Zobrazeno 1 - 10
of 16 847
pro vyhledávání: '"Sequence modeling"'
Autor:
Kawawa-Beaudan, Maxime, Sood, Srijan, Palande, Soham, Mani, Ganapathy, Balch, Tucker, Veloso, Manuela
Publikováno v:
NeurIPS 2024, Workshop on Behavioral Machine Learning
We investigate the use of sequence analysis for behavior modeling, emphasizing that sequential context often outweighs the value of aggregate features in understanding human behavior. We discuss framing common problems in fields like healthcare, fina
Externí odkaz:
http://arxiv.org/abs/2411.02174
A central challenge in sequence modeling is efficiently handling tasks with extended contexts. While recent state-space models (SSMs) have made significant progress in this area, they often lack input-dependent filtering or require substantial increa
Externí odkaz:
http://arxiv.org/abs/2410.03464
Autor:
Zhang, Yang, You, Juntao, Bai, Yimeng, Zhang, Jizhi, Bao, Keqin, Wang, Wenjie, Chua, Tat-Seng
Recent advancements in recommender systems have focused on leveraging Large Language Models (LLMs) to improve user preference modeling, yielding promising outcomes. However, current LLM-based approaches struggle to fully leverage user behavior sequen
Externí odkaz:
http://arxiv.org/abs/2410.22809
As the demand for processing extended textual data grows, the ability to handle long-range dependencies and maintain computational efficiency is more critical than ever. One of the key issues for long-sequence modeling using attention-based model is
Externí odkaz:
http://arxiv.org/abs/2410.20926
In the endeavor to make autonomous robots take actions, task planning is a major challenge that requires translating high-level task descriptions into long-horizon action sequences. Despite recent advances in language model agents, they remain prone
Externí odkaz:
http://arxiv.org/abs/2410.01440
Autor:
Sridhar, Aditya
Music genre classification is a critical component of music recommendation systems, generation algorithms, and cultural analytics. In this work, we present an innovative model for classifying music genres using attention-based temporal signature mode
Externí odkaz:
http://arxiv.org/abs/2411.14474
Autor:
Celestini, Davide, Gammelli, Daniele, Guffanti, Tommaso, D'Amico, Simone, Capello, Elisa, Pavone, Marco
Publikováno v:
IEEE Robotics and Automation Letters, vol. 9, n. 11, pp. 9820-9827, Nov. 2024
Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive sol
Externí odkaz:
http://arxiv.org/abs/2410.23916
Link adaptation (LA) is an essential function in modern wireless communication systems that dynamically adjusts the transmission rate of a communication link to match time- and frequency-varying radio link conditions. However, factors such as user mo
Externí odkaz:
http://arxiv.org/abs/2410.23031
A longstanding goal of artificial general intelligence is highly capable generalists that can learn from diverse experiences and generalize to unseen tasks. The language and vision communities have seen remarkable progress toward this trend by scalin
Externí odkaz:
http://arxiv.org/abs/2410.11448
Learning policies from offline datasets through offline reinforcement learning (RL) holds promise for scaling data-driven decision-making and avoiding unsafe and costly online interactions. However, real-world data collected from sensors or humans of
Externí odkaz:
http://arxiv.org/abs/2407.04285