134 citations · 459 across the 38 of their papers we have counts for
29 papers · 1 filter
Shortcut Invariance: Targeted Jacobian Regularization in Disentangled Latent Space
Shivam Pal, Sakshi Varshney, Piyush Rai
Deep neural networks are prone to learning shortcuts, spurious correlations present in the training data that undermine out-of-distribution (OOD) generalization. Most prior work mi…
Spatio-Temporal Forecasting of PM2.5 via Spatial-Diffusion guided Encoder-Decoder Architecture
Malay Pandey, Vaishali Jain, Nimit Godhani +2
In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial di…
Federated Learning with Uncertainty and Personalization via Efficient Second-order Optimization
Shivam Pal, Aishwarya Gupta, Saqib Sarwar +1
Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement…
Robust Black-box Testing of Deep Neural Networks using Co-Domain Coverage
Aishwarya Gupta, Indranil Saha, Piyush Rai
Rigorous testing of machine learning models is necessary for trustworthy deployments. We present a novel black-box approach for generating test-suites for robust testing of deep ne…
VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference
Soumya Banerjee, Vinay K. Verma, Avideep Mukherjee +3
Lifelong learning or continual learning is the problem of training an AI agent continuously while also preventing it from forgetting its previously acquired knowledge. Streaming li…
A Probabilistic Framework for Lifelong Test-Time Adaptation
Dhanajit Brahma, Piyush Rai
Test-time adaptation (TTA) is the problem of updating a pre-trained source model at inference time given test input(s) from a different target domain. Most existing TTA approaches…
Federated Learning with Uncertainty via Distilled Predictive Distributions
Shrey Bhatt, Aishwarya Gupta, Piyush Rai
Most existing federated learning methods are unable to estimate model/predictive uncertainty since the client models are trained using the standard loss function minimization appro…
DiffuseVAE: Efficient, Controllable and High-Fidelity Generation from Low-Dimensional Latents
Kushagra Pandey, Avideep Mukherjee, Piyush Rai +1
Diffusion probabilistic models have been shown to generate state-of-the-art results on several competitive image synthesis benchmarks but lack a low-dimensional, interpretable late…
Hypernetworks for Continual Semi-Supervised Learning
Dhanajit Brahma, Vinay Kumar Verma, Piyush Rai
Learning from data sequentially arriving, possibly in a non i.i.d. way, with changing task distribution over time is called continual learning. Much of the work thus far in continu…
Knowledge Consolidation based Class Incremental Online Learning with Limited Data
Mohammed Asad Karim, Vinay Kumar Verma, Pravendra Singh +2
We propose a novel approach for class incremental online learning in a limited data setting. This problem setting is challenging because of the following constraints: (1) Classes a…
Variational Rejection Particle Filtering
Rahul Sharma, Soumya Banerjee, Dootika Vats +1
We present a variational inference (VI) framework that unifies and leverages sequential Monte-Carlo (particle filtering) with \emph{approximate} rejection sampling to construct a f…
Efficient Feature Transformations for Discriminative and Generative Continual Learning
Vinay Kumar Verma, Kevin J Liang, Nikhil Mehta +2
As neural networks are increasingly being applied to real-world applications, mechanisms to address distributional shift and sequential task learning without forgetting are critica…
CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks
Sakshi Varshney, Vinay Kumar Verma, Srijith P K +2
We present a continual learning approach for generative adversarial networks (GANs), by designing and leveraging parameter-efficient feature map transformations. Our approach is ba…
Generalized Adversarially Learned Inference
Yatin Dandi, Homanga Bharadhwaj, Abhishek Kumar +1
Allowing effective inference of latent vectors while training GANs can greatly increase their applicability in various downstream tasks. Recent approaches, such as ALI and BiGAN fr…
Quantile Regularization: Towards Implicit Calibration of Regression Models
Saiteja Utpala, Piyush Rai
Recent works have shown that most deep learning models are often poorly calibrated, i.e., they may produce overconfident predictions that are wrong. It is therefore desirable to ha…
Jointly Trained Image and Video Generation using Residual Vectors
Yatin Dandi, Aniket Das, Soumye Singhal +2
In this work, we propose a modeling technique for jointly training image and video generation models by simultaneously learning to map latent variables with a fixed prior onto real…
On the relationship between multitask neural networks and multitask Gaussian Processes
Karthikeyan K, Shubham Kumar Bharti, Piyush Rai
Despite the effectiveness of multitask deep neural network (MTDNN), there is a limited theoretical understanding on how the information is shared across different tasks in MTDNN. I…
Bayesian Structure Adaptation for Continual Learning
Abhishek Kumar, Sunabha Chatterjee, Piyush Rai
Continual Learning is a learning paradigm where learning systems are trained with sequential or streaming tasks. Two notable directions among the recent advances in continual learn…
Refined -Divergence Variational Inference via Rejection Sampling
Rahul Sharma, Abhishek Kumar, Piyush Rai
We present an approximate inference method, based on a synergistic combination of Rényi -divergence variational inference (RDVI) and rejection sampling (RS). RDVI is based on mi…
A Generative Framework for Zero-Shot Learning with Adversarial Domain Adaptation
Varun Khare, Divyat Mahajan, Homanga Bharadhwaj +2
We present a domain adaptation based generative framework for zero-shot learning. Our framework addresses the problem of domain shift between the seen and unseen class distribution…
Variational Autoencoders for Sparse and Overdispersed Discrete Data
He Zhao, Piyush Rai, Lan Du +2
Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued…
Stochastic Blockmodels meet Graph Neural Networks
Nikhil Mehta, Lawrence Carin, Piyush Rai
Stochastic blockmodels (SBM) and their variants, , mixed-membership and overlapping stochastic blockmodels, are latent variable based generative models for graphs. They have…
Generalized Zero-Shot Learning via Synthesized Examples
Vinay Kumar Verma, Gundeep Arora, Ashish Mishra +1
We present a generative framework for generalized zero-shot learning where the training and test classes are not necessarily disjoint. Built upon a variational autoencoder based ar…
Zero-Shot Learning via Class-Conditioned Deep Generative Models
Wenlin Wang, Yunchen Pu, Vinay Kumar Verma +5
We present a deep generative model for learning to predict classes not seen at training time. Unlike most existing methods for this problem, that represent each class as a point (v…