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pro vyhledávání: '"Lomonaco A"'
It is a central problem in the study of critical circle dynamics to understand the regularity of Yoccoz conjugators: the circle homeomorphisms that conjugate critical circle maps of irrational rotation numbers with their corresponding rigid rotations
Externí odkaz:
http://arxiv.org/abs/2411.08643
We prove that any degree $d$ rational map having a parabolic fixed point of multiplier $1$ with a fully invariant and simply connected immediate basin of attraction is mateable with the Hecke group $H_{d+1}$, with the mating realized by an algebraic
Externí odkaz:
http://arxiv.org/abs/2407.14780
Learning continually from a stream of non-i.i.d. data is an open challenge in deep learning, even more so when working in resource-constrained environments such as embedded devices. Visual models that are continually updated through supervised learni
Externí odkaz:
http://arxiv.org/abs/2407.08279
Multi-Task Reinforcement Learning aims at developing agents that are able to continually evolve and adapt to new scenarios. However, this goal is challenging to achieve due to the phenomenon of catastrophic forgetting and the high demand of computati
Externí odkaz:
http://arxiv.org/abs/2406.09835
Autor:
Hemati, Hamed, Pellegrini, Lorenzo, Duan, Xiaotian, Zhao, Zixuan, Xia, Fangfang, Masana, Marc, Tscheschner, Benedikt, Veas, Eduardo, Zheng, Yuxiang, Zhao, Shiji, Li, Shao-Yuan, Huang, Sheng-Jun, Lomonaco, Vincenzo, van de Ven, Gido M.
Publikováno v:
Neural Networks, March 2025: Vol 183, 106920
Continual learning (CL) provides a framework for training models in ever-evolving environments. Although re-occurrence of previously seen objects or tasks is common in real-world problems, the concept of repetition in the data stream is not often con
Externí odkaz:
http://arxiv.org/abs/2405.04101
Continual Learning (CL) focuses on maximizing the predictive performance of a model across a non-stationary stream of data. Unfortunately, CL models tend to forget previous knowledge, thus often underperforming when compared with an offline model tra
Externí odkaz:
http://arxiv.org/abs/2404.07817
Dexterous manipulation, often facilitated by multi-fingered robotic hands, holds solid impact for real-world applications. Soft robotic hands, due to their compliant nature, offer flexibility and adaptability during object grasping and manipulation.
Externí odkaz:
http://arxiv.org/abs/2404.04219
Hyperparameter selection in continual learning scenarios is a challenging and underexplored aspect, especially in practical non-stationary environments. Traditional approaches, such as grid searches with held-out validation data from all tasks, are u
Externí odkaz:
http://arxiv.org/abs/2403.07015
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