no code implementations • EACL 2021 • Naganand Yadati, Dayanidhi R S, Vaishnavi S, Indira K M, Srinidhi G
Answering questions typically requires reasoning over multiple links in the given KB.
no code implementations • NeurIPS 2020 • Naganand Yadati
Message passing neural network (MPNN) has recently emerged as a successful framework by achieving state-of-the-art performances on many graph-based learning tasks.
1 code implementation • NeurIPS 2019 • Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
no code implementations • NeurIPS Workshop Neuro_AI 2019 • Sruthi Gorantla, Anand Louis, Christos H. Papadimitriou, Santosh Vempala, Naganand Yadati
Artificial neural networks (ANNs) lack in biological plausibility, chiefly because backpropagation requires a variant of plasticity (precise changes of the synaptic weights informed by neural events that occur downstream in the neural circuit) that is profoundly incompatible with the current understanding of the animal brain.
no code implementations • 25 Sep 2019 • Naganand Yadati, Tingran Gao, Shahab Asoodeh, Partha Talukdar, Anand Louis
In this paper, we explore GNNs for graph-based SSL of histograms.
no code implementations • AAAI Conference on Artificial Intelligence 2019 • Sanket Shah, Hyderabad Anand Mishra, Naganand Yadati, Partha Pratim Talukdar
In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e. g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research.
no code implementations • ICLR 2019 • Naganand Yadati, Vikram Nitin, Madhav Nimishakavi, Prateek Yadav, Anand Louis, Partha Talukdar
Additionally, there is need to represent the direction from reactants to products.
1 code implementation • 14 Nov 2018 • Soumya Sanyal, Janakiraman Balachandran, Naganand Yadati, Abhishek Kumar, Padmini Rajagopalan, Suchismita Sanyal, Partha Talukdar
Some of the major challenges involved in developing such models are, (i) limited availability of materials data as compared to other fields, (ii) lack of universal descriptor of materials to predict its various properties.
Ranked #4 on Band Gap on Materials Project
1 code implementation • 7 Sep 2018 • Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, Partha Talukdar
In many real-world network datasets such as co-authorship, co-citation, email communication, etc., relationships are complex and go beyond pairwise.
1 code implementation • 29 May 2018 • Prateek Yadav, Madhav Nimishakavi, Naganand Yadati, Shikhar Vashishth, Arun Rajkumar, Partha Talukdar
We analyse local and global properties of graphs and demonstrate settings where LCNs tend to work better than GCNs.