no code implementations • 10 Jan 2023 • Prakhar Mishra, Chaitali Diwan, Srinath Srinivasa, G. Srinivasaraghavan
It also helps to generate curiosity and interest among the learners and encourages them to pursue a course.
no code implementations • 20 Oct 2021 • Biswesh Mohapatra, Sumit Bhatia, Raghava Mutharaju, G. Srinivasaraghavan
However, most of the existing KG embeddings only consider the network structure of the graph and ignore the semantics and the characteristics of the underlying ontology that provides crucial information about relationships between entities in the KG.
no code implementations • 23 Mar 2021 • Sonal Garg, Sumanth Prabhu, Hemant Misra, G. Srinivasaraghavan
Given that the agents as well as the customers can have varying levels of literacy, the overall quality of responses provided by the agents tend to be poor if they are not predefined.
no code implementations • 2 Nov 2020 • Kumar Shubham, Gopalakrishnan Venkatesh, Reijul Sachdev, Akshi, Dinesh Babu Jayagopi, G. Srinivasaraghavan
In our work, we have formulated a Markov Decision Process (MDP) over the latent space of a pre-trained GAN model to learn a conditional policy for semantic manipulation along specific attributes under defined identity bounds.
no code implementations • ICLR 2019 • Rajesh Devaraddi, G. Srinivasaraghavan
Deep Q-learning networks (DQN) and Deep Deterministic Policy Gradient (DDPG) are two such methods that have shown state-of-the-art results in recent times.
no code implementations • 26 May 2017 • Daksh Varshneya, G. Srinivasaraghavan
All these approaches have been limited by problems like inefficient features in the case of hand crafted features, large error propagation across the predicted trajectory and no information of static artefacts around the dynamic moving objects.
no code implementations • 30 Mar 2016 • Rahul Aralikatte, G. Srinivasaraghavan
With the advent of deep learning, chess playing agents can surpass human ability with relative ease.