E-LIB Suche - Ergebnisse für: Ko, Ching-Yun

1

Visual Pre-Training for Navigation: What Can We Learn from ..:

, In: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),

Wang, Yanwei ; Ko, Ching-Yun ; Agrawal, Pulkit - p. 3897-3902 , 2023

Link: https://doi.org/10.1109/..

2

Facile Prediction of Neutrophil Activation State from Micro..:

, In: 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI),

Liao, Wei ; Ko, Ching-Yun ; Weng, Tsui-Wei.. - p. 1-5 , 2022

Link: https://doi.org/10.1109/..

3

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper..:

, In: 2020 IEEE 15th International Conference on Solid-State & Integrated Circuit Technology (ICSICT),

Lin, Rui ; Ko, Ching-Yun ; He, Zhuolun... - p. 1-4 , 2020

Link: https://doi.org/10.1109/..

4

Extended Kalman Filtering with Low-Rank Tensor Networks for..:

, In: 2019 IEEE 58th Conference on Decision and Control (CDC),

Batselier, Kim ; Ko, Ching-Yun ; Wong, Ngai - p. 7148-7153 , 2019

Link: https://doi.org/10.1109/..

5

Tensor network subspace identification of polynomial state ..:

Batselier, Kim ; Ko, Ching-Yun ; Wong, Ngai
Automatica. 95 (2018) - p. 187-196 , 2018

Link: https://doi.org/10.1016/..

6

Sample-Specific Debiasing for Better Image-Text Models:

Wang, Peiqi ; Liu, Yingcheng ; Ko, Ching-Yun...
http://arxiv.org/abs/2304.13181. , 2023

Link: http://arxiv.org/abs/230..

7

Revisiting Contrastive Learning through the Lens of Neighbo..:

Ko, Ching-Yun
https://hdl.handle.net/1721.1/145052. , 2022

Link: https://hdl.handle.net/1..

8

Visual Pre-training for Navigation: What Can We Learn from ..:

Wang, Yanwei ; Ko, Ching-Yun ; Agrawal, Pulkit
http://arxiv.org/abs/2207.00052. , 2022

Link: http://arxiv.org/abs/220..

9

SynBench: Task-Agnostic Benchmarking of Pretrained Represen..:

Ko, Ching-Yun ; Chen, Pin-Yu ; Mohapatra, Jeet..
http://arxiv.org/abs/2210.02989. , 2022

Link: http://arxiv.org/abs/221..

10

Revisiting Contrastive Learning through the Lens of Neighbo..:

Ko, Ching-Yun ; Mohapatra, Jeet ; Liu, Sijia...
http://arxiv.org/abs/2112.04468. , 2021

Link: http://arxiv.org/abs/211..

11

Fast and Accurate Tensor Completion with Total Variation Re..:

Ko, Ching-Yun ; Batselier, Kim ; Yu, Wenjian.
10.1109/TIP.2020.2995061. , 2020

Link: https://hdl.handle.net/1..

12

HOTCAKE: Higher Order Tucker Articulated Kernels for Deeper..:

Lin, Rui ; Ko, Ching-Yun ; He, Zhuolun...
http://arxiv.org/abs/2002.12663. , 2020

Link: http://arxiv.org/abs/200..

13

Higher-Order Certification for Randomized Smoothing:

Mohapatra, Jeet ; Ko, Ching-Yun ; Weng, Tsui-Wei...
http://arxiv.org/abs/2010.06651. , 2020

Link: http://arxiv.org/abs/201..

14

Hidden Cost of Randomized Smoothing:

Mohapatra, Jeet ; Ko, Ching-Yun ; Tsui-Wei...
http://arxiv.org/abs/2003.01249. , 2020

Link: http://arxiv.org/abs/200..

15

POPQORN: Quantifying Robustness of Recurrent Neural Network:

Ko, Ching-Yun ; Lyu, Zhaoyang ; Weng, Tsui-Wei...
http://arxiv.org/abs/1905.07387. , 2019

Link: http://arxiv.org/abs/190..