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Towards Understanding How Machines Can Learn Causal Overhypotheses


Jun 16, 2022
Eliza Kosoy, David M. Chan, Adrian Liu, Jasmine Collins, Bryanna Kaufmann, Sandy Han Huang, Jessica B. Hamrick, John Canny, Nan Rosemary Ke, Alison Gopnik


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Content-Context Factorized Representations for Automated Speech Recognition


May 19, 2022
David M. Chan, Shalini Ghosh


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What's in a Caption? Dataset-Specific Linguistic Diversity and Its Effect on Visual Description Models and Metrics


May 12, 2022
David M. Chan, Austin Myers, Sudheendra Vijayanarasimhan, David A. Ross, Bryan Seybold, John F. Canny

* The 1st Workshop on Vision Datasets Understanding, IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2022 

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Multi-Modal Pre-Training for Automated Speech Recognition


Oct 12, 2021
David M. Chan, Shalini Ghosh, Debmalya Chakrabarty, Björn Hoffmeister


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A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery


Apr 02, 2021
Aatif Jiwani, Shubhrakanti Ganguly, Chao Ding, Nan Zhou, David M. Chan

* 11 pages, 6 figures. Code available at https://github.com/aatifjiwani/rgb-footprint-extract/ 

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Active Learning for Video Description With Cluster-Regularized Ensemble Ranking


Jul 29, 2020
David M. Chan, Sudheendra Vijayanarasimhan, David A. Ross, John Canny


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Exploring Exploration: Comparing Children with RL Agents in Unified Environments


May 06, 2020
Eliza Kosoy, Jasmine Collins, David M. Chan, Jessica B. Hamrick, Sandy Huang, Alison Gopnik, John Canny

* Published as a workshop paper at "Bridging AI and Cognitive Science" (ICLR 2020) 

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Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics


Dec 11, 2018
Biye Jiang, David M. Chan, Tianhao Zhang, John F. Canny


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t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern Data


Jul 31, 2018
David M. Chan, Roshan Rao, Forrest Huang, John F. Canny

* To appear in HPML 2018 High Performance Machine Learning Workshop (Accepted, 2018) 

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