326 citations · 717 across the 90 of their papers we have counts for
14 papers · 1 filter
Studying the Plasticity in Deep Convolutional Neural Networks using Random Pruning
Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra +1
Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations.The key idea is to rank the filt…
On Controllable Sparse Alternatives to Softmax
Anirban Laha, Saneem A. Chemmengath, Priyanka Agrawal +3
Converting an n-dimensional vector to a probability distribution over n objects is a commonly used component in many machine learning tasks like multiclass classification, multilab…
Towards Exploiting Background Knowledge for Building Conversation Systems
Nikita Moghe, Siddhartha Arora, Suman Banerjee +1
Existing dialog datasets contain a sequence of utterances and responses without any explicit background knowledge associated with them. This has resulted in the development of mode…
Towards a Better Metric for Evaluating Question Generation Systems
Preksha Nema, Mitesh M. Khapra
There has always been criticism for using -gram based similarity metrics, such as BLEU, NIST, etc, for evaluating the performance of NLG systems. However, these metrics continue…
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems
Suman Banerjee, Nikita Moghe, Siddhartha Arora +1
There is an increasing demand for goal-oriented conversation systems which can assist users in various day-to-day activities such as booking tickets, restaurant reservations, shopp…
FigureNet: A Deep Learning model for Question-Answering on Scientific Plots
Revanth Reddy, Rahul Ramesh, Ameet Deshpande +1
Deep Learning has managed to push boundaries in a wide variety of tasks. One area of interest is to tackle problems in reasoning and understanding, with an aim to emulate human int…
I Have Seen Enough: A Teacher Student Network for Video Classification Using Fewer Frames
Shweta Bhardwaj, Mitesh M. Khapra
Over the past few years, various tasks involving videos such as classification, description, summarization and question answering have received a lot of attention. Current models f…
Fusion Graph Convolutional Networks
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Semi-supervised node classification in attributed graphs, i.e., graphs with node features, involves learning to classify unlabeled nodes given a partially labeled graph. Label pred…
HOPF: Higher Order Propagation Framework for Deep Collective Classification
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra +2
Given a graph where every node has certain attributes associated with it and some nodes have labels associated with them, Collective Classification (CC) is the task of assigning la…
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Parag Jain, Anirban Laha, Karthik Sankaranarayanan +3
Structured data summarization involves generation of natural language summaries from structured input data. In this work, we consider summarizing structured data occurring in the f…
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
Preksha Nema, Shreyas Shetty, Parag Jain +3
In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values…
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
Amrita Saha, Rahul Aralikatte, Mitesh M. Khapra +1
We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by exist…
Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks
Deepak Mittal, Shweta Bhardwaj, Mitesh M. Khapra +1
Recently there has been a lot of work on pruning filters from deep convolutional neural networks (CNNs) with the intention of reducing computations. The key idea is to rank the fil…
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
Amrita Saha, Vardaan Pahuja, Mitesh M. Khapra +2
While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While…