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of 3
pro vyhledávání: '"Palande, Soham"'
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
Autor:
Kawawa-Beaudan, Maxime, Sood, Srijan, Palande, Soham, Mani, Ganapathy, Balch, Tucker, Veloso, Manuela
We present a lightweight approach to sequence classification using Ensemble Methods for Hidden Markov Models (HMMs). HMMs offer significant advantages in scenarios with imbalanced or smaller datasets due to their simplicity, interpretability, and eff
Externí odkaz:
http://arxiv.org/abs/2409.07619
Autor:
Fons, Elizabeth, Kaur, Rachneet, Palande, Soham, Zeng, Zhen, Balch, Tucker, Veloso, Manuela, Vyetrenko, Svitlana
Large Language Models (LLMs) offer the potential for automatic time series analysis and reporting, which is a critical task across many domains, spanning healthcare, finance, climate, energy, and many more. In this paper, we propose a framework for r
Externí odkaz:
http://arxiv.org/abs/2404.16563