5 citations · 11 across the 5 of their papers we have counts for
5 papers
Learn to Accumulate Evidence from All Training Samples: Theory and Practice
Deep Pandey, Qi Yu
Evidential deep learning, built upon belief theory and subjective logic, offers a principled and computationally efficient way to turn a deterministic neural network uncertainty-aw…
Evidential Conditional Neural Processes
Deep Shankar Pandey, Qi Yu
The Conditional Neural Process (CNP) family of models offer a promising direction to tackle few-shot problems by achieving better scalability and competitive predictive performance…
Multidimensional Belief Quantification for Label-Efficient Meta-Learning
Deep Pandey, Qi Yu
Optimization-based meta-learning offers a promising direction for few-shot learning that is essential for many real-world computer vision applications. However, learning from few s…
Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data
Yuansheng Zhu, Weishi Shi, Deep Shankar Pandey +4
We propose a novel framework to classify large-scale time series data with long duration. Long time seriesclassification (L-TSC) is a challenging problem because the dataoften cont…
Reinforcement Learning by Comparing Immediate Reward
Punit Pandey, Deepshikha Pandey, Shishir Kumar
This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate rewards using a variation of Q-Learning algorithm. Unlike the conventional Q-Lear…