no code implementations • 21 Feb 2024 • Yun-Shiuan Chuang, Jerry Zhu, Timothy T. Rogers
Whereas cognitive models of learning often assume direct experience with both the features of an event and with a true label or outcome, much of everyday learning arises from hearing the opinions of others, without direct access to either the experience or the ground truth outcome.
no code implementations • 16 Nov 2023 • Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka, Agam Goyal, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers
Human groups are able to converge on more accurate beliefs through deliberation, even in the presence of polarization and partisan bias -- a phenomenon known as the "wisdom of partisan crowds."
no code implementations • 16 Nov 2023 • Yun-Shiuan Chuang, Yi Wu, Dhruv Gupta, Rheeya Uppaal, Ananya Kumar, Luhang Sun, Makesh Narsimhan Sreedhar, Sijia Yang, Timothy T. Rogers, Junjie Hu
Adapting pre-trained language models (PLMs) for time-series text classification amidst evolving domain shifts (EDS) is critical for maintaining accuracy in applications like stance detection.
no code implementations • 16 Nov 2023 • Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers
Accurately simulating human opinion dynamics is crucial for understanding a variety of societal phenomena, including polarization and the spread of misinformation.
no code implementations • 16 Nov 2023 • Ara Vartanian, Xiaoxi Sun, Yun-Shiuan Chuang, Siddharth Suresh, Xiaojin Zhu, Timothy T. Rogers
This paper considers how interactions with AI algorithms can boost human creative thought.
no code implementations • 12 Apr 2023 • Siddharth Suresh, Kushin Mukherjee, Timothy T. Rogers
This study evaluates the potential of a large language model for aiding in generation of semantic feature norms - a critical tool for evaluating conceptual structure in cognitive science.
no code implementations • 11 Apr 2023 • Kushin Mukherjee, Siddharth Suresh, Timothy T. Rogers
Semantic feature norms, lists of features that concepts do and do not possess, have played a central role in characterizing human conceptual knowledge, but require extensive human labor.
no code implementations • NeurIPS Workshop SVRHM 2020 • Kushin Mukherjee, Timothy T. Rogers
While modern deep convolutional neural networks can be trained to perform at human levels of object recognition and learn visual features in the process, humans use vision for a host of tasks beyond object recognition including — drawing, acting, and making propositional statements.
no code implementations • NeurIPS 2015 • Kwang-Sung Jun, Jerry Zhu, Timothy T. Rogers, Zhuoran Yang, Ming Yuan
In this paper, we propose the first efficient maximum likelihood estimate (MLE) for INVITE by decomposing the censored output into a series of absorbing random walks.
no code implementations • NeurIPS 2009 • Jerry Zhu, Bryan R. Gibson, Timothy T. Rogers
We propose to use Rademacher complexity, originally developed in computational learning theory, as a measure of human learning capacity.