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Taeyeong Choi
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2020 – today
- 2024
- [c10]Grisha Bandodkar, Shyam Agarwal, Athul Krishna Sughosh, Sahilbir Singh, Taeyeong Choi:
"Allot?" is "A Lot!" Towards Developing More Generalized Speech Recognition System for Accessible Communication. AAAI 2024: 23327-23334 - [c9]Satyam Goyal, Kavya Sasikumar, Rohan Sheth, Akash Seelam, Taeyeong Choi, Xin Liu:
EnColor: Improving Visual Accessibility with a Deep Encoder-Decoder Image Corrector for Color Vision Deficient Individuals. AAAI 2024: 23335-23342 - 2023
- [i5]Taeyeong Choi, Dario Guevara, Grisha Bandodkar, Zifei Cheng, Chonghan Wang, Brian N. Bailey, Mason Earles, Xin Liu:
DAVIS-Ag: A Synthetic Plant Dataset for Developing Domain-Inspired Active Vision in Agricultural Robots. CoRR abs/2303.05764 (2023) - 2022
- [j1]Taeyeong Choi
, Benjamin Pyenson, Juergen Liebig, Theodore P. Pavlic:
Beyond tracking: using deep learning to discover novel interactions in biological swarms. Artif. Life Robotics 27(2): 393-400 (2022) - [c8]Taeyeong Choi
, Owen Would, Adrian Salazar Gomez, Grzegorz Cielniak:
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. ICRA 2022: 2266-2272 - 2021
- [c7]Taeyeong Choi, Benjamin Pyenson, Juergen Liebig, Theodore P. Pavlic:
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change. AAAI 2021: 15286-15292 - [c6]Taeyeong Choi
, Grzegorz Cielniak:
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning. ECMR 2021: 1-6 - [i4]Taeyeong Choi, Grzegorz Cielniak:
Adaptive Selection of Informative Path Planning Strategies via Reinforcement Learning. CoRR abs/2108.06618 (2021) - [i3]Taeyeong Choi, Benjamin Pyenson, Juergen Liebig, Theodore P. Pavlic:
Beyond Tracking: Using Deep Learning to Discover Novel Interactions in Biological Swarms. CoRR abs/2108.09394 (2021) - [i2]Taeyeong Choi, Owen Would, Adrian Salazar Gomez, Grzegorz Cielniak:
Self-supervised Representation Learning for Reliable Robotic Monitoring of Fruit Anomalies. CoRR abs/2109.10135 (2021) - 2020
- [b1]Taeyeong Choi:
Deep Learning Approaches for Inferring Collective Macrostates from Individual Observations in Natural and Artificial Multi-Agent Systems Under Realistic Constraints. Arizona State University, Tempe, USA, 2020 - [c5]Sehyeok Kang
, Taeyeong Choi
, Theodore P. Pavlic
:
How far should I watch? Quantifying the effect of various observational capabilities on long-range situational awareness in multi-robot teams. ACSOS 2020: 146-152 - [c4]Taeyeong Choi
, Sehyeok Kang
, Theodore P. Pavlic
:
Learning local behavioral sequences to better infer non-local properties in real multi-robot systems. ICRA 2020: 2138-2144 - [c3]Taeyeong Choi
, Theodore P. Pavlic
:
Automatic Discovery of Motion Patterns that Improve Learning Rate in Communication-Limited Multi-Robot Systems. MFI 2020: 243-248 - [i1]Taeyeong Choi, Benjamin Pyenson, Juergen Liebig, Theodore P. Pavlic:
Identification of Abnormal States in Videos of Ants Undergoing Social Phase Change. CoRR abs/2009.08626 (2020)
2010 – 2019
- 2017
- [c2]Taeyeong Choi
, Theodore P. Pavlic
, Andréa W. Richa:
Automated synthesis of scalable algorithms for inferring non-local properties to assist in multi-robot teaming. CASE 2017: 1522-1527 - 2015
- [c1]Taeyeong Choi
, Hyeon-Suk Na:
Stealthy behavior simulations based on cognitive data. ICMLC 2015: 68-74
Coauthor Index
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