LIC-GAN: Language Information Conditioned Graph Generative GAN Model

no code implementations • 2 Jun 2023 • Robert Lo, Arnhav Datar, Abishek Sridhar

Deep generative models for Natural Language data offer a new angle on the problem of graph synthesis: by optimizing differentiable models that directly generate graphs, it is possible to side-step expensive search procedures in the discrete and vast space of possible graphs.

Graph Generation Graph Matching +1

Byzantine Spectral Ranking

no code implementations • 15 Nov 2022 • Arnhav Datar, Arun Rajkumar, John Augustine

We first show that the popular spectral ranking based Rank-Centrality algorithm, though optimal for the BTL model, does not perform well even when a small constant fraction of the voters are Byzantine.

Multi-Modal Detection of Alzheimer's Disease from Speech and Text

no code implementations • 30 Nov 2020 • Amish Mittal, Sourav Sahoo, Arnhav Datar, Juned Kadiwala, Hrithwik Shalu, Jimson Mathew

Reliable detection of the prodromal stages of Alzheimer's disease (AD) remains difficult even today because, unlike other neurocognitive impairments, there is no definitive diagnosis of AD in vivo.

Multimodal Deep Learning speech-recognition +1

Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model

no code implementations • 30 Nov 2020 • Hrithwik Shalu, Harikrishnan P, Hari Sankar CN, Akash Das, Saptarshi Majumder, Arnhav Datar, Subin Mathew MS, Anugyan Das, Juned Kadiwala

Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting.

Misconceptions One-Shot Learning