1 code implementation • 11 Oct 2023 • Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das
Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.
no code implementations • 20 Sep 2023 • Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth
This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches.
no code implementations • 8 May 2023 • Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal G. Gajera, Shreeyash Mukul Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.
no code implementations • 30 Mar 2022 • Ruwan Wickramarachchi, Cory Henson, Amit Sheth
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.
no code implementations • 9 Mar 2020 • Alessandro Oltramari, Jonathan Francis, Cory Henson, Kaixin Ma, Ruwan Wickramarachchi
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI).
no code implementations • 29 Feb 2020 • Ruwan Wickramarachchi, Cory Henson, Amit Sheth
With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data.