arXiv:2609.38356v1 Announce Type: new
Abstract: Dynamical Systems Reconstruction (DSR) aims to infer models from observed time series that reproduce a system's qualitative long-term behavior. Continu...
By Sima Hashemi, Daniel Durstewitz, Georgia Koppe
arXiv:2410.14606v3 Announce Type: replace
Abstract: Learning from a stream of experience as it arrives, also known as streaming learning, is a core part of natural learning. However, reliable streami...
By Mohamed Elsayed, Elena Sorina Lupu, Gautham Vasan, A. Rupam Mahmood
arXiv:2609.38598v1 Announce Type: new
Abstract: Partially observable environments pose a fundamental challenge in deep reinforcement learning, requiring agents to compress temporal information from o...
By Sathya Kamesh Bhethanabhotla, Efstratios Gavves, Andr\'e Biedenkapp
arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.
By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi
arXiv:2610.00592v1 Announce Type: cross
Abstract: In partially observable reinforcement learning (RL), a later observation can make stored information obsolete or change what it implies for the next...
By Oleg Shchendrigin, Egor Cherepanov, Aleksandr I. Panov, Alexey K. Kovalev
arXiv:2605. 06384v3 Announce Type: replace-cross Abstract: We introduce MinMax Recurrent Neural Cascades (MinMax RNCs), a class of recurrent neural networks built from a novel form of recurrence over the MinMax algebra.
By Alessandro Ronca