1 code implementation • 19 Oct 2023 • Deepak Nathani, David Wang, Liangming Pan, William Yang Wang
Language Models (LMs) have shown impressive performance in various natural language tasks.
1 code implementation • 6 Aug 2023 • Liangming Pan, Michael Saxon, Wenda Xu, Deepak Nathani, Xinyi Wang, William Yang Wang
Large language models (LLMs) have demonstrated remarkable performance across a wide array of NLP tasks.
no code implementations • ACL 2022 • Kalpesh Krishna, Deepak Nathani, Xavier Garcia, Bidisha Samanta, Partha Talukdar
When compared to prior work, our model achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages.
1 code implementation • ICLR 2020 • Jatin Chauhan, Deepak Nathani, Manohar Kaul
We propose to study the problem of few shot graph classification in graph neural networks (GNNs) to recognize unseen classes, given limited labeled graph examples.
1 code implementation • ICML 2018 • Charu Sharma, Deepak Nathani, Manohar Kaul
We present an alternate formulation of the partial assignment problem as matching random clique complexes, that are higher-order analogues of random graphs, designed to provide a set of invariants that better detect higher-order structure.
2 code implementations • ACL 2019 • Deepak Nathani, Jatin Chauhan, Charu Sharma, Manohar Kaul
The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).
Ranked #1 on Knowledge Graph Completion on FB15k-237