Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Dhavala, Soma"'
Quantification of Uncertainty in predictions is a challenging problem. In the classification settings, although deep learning based models generalize well, class probabilities often lack reliability. Calibration errors are used to quantify uncertaint
Externí odkaz:
http://arxiv.org/abs/2304.12766
Autor:
Banerjee, Pronoma, Gude, Manasi V, Sampat, Rajvi J, Hedaoo, Sharvari M, Dhavala, Soma, Saha, Snehanshu
Machine learning models are often misspecified in the likelihood, which leads to a lack of robustness in the predictions. In this paper, we introduce a framework for correcting likelihood misspecifications in several paradigm agnostic noisy prior mod
Externí odkaz:
http://arxiv.org/abs/2304.03805
Anomalies refer to the departure of systems and devices from their normal behaviour in standard operating conditions. An anomaly in an industrial device can indicate an upcoming failure, often in the temporal direction. In this paper, we make two con
Externí odkaz:
http://arxiv.org/abs/2302.08712
Mining large datasets and obtaining calibrated predictions from tem is of immediate relevance and utility in reliable deep learning. In our work, we develop methods for Deep neural networks based inferences in such datasets like the Gene Expression.
Externí odkaz:
http://arxiv.org/abs/2206.09333
Autor:
Vaidya, Omatharv Bharat, DSouza, Rithvik Terence, Saha, Snehanshu, Dhavala, Soma, Das, Swagatam
We introduce the Hamiltonian Monte Carlo Particle Swarm Optimizer (HMC-PSO), an optimization algorithm that reaps the benefits of both Exponentially Averaged Momentum PSO and HMC sampling. The coupling of the position and velocity of each particle wi
Externí odkaz:
http://arxiv.org/abs/2206.14134
Quantile regression, based on check loss, is a widely used inferential paradigm in Econometrics and Statistics. The conditional quantiles provide a robust alternative to classical conditional means, and also allow uncertainty quantification of the pr
Externí odkaz:
http://arxiv.org/abs/2102.06575
It has long been recognized that academic success is a result of both cognitive and non-cognitive dimensions acting together. Consequently, any intelligent learning platform designed to improve learning outcomes (LOs) must provide actionable inputs t
Externí odkaz:
http://arxiv.org/abs/2010.02629
Autor:
Saha, Snehanshu, Prashanth, Tejas, Aralihalli, Suraj, Basarkod, Sumedh, Sudarshan, T. S. B, Dhavala, Soma S
We propose a theoretical framework for an adaptive learning rate policy for the Mean Absolute Error loss function and Quantile loss function and evaluate its effectiveness for regression tasks. The framework is based on the theory of Lipschitz contin
Externí odkaz:
http://arxiv.org/abs/2006.13307
Autor:
Mohapatra, Rohan, Saha, Snehanshu, Coello, Carlos A. Coello, Bhattacharya, Anwesh, Dhavala, Soma S., Saha, Sriparna
Publikováno v:
IEEE Transactions on Emerging Topics in Computational Intelligence 2021
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Partic
Externí odkaz:
http://arxiv.org/abs/2006.09875
Machine Learning and Artificial Intelligence are considered an integral part of the Fourth Industrial Revolution. Their impact, and far-reaching consequences, while acknowledged, are yet to be comprehended. These technologies are very specialized, an
Externí odkaz:
http://arxiv.org/abs/2001.00818