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Dian Ang Yap

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2020 – today

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2023
[i7]
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  - journals/corr/abs-2303-04248
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  - https://dblp.org/rec/journals/corr/abs-2303-04248
David Berthelot, Arnaud Autef, Jierui Lin, Dian Ang Yap, Shuangfei Zhai, Siyuan Hu, Daniel Zheng, Walter Talbott, Eric Gu:
TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation. CoRR abs/2303.04248 (2023)
2020
[i6]
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  - journals/corr/abs-2007-16012
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Josh Payne, Mario Srouji, Dian Ang Yap, Vineet Kosaraju:
BERT Learns (and Teaches) Chemistry. CoRR abs/2007.16012 (2020)

2010 – 2019

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2019
[c1]
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  - conf/iclr/PrabhuHYDS19
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Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri:
A Seed-Augment-Train Framework for Universal Digit Classification. DGS@ICLR 2019
[i5]
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  - journals/corr/abs-1905-08633
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Vinay Uday Prabhu, Sanghyun Han, Dian Ang Yap, Mihail Douhaniaris, Preethi Seshadri, John Whaley:
Fonts-2-Handwriting: A Seed-Augment-Train framework for universal digit classification. CoRR abs/1905.08633 (2019)
[i4]
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  - journals/corr/abs-1907-09061
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Vinay Uday Prabhu, Dian Ang Yap, Joyce Xu, John Whaley:
Understanding Adversarial Robustness Through Loss Landscape Geometries. CoRR abs/1907.09061 (2019)
[i3]
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  - journals/corr/abs-1907-12917
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Vinay Uday Prabhu, Dian Ang Yap, Alexander Wang, John Whaley:
Covering up bias in CelebA-like datasets with Markov blankets: A post-hoc cure for attribute prior avoidance. CoRR abs/1907.12917 (2019)
[i2]
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  - journals/corr/abs-1911-07418
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Dian Ang Yap, Nicholas Roberts, Vinay Uday Prabhu:
Grassmannian Packings in Neural Networks: Learning with Maximal Subspace Packings for Diversity and Anti-Sparsity. CoRR abs/1911.07418 (2019)
[i1]
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  - journals/corr/abs-1912-08986
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Nicholas Roberts, Dian Ang Yap, Vinay Uday Prabhu:
Deep Connectomics Networks: Neural Network Architectures Inspired by Neuronal Networks. CoRR abs/1912.08986 (2019)

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