134 citations · 459 across the 38 of their papers we have counts for
12 papers · 1 filter
Deep Attentive Ranking Networks for Learning to Order Sentences
Pawan Kumar, Dhanajit Brahma, Harish Karnick +1
We present an attention-based ranking framework for learning to order sentences given a paragraph. Our framework is built on a bidirectional sentence encoder and a self-attention b…
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 Meta-Learning Framework for Generalized Zero-Shot Learning
Vinay Kumar Verma, Dhanajit Brahma, Piyush Rai
Learning to classify unseen class samples at test time is popularly referred to as zero-shot learning (ZSL). If test samples can be from training (seen) as well as unseen classes,…
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…
Play and Prune: Adaptive Filter Pruning for Deep Model Compression
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
While convolutional neural networks (CNN) have achieved impressive performance on various classification/recognition tasks, they typically consist of a massive number of parameters…
Generative Model for Zero-Shot Sketch-Based Image Retrieval
Vinay Kumar Verma, Aakansha Mishra, Ashish Mishra +1
We present a probabilistic model for Sketch-Based Image Retrieval (SBIR) where, at retrieval time, we are given sketches from novel classes, that were not present at training time.…
HetConv: Heterogeneous Kernel-Based Convolutions for Deep CNNs
Pravendra Singh, Vinay Kumar Verma, Piyush Rai +1
We present a novel deep learning architecture in which the convolution operation leverages heterogeneous kernels. The proposed HetConv (Heterogeneous Kernel-Based Convolution) redu…