I am a second year PhD student at New York University's Center for Data Science, advised by Prof. Yann LeCun and Prof. Kyunghyun Cho. My research focuses on using information from multiple sources such as text, images, video and speech to improve commonsense reasoning capabilities of machines. Prior to this, I was advised during my Masters by Prof. Andrew McCallum at University of Massachusetts Amherst in areas of natural language processing and machine learning, with a special focus on structured prediction.
Here's a list of my publications-MDETR - Modulated Detection for End-to-End Multi-Modal Understanding
Aishwarya Kamath, Mannat Singh, Yann LeCun, Gabriel Synnaeve, Ishan Misra, Nicolas Carion. Arxiv preprint, April 2021.
AdapterFusion: Non-Destructive Task Composition for Transfer Learning
Jonas Pfeiffer , Aishwarya Kamath , Andreas Ruckle , Kyunghyun Cho , Iryna Gurevych, EACL 2021.
A Survey on Semantic Parsing
Aishwarya Kamath and Rajarshi Das, AKBC 2019.
What do Deep Networks Like to Read?
Jonas Pfeiffer* , Aishwarya Kamath* , Sebastian Ruder. Arxiv preprint, September 2019.
Specializing Distributional Vectors of All Words for Lexical Entailment
Aishwarya Kamath* , Jonas Pfeiffer* , Edoardo M. Ponti, Goran Glavaš, Ivan Vulic. Best paper award at Representation Learning for NLP Workshop at ACL 2019.
Training Structured Prediction Energy Networks with Indirect Supervision
Amirmohammad Rooshenas, Aishwarya Kamath, and Andrew McCallum, NAACL-HLT 2018.