326 citations · 1k across the 67 of their papers we have counts for
8 papers · 1 filter
Hypergraph Clustering: A Modularity Maximization Approach
Tarun Kumar, Sankaran Vaidyanathan, Harini Ananthapadmanabhan +2
Clustering on hypergraphs has been garnering increased attention with potential applications in network analysis, VLSI design and computer vision, among others. In this work, we ge…
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…
Discovering hierarchies using Imitation Learning from hierarchy aware policies
Ameet Deshpande, Harshavardhan Kamarthi, Balaraman Ravindran
Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfe…
Pack and Detect: Fast Object Detection in Videos Using Region-of-Interest Packing
Athindran Ramesh Kumar, Balaraman Ravindran, Anand Raghunathan
Object detection in videos is an important task in computer vision for various applications such as object tracking, video summarization and video search. Although great progress h…
Language Expansion In Text-Based Games
Ghulam Ahmed Ansari, Sagar J P, Sarath Chandar +1
Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcem…
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…
DiGrad: Multi-Task Reinforcement Learning with Shared Actions
Parijat Dewangan, S Phaniteja, K Madhava Krishna +2
Most reinforcement learning algorithms are inefficient for learning multiple tasks in complex robotic systems, where different tasks share a set of actions. In such environments a…
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…