51 citations · 64 across the 8 of their papers we have counts for
8 papers
Quantum Fourier Networks for Solving Parametric PDEs
Nishant Jain, Jonas Landman, Natansh Mathur +1
Many real-world problems, like modelling environment dynamics, physical processes, time series etc., involve solving Partial Differential Equations (PDEs) parameterised by problem-…
Selective classification using a robust meta-learning approach
Nishant Jain, Karthikeyan Shanmugam, Pradeep Shenoy
Predictive uncertainty-a model's self awareness regarding its accuracy on an input-is key for both building robust models via training interventions and for test-time applications…
Instance-Conditional Timescales of Decay for Non-Stationary Learning
Nishant Jain, Pradeep Shenoy
Slow concept drift is a ubiquitous, yet under-studied problem in practical machine learning systems. In such settings, although recent data is more indicative of future data, naive…
Robustifying the Multi-Scale Representation of Neural Radiance Fields
Nishant Jain, Suryansh Kumar, Luc Van Gool
Neural Radiance Fields (NeRF) recently emerged as a new paradigm for object representation from multi-view (MV) images. Yet, it cannot handle multi-scale (MS) images and camera pos…
Graph neural network initialisation of quantum approximate optimisation
Nishant Jain, Brian Coyle, Elham Kashefi +1
Approximate combinatorial optimisation has emerged as one of the most promising application areas for quantum computers, particularly those in the near term. In this work, we focus…
MTCNET: Multi-task Learning Paradigm for Crowd Count Estimation
Abhay Kumar, Nishant Jain, Suraj Tripathi +2
We propose a Multi-Task Learning (MTL) paradigm based deep neural network architecture, called MTCNet (Multi-Task Crowd Network) for crowd density and count estimation. Crowd count…
From Fully Supervised to Zero Shot Settings for Twitter Hashtag Recommendation
Abhay Kumar, Nishant Jain, Suraj Tripathi +1
We propose a comprehensive end-to-end pipeline for Twitter hashtags recommendation system including data collection, supervised training setting and zero shot training setting. In…
Exploiting SIFT Descriptor for Rotation Invariant Convolutional Neural Network
Abhay Kumar, Nishant Jain, Chirag Singh +1
This paper presents a novel approach to exploit the distinctive invariant features in convolutional neural network. The proposed CNN model uses Scale Invariant Feature Transform (S…