Uncertainty-Aware Multiple Instance Learning from Large-Scale Long Time Series Data. - Yuansheng Zhu; Weishi Shi; Deep Shankar Pandey; Yang Liu; Xiaofan Que; Daniel E. Krutz; Qi Yu 001 (IEEE BigData, 2021)

Multidimensional Belief Quantification for Label-Efficient Meta-Learning. - Deep Shankar Pandey; Qi Yu 001 (CVPR, 2022)

Evidential Conditional Neural Processes. - Deep Shankar Pandey; Qi Yu 001 (AAAI, 2023)

Learn to Accumulate Evidence from All Training Samples: Theory and Practice. - Deep Shankar Pandey; Qi Yu 001 (ICML, 2023)

Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. - Dingrong Wang; Deep Shankar Pandey; Krishna Prasad Neupane; Zhiwei Yu; Ervine Zheng; Zhi Zheng 002; Qi Yu 001 (ICML, 2023)

Be Confident in What You Know: Bayesian Parameter Efficient Fine-Tuning of Vision Foundation Models. - Deep Shankar Pandey; Spandan Pyakurel; Qi Yu 001 (NeurIPS, 2024)

A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon. - Bishwas Pokharel; Deep Shankar Pandey; Anjuli Sapkota; Bhimraj Yadav; Vasanta Gurung; Mandira Pradhananga Adhikari; Loknath Regmi; Nanda B. Adhikari (IEEE Access, 2024)

Deep energy-pressure regression for a thermodynamically consistent EOS model. - Dayou Yu; Deep Shankar Pandey; Joshua Hinz; Deyan I. Mihaylov; Valentin V. Karasiev; S. X. Hu; Qi Yu 001 (Mach. Learn. Sci. Technol., 2024)