myungsub

• Improving Pairwise Ranking for Multi-label Image Classification
• Improved Deep Metric Learning with Multi-class N-pair Loss Objective
• Dimensionality Reduction by Learning an Invariant Mapping
• Exponential discriminative metric embedding in deep learning
• Learning Universal Embeddings from Attributes
• TVAE: TRIPLET-BASED VARIATIONAL AUTOENCODER USING METRIC LEARNING
• [Simple Triplet Loss Based on Intra/Inter-class Metric Learning for Face Verification](http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w23/Ming

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Papers from Super SloMo references

• Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation [Paper]
  • Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz
  • CVPR 2018 (splotlight)
• Video frame synthesis using deep voxel flow [Paper] [Code]
  • Z. Liu, R. Yeh, X. Tang, Y. Liu, and A. Agarwala.
  • ICCV 2017
• Video frame interpolation via adaptive separable convolution. [Paper] [Code]

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How I gather all the (research-related) information

1. Twitter
2. Facebook
3. Reddit - Machine Learning subreddit
4. Arxiv-Sanity
5. Github
6. Conference accepted papers list
7. Conference workshop accepted papers list
8. Manual wandering of famous blogs

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NIPS 2017

• Temporal Coherency based Criteria for Predicting Video Frames using Deep Multi-stage Generative Adversarial Networks
• Prototypical Networks for Few-shot Learning
• Good Semi-supervised Learning That Requires a Bad GAN
• Neural Discrete Representation Learning
• Learning Disentangled Representations with Semi-Supervised Deep Generative Models

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• Self-Transfer Learning for Fully Weakly Supervised Object Localization
  • by Sangheum Hwang and Hyo-Eun Kim (both from Lunit)
  • Arxiv:1602, MICCAI 2016
  • Datasets used: KIT(training), Shenzhen(test), MC set(test)
    • Localization annotation is obtained by human clinicians
• LEARNING TO DIAGNOSE FROM SCRATCH BY EXPLOITING DEPENDENCIES AMONG LABELS [paper]
  • Li Yao, Eric Poblenz, Dmitry Dagunts, Ben Covington, Devon Bernard, Kevin Lyman
  • Arxiv:1710
• Learning to detect chest radiographs containing lung nodules using visual attention networks [paper]
• Emanuele Pesce, Petros-Pavlos Ypsilantis, Samuel Withey, Robert Bakewell, Vicky Goh, Giovanni Montana

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Github with Mathjax Chrome Extension Test

$$ E = mc^2 $$ I want this equation $ \alpha = \beta + \gamma $ to render correctly

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{"lastUpload":"2017-04-10T12:47:54.193Z","extensionVersion":"v2.6.2"}

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# Jupyter Notebook Tutorial
https://www.digitalocean.com/community/tutorials/how-to-set-up-a-jupyter-notebook-to-run-ipython-on-ubuntu-16-04

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cjson = require 'cjson'
f = io.open(filename, 'r')
text = f:read()
info = cjson.decode(text)

-- 'info' is a table containing everything
-- info.all_losses
-- info.iter
-- info.loss_history
-- info.results_history

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Torch blog - GTSRB w. spatial transformer

• J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “The german traffic sign recognition benchmark: A multi-class classification competition,” in Proc. IJCNN, 2011, pp. 1453–1460. [Online]
• J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, "Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” Neural Netw., vol. 32, pp. 323–332, Aug. 2012. [Online]

** Recognition **

• Sermanet, P. and LeCun, Y. (2011). Traffic sign recognition with multi-scale convolutional networks. In Proceedings of the IEEE International Joint Conference on Neural Networks, pages 2809–2813. IEEE Press.
• Ciresan, D. C., Meier, U., Masci, J., and Schmidhuber, J. (2011). A committee of neural networks for traffic sign classification. In Proceedings of the IEEE Internationa