no code implementations • ACL (NLP4Prog) 2021 • Vikram Nitin, Anthony Saieva, Baishakhi Ray, Gail Kaiser
Decompiling binary executables to high-level code is an important step in reverse engineering scenarios, such as malware analysis and legacy code maintenance.
no code implementations • 1 Jan 2021 • Vikram Nitin
We present our findings in light of other recent results on the evolution of inductive biases learned by neural networks over the course of training.
1 code implementation • ECCV 2020 • Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, Carl Vondrick
Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool the network.
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.
4 code implementations • ICLR 2020 • Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Partha Talukdar
Multi-relational graphs are a more general and prevalent form of graphs where each edge has a label and direction associated with it.
Ranked #22 on Link Prediction on FB15k-237
1 code implementation • 1 Nov 2019 • Shikhar Vashishth, Soumya Sanyal, Vikram Nitin, Nilesh Agrawal, Partha Talukdar
In this paper, we analyze how increasing the number of these interactions affects link prediction performance, and utilize our observations to propose InteractE.
Ranked #11 on Link Prediction on YAGO3-10
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 • 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.