Devleena Das

Orcid: 0000-0002-7644-6303

According to our database1, Devleena Das authored at least 12 papers between 2018 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Links

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Bibliography

2023
Explainable Activity Recognition for Smart Home Systems.
ACM Trans. Interact. Intell. Syst., 2023

DEFT: Data Efficient Fine-Tuning for Large Language Models via Unsupervised Core-Set Selection.
CoRR, 2023

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems.
Proceedings of the 28th International Conference on Intelligent User Interfaces, 2023

Reprogramming Pretrained Language Models for Antibody Sequence Infilling.
Proceedings of the International Conference on Machine Learning, 2023

2022
Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling.
CoRR, 2022

Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022

2021
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021

Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery.
Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 2021

2020
Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery.
CoRR, 2020

Leveraging rationales to improve human task performance.
Proceedings of the IUI '20: 25th International Conference on Intelligent User Interfaces, 2020

2018
Vision-Based Detection of Simultaneous Kicking for Identifying Movement Characteristics of Infants At-Risk for Neuro-Disorders.
Proceedings of the 17th IEEE International Conference on Machine Learning and Applications, 2018


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