.org/abs/2006.11958