no code implementations • 21 Sep 2021 • Saurabh Kamal, Sahil Sharma
Another factor on which investors can make investment decisions is through sentiment analysis of news headlines, the sole purpose of this study.
no code implementations • 12 Sep 2021 • Jatin Sharma, Sahil Sharma
Deep learning has been successfully appertained to solve various complex problems in the area of big data analytics to computer vision.
no code implementations • 27 Aug 2021 • Arjit Sharma, Sahil Sharma
Urban autonomous driving is an open and challenging problem to solve as the decision-making system has to account for several dynamic factors like multi-agent interactions, diverse scene perceptions, complex road geometries, and other rarely occurring real-world events.
no code implementations • 26 Sep 2018 • Suraj Kothawade, Kunjan Mhaske, Sahil Sharma, Furkhan Shaikh
Satellite Remote Sensing Technology is becoming a major milestone in the prediction of weather anomalies, natural disasters as well as finding alternative resources in proximity using multiple multi-spectral sensors emitting electromagnetic waves at distinct wavelengths.
no code implementations • ICLR 2018 • Sahil Sharma, Girish Raguvir J, Srivatsan Ramesh, Balaraman Ravindran
Our second major contribution is that we propose a generalization of lambda-returns called Confidence-based Autodidactic Returns (CAR), wherein the RL agent learns the weighting of the n-step returns in an end-to-end manner.
no code implementations • 20 May 2017 • Sahil Sharma, Aravind Suresh, Rahul Ramesh, Balaraman Ravindran
Deep Reinforcement Learning (DRL) methods have performed well in an increasing numbering of high-dimensional visual decision making domains.
1 code implementation • ICLR 2018 • Sahil Sharma, Ashutosh Jha, Parikshit Hegde, Balaraman Ravindran
In this work, we propose an efficient multi-task learning framework which solves multiple goal-directed tasks in an on-line setup without the need for expert supervision.
no code implementations • 20 Feb 2017 • Sahil Sharma, Aravind Srinivas, Balaraman Ravindran
Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it.
no code implementations • 28 Jun 2016 • Sahil Sharma, Vinod Sharma, Atul Sharma
In order to calculate efficiency, results of the prediction by candidate methods were compared with the actual medical results of the subject. The various metrics used for performance evaluation are predictive accuracy, precision, sensitivity and specificity.
no code implementations • 17 May 2016 • Aravind Srinivas, Sahil Sharma, Balaraman Ravindran
Deep Reinforcement Learning methods have achieved state of the art performance in learning control policies for the games in the Atari 2600 domain.