Zobrazeno 1 - 10
of 159
pro vyhledávání: '"Fisher, Zachary"'
Autor:
Crawford, Christopher M., Park, Jonathan J., Chow, Sy-Miin, Ernst, Anja F., Pipiras, Vladas, Fisher, Zachary F.
Interest in the study and analysis of dynamic processes in the social, behavioral, and health sciences has burgeoned in recent years due to the increased availability of intensive longitudinal data. However, how best to model and account for the pers
Externí odkaz:
http://arxiv.org/abs/2409.03085
Autor:
Gunel, Beliz, Wendt, James B., Xie, Jing, Zhou, Yichao, Vo, Nguyen, Fisher, Zachary, Tata, Sandeep
Users often struggle with decision-making between two options (A vs B), as it usually requires time-consuming research across multiple web pages. We propose STRUM-LLM that addresses this challenge by generating attributed, structured, and helpful con
Externí odkaz:
http://arxiv.org/abs/2403.19710
Autor:
Aksitov, Renat, Miryoosefi, Sobhan, Li, Zonglin, Li, Daliang, Babayan, Sheila, Kopparapu, Kavya, Fisher, Zachary, Guo, Ruiqi, Prakash, Sushant, Srinivasan, Pranesh, Zaheer, Manzil, Yu, Felix, Kumar, Sanjiv
Answering complex natural language questions often necessitates multi-step reasoning and integrating external information. Several systems have combined knowledge retrieval with a large language model (LLM) to answer such questions. These systems, ho
Externí odkaz:
http://arxiv.org/abs/2312.10003
Autor:
Fisher, Zachary F., Kim, Younghoon, Pipiras, Vladas, Crawford, Christopher, Petrie, Daniel J., Hunter, Michael D., Geier, Charles F.
How best to model structurally heterogeneous processes is a foundational question in the social, health and behavioral sciences. Recently, Fisher et al., (2022) introduced the multi-VAR approach for simultaneously estimating multiple-subject multivar
Externí odkaz:
http://arxiv.org/abs/2311.08658
This work introduces a novel framework for dynamic factor model-based group-level analysis of multiple subjects time series data, called GRoup Integrative DYnamic factor (GRIDY) models. The framework identifies and characterizes inter-subject similar
Externí odkaz:
http://arxiv.org/abs/2307.15330
This work considers estimation and forecasting in a multivariate, possibly high-dimensional count time series model constructed from a transformation of a latent Gaussian dynamic factor series. The estimation of the latent model parameters is based o
Externí odkaz:
http://arxiv.org/abs/2307.10454
Machine learning models such as Transformers or LSTMs struggle with tasks that are compositional in nature such as those involving reasoning/inference. Although many datasets exist to evaluate compositional generalization, when it comes to evaluating
Externí odkaz:
http://arxiv.org/abs/2203.15099
Autor:
Olson, Anneke E., Shenk, Chad E., Fisher, Zachary, Heim, Christine M., Noll, Jennie G., Shalev, Idan, Schreier, Hannah M.C.
Publikováno v:
In Child Protection and Practice August 2024 2
Publikováno v:
In Developmental Cognitive Neuroscience August 2024 68
Publikováno v:
ACL 2022
Several studies have reported the inability of Transformer models to generalize compositionally, a key type of generalization in many NLP tasks such as semantic parsing. In this paper we explore the design space of Transformer models showing that the
Externí odkaz:
http://arxiv.org/abs/2108.04378