Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Mallick, Tanwi"'
Autor:
Jiang, Bowen, Xie, Yangxinyu, Hao, Zhuoqun, Wang, Xiaomeng, Mallick, Tanwi, Su, Weijie J., Taylor, Camillo J., Roth, Dan
This study introduces a hypothesis-testing framework to assess whether large language models (LLMs) possess genuine reasoning abilities or primarily depend on token bias. We go beyond evaluating LLMs on accuracy; rather, we aim to investigate their t
Externí odkaz:
http://arxiv.org/abs/2406.11050
Autor:
Jiang, Bowen, Xie, Yangxinyu, Wang, Xiaomeng, Su, Weijie J., Taylor, Camillo J., Mallick, Tanwi
Rationality is the quality of being guided by reason, characterized by logical thinking and decision-making that align with evidence and logical rules. This quality is essential for effective problem-solving, as it ensures that solutions are well-fou
Externí odkaz:
http://arxiv.org/abs/2406.00252
Autor:
Xie, Yangxinyu, Jiang, Bowen, Mallick, Tanwi, Bergerson, Joshua David, Hutchison, John K., Verner, Duane R., Branham, Jordan, Alexander, M. Ross, Ross, Robert B., Feng, Yan, Levy, Leslie-Anne, Su, Weijie, Taylor, Camillo J.
Recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence. However, LLMs are generalized models, trained on extensive text corpus, and often struggle to provide context-spec
Externí odkaz:
http://arxiv.org/abs/2402.07877
Autor:
Qian, Qipeng, Mallick, Tanwi
Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. However, these methods do not explicitly model some of the natur
Externí odkaz:
http://arxiv.org/abs/2401.06040
Autor:
Xie, Yangxinyu, Mallick, Tanwi
Spatiotemporal graph neural networks have achieved state-of-the-art performance in traffic forecasting. However, they often struggle to forecast congestion accurately due to the limitations of traditional loss functions. While accurate forecasting of
Externí odkaz:
http://arxiv.org/abs/2308.15464
Autor:
Mallick, Tanwi, Bergerson, Joshua David, Verner, Duane R., Hutchison, John K, Levy, Leslie-Anne, Balaprakash, Prasanna
Natural language processing (NLP) is a promising approach for analyzing large volumes of climate-change and infrastructure-related scientific literature. However, best-in-practice NLP techniques require large collections of relevant documents (corpus
Externí odkaz:
http://arxiv.org/abs/2302.01887
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic forecasting of up to 1 hour, long-term traffic forecasting that spans mult
Externí odkaz:
http://arxiv.org/abs/2209.13123
Deep-learning-based data-driven forecasting methods have produced impressive results for traffic forecasting. A major limitation of these methods, however, is that they provide forecasts without estimates of uncertainty, which are critical for real-t
Externí odkaz:
http://arxiv.org/abs/2204.01618
Using the data from loop detector sensors for near-real-time detection of traffic incidents in highways is crucial to averting major traffic congestion. While recent supervised machine learning methods offer solutions to incident detection by leverag
Externí odkaz:
http://arxiv.org/abs/2112.09792
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers. With demands growing at exponential rates, these netwo
Externí odkaz:
http://arxiv.org/abs/2008.12767