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Leilani H. Gilpin
Person information
- affiliation: University of California Santa Cruz, CA, USA
- affiliation (former): MIT, CSAIL, USA
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2020 – today
- 2024
- [c14]Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani H. Gilpin:
ProSLM: A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering. NeSy (2) 2024: 291-304 - [i15]Rui-Jie Zhu, Ziqing Wang, Leilani Gilpin, Jason K. Eshraghian:
Autonomous Driving with Spiking Neural Networks. CoRR abs/2405.19687 (2024) - [i14]Jonathan Wellington Morris, Vishrut Shah, Alex Besanceney, Daksh Shah, Leilani H. Gilpin:
Slug Mobile: Test-Bench for RL Testing. CoRR abs/2409.10532 (2024) - [i13]Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani Gilpin:
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering. CoRR abs/2409.11589 (2024) - [i12]Li Liu, Diji Yang, Sijia Zhong, Kalyana Suma Sree Tholeti, Lei Ding, Yi Zhang, Leilani H. Gilpin:
Right this way: Can VLMs Guide Us to See More to Answer Questions? CoRR abs/2411.00394 (2024) - 2023
- [j6]Adam Amos-Binks
, Dustin Dannenhauer
The anticipatory paradigm. AI Mag. 44(2): 133-143 (2023) - [j5]Ryan Jenkins
Separating facts and evaluation: motivation, account, and learnings from a novel approach to evaluating the human impacts of machine learning. AI Soc. 38(4): 1415-1428 (2023) - [c13]Leilani H. Gilpin:
Accountability Layers: Explaining Complex System Failures by Parts. AAAI 2023: 15439 - [c12]Biagio La Rosa, Leilani Gilpin, Roberto Capobianco:
Towards a fuller understanding of neurons with Clustered Compositional Explanations. NeurIPS 2023 - [i11]Shreyan Mitra
The XAISuite framework and the implications of explanatory system dissonance. CoRR abs/2304.08499 (2023) - [i10]Adam Amos-Binks, Dustin Dannenhauer, Leilani H. Gilpin:
Anticipatory Thinking Challenges in Open Worlds: Risk Management. CoRR abs/2306.13157 (2023) - [i9]Sharan Subramanian, Leilani H. Gilpin:
Convolutional Neural Network Model for Diabetic Retinopathy Feature Extraction and Classification. CoRR abs/2310.10806 (2023) - [i8]Shiyuan Huang, Siddarth Mamidanna, Shreedhar Jangam, Yilun Zhou, Leilani H. Gilpin:
Can Large Language Models Explain Themselves? A Study of LLM-Generated Self-Explanations. CoRR abs/2310.11207 (2023) - [i7]Biagio La Rosa, Leilani H. Gilpin, Roberto Capobianco:
Towards a fuller understanding of neurons with Clustered Compositional Explanations. CoRR abs/2310.18443 (2023) - [i6]Shreyan Mitra, Leilani Gilpin:
A novel post-hoc explanation comparison metric and applications. CoRR abs/2311.10811 (2023) - 2022
- [j4]Peter R. Wurman
Outracing champion Gran Turismo drivers with deep reinforcement learning. Nat. 602(7896): 223-228 (2022) - [i5]Leilani H. Gilpin, Andrew R. Paley, Mohammed A. Alam, Sarah Spurlock, Kristian J. Hammond:
"Explanation" is Not a Technical Term: The Problem of Ambiguity in XAI. CoRR abs/2207.00007 (2022) - [i4]Cosmin Badea, Leilani Gilpin:
Establishing Meta-Decision-Making for AI: An Ontology of Relevance, Representation and Reasoning. CoRR abs/2210.00608 (2022) - 2021
- [c11]Leilani H. Gilpin:
Anticipatory Thinking: A Testing and Representation Challenge for Self-Driving Cars. CISS 2021: 1-2 - [c10]Leilani H. Gilpin, Vishnu Penubarthi, Lalana Kagal:
Explaining Multimodal Errors in Autonomous Vehicles. DSAA 2021: 1-10 - 2020
- [j3]Michal Araszkiewicz
ICAIL Doctoral Consortium, Montreal 2019. Artif. Intell. Law 28(2): 267-280 (2020) - [i3]Jason R. Wilson, Leilani Gilpin, Irina Rabkina:
A Knowledge Driven Approach to Adaptive Assistance Using Preference Reasoning and Explanation. CoRR abs/2012.02904 (2020)
2010 – 2019
- 2019
- [j2]Ioana Baldini, Clark W. Barrett, Antonio Chella
Reports of the AAAI 2019 Spring Symposium Series. AI Mag. 40(3): 59-66 (2019) - [c9]Leilani Gilpin:
Explaining Possible Futures for Robust Autonomous Decision-Making. COGSAT@AAAI Fall Symposium 2019 - [c8]Leilani H. Gilpin, Lalana Kagal:
An Adaptable Self-Monitoring Framework for Opaque Machines. AAMAS 2019: 1982-1984 - [i2]Leilani H. Gilpin, Cecilia Testart, Nathaniel Fruchter, Julius Adebayo:
Explaining Explanations to Society. CoRR abs/1901.06560 (2019) - 2018
- [c7]Leilani H. Gilpin:
Reasonableness Monitors. AAAI 2018: 8014-8015 - [c6]Leilani H. Gilpin, Danielle M. Olson
Perception of Speaker Personality Traits Using Speech Signals. CHI Extended Abstracts 2018 - [c5]Leilani H. Gilpin, David Bau
Explaining Explanations: An Overview of Interpretability of Machine Learning. DSAA 2018: 80-89 - [c4]Leilani H. Gilpin, Cagri Zaman, Danielle Olson
Reasonable Perception: Connecting Vision and Language Systems for Validating Scene Descriptions. HRI (Companion) 2018: 115-116 - [i1]Leilani H. Gilpin, David Bau, Ben Z. Yuan, Ayesha Bajwa, Michael A. Specter, Lalana Kagal:
Explaining Explanations: An Approach to Evaluating Interpretability of Machine Learning. CoRR abs/1806.00069 (2018) - 2017
- [c3]Leilani Hendrina Gilpin, Ben Ze Yuan:
Getting Up to Speed on Vehicle Intelligence. AAAI Spring Symposia 2017 - 2016
- [j1]Juan Liu, Eric Bier, Aaron Wilson, John Alexis Guerra Gómez, Tomonori Honda
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data. AI Mag. 37(2): 33-46 (2016) - 2015
- [c2]Juan Liu, Eric Bier, Aaron Wilson, Tomonori Honda, Kumar Sricharan, Leilani Gilpin, John Alexis Guerra Gómez, Daniel Davies:
Graph Analysis for Detecting Fraud, Waste, and Abuse in Healthcare Data. AAAI 2015: 3912-3919 - 2014
- [c1]Leilani Gilpin, Laurent Ciarletta, Yannick Presse, Vincent Chevrier, Virginie Galtier:
Co-simulation solutions using AA4MM-FMI applied to smart space heating models. SimuTools 2014: 153-159
Coauthor Index
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