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FEBRUARY2018

READING NOTES

Chris J. Maddison, Andriy Mnih, Yee Whye Teh. The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables. CoRR abs/1611.00712 (2016)
DEEP LEARNING MACHINE LEARNING

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FEBRUARY2018

READING NOTES

Eric Jang, Shixiang Gu, Ben Poole. Categorical Reparameterization with Gumbel-Softmax. CoRR, 2016.
DEEP LEARNING MACHINE LEARNING

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FEBRUARY2018

READING NOTES

Daniel Jiwoong Im, Sungjin Ahn, Roland Memisevic, Yoshua Bengio. Denoising Criterion for Variational Auto-Encoding Framework. AAAI, 2017.
DEEP LEARNING MACHINE LEARNING

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FEBRUARY2018

READING NOTES

Kihyuk Sohn, Honglak Lee, Xinchen Yan. Learning Structured Output Representation using Deep Conditional Generative Models. NIPS, 2015.
DEEP LEARNING MACHINE LEARNING

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FEBRUARY2018

READING NOTES

Diederik P. Kingma, Shakir Mohamed, Danilo Jimenez Rezende, Max Welling. Semi-supervised Learning with Deep Generative Models. NIPS, 2014.
DEEP LEARNING MACHINE LEARNING

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FEBRUARY2018

READING NOTES

Moritz Menze, Andreas Geiger. Object scene flow for autonomous vehicles. CVPR, 2015.
COMPUTER VISION

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FEBRUARY2018

READING NOTES

Jifeng Dai, Kaiming He, Jian Sun. Convolutional feature masking for joint object and stuff segmentation. CVPR, 2015.
COMPUTER VISION DEEP LEARNING

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FEBRUARY2018

READING NOTES

Jonas Uhrig, Marius Cordts, Uwe Franke, Thomas Brox. Pixel-Level Encoding and Depth Layering for Instance-Level Semantic Labeling. GCPR, 2016.
COMPUTER VISION DEEP LEARNING

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FEBRUARY2018

READING NOTES

Jifeng Dai, Kaiming He, Jian Sun. Instance-Aware Semantic Segmentation via Multi-task Network Cascades. CVPR. 2016.

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FEBRUARY2018

READING NOTES

Elad Hoffer, Itay Hubara, Nir Ailon. Deep unsupervised learning through spatial contrasting. CoRR abs/1610.00243, 2016.
COMPUTER VISION DEEP LEARNING

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