77 citations · 95 across the 9 of their papers we have counts for
9 papers
Learning Agents With Prioritization and Parameter Noise in Continuous State and Action Space
Rajesh Mangannavar, Gopalakrishnan Srinivasaraghavan
Among the many variants of RL, an important class of problems is where the state and action spaces are continuous -- autonomous robots, autonomous vehicles, optimal control are all…
AI based approach to Trailer Generation for Online Educational Courses
Prakhar Mishra, Chaitali Diwan, Srinath Srinivasa +1
In this paper, we propose an AI based approach to Trailer Generation in the form of short videos for online educational courses. Trailers give an overview of the course to the lear…
Why Settle for Just One? Extending EL++ Ontology Embeddings with Many-to-Many Relationships
Biswesh Mohapatra, Sumit Bhatia, Raghava Mutharaju +1
Knowledge Graph (KG) embeddings provide a low-dimensional representation of entities and relations of a Knowledge Graph and are used successfully for various applications such as q…
One-shot domain adaptation for semantic face editing of real world images using StyleALAE
Ravi Kiran Reddy, Kumar Shubham, Gopalakrishnan Venkatesh +4
Semantic face editing of real world facial images is an important application of generative models. Recently, multiple works have explored possible techniques to generate such modi…
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning
Sonal Garg, Sumanth Prabhu, Hemant Misra +1
Customer support via chat requires agents to resolve customer queries with minimum wait time and maximum customer satisfaction. Given that the agents as well as the customers can h…
HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging
Nikunj Gupta, G Srinivasaraghavan, Swarup Kumar Mohalik +2
Cooperative multi-agent reinforcement learning (MARL) has achieved significant results, most notably by leveraging the representation-learning abilities of deep neural networks. Ho…
Learning a Deep Reinforcement Learning Policy Over the Latent Space of a Pre-trained GAN for Semantic Age Manipulation
Kumar Shubham, Gopalakrishnan Venkatesh, Reijul Sachdev +3
Learning a disentangled representation of the latent space has become one of the most fundamental problems studied in computer vision. Recently, many Generative Adversarial Network…
Human Trajectory Prediction using Spatially aware Deep Attention Models
Daksh Varshneya, G. Srinivasaraghavan
Trajectory Prediction of dynamic objects is a widely studied topic in the field of artificial intelligence. Thanks to a large number of applications like predicting abnormal events…
Phoenix: A Self-Optimizing Chess Engine
Rahul Aralikatte, G Srinivasaraghavan
Since the advent of computers, many tasks which required humans to spend a lot of time and energy have been trivialized by the computers' ability to perform repetitive tasks extrem…