eunchong/PR_Tensorflow_kr

Tensorflow KR의 논문 발표 모음집

PR_Tensorflow_kr

Tensorflow KR의 논문 발표 모음집

발표자 목록

• Jaejun Yoo
• Terry Taewoong Um
• Keunbong Kwak
• Taegyun Jeon
• Sung Kim
• Kiho Suh
• Seungil Kim
• Dongjun Jung
• Youngjae Choung
• 차준범
• Jiyang Kang
• Jinwon Lee
• Jihoon Kim
• Taesu Kim
• Byung-hak Kim
• 이광희
• Taeoh Kim

발표 목록

- [논문 번호: 논문 제목](유투브 링크)
    - 발표자
    - 논문 링크
        - 참고 문헌

• PR-001: Generative adversarial nets

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1406.2661

• PR-002: Deformable Convolutional networks

  • 발표자: Terry Taewoong Um
  • https://arxiv.org/abs/1703.06211
    • https://www.slideshare.net/TerryTaewoongUm/deformable-convolutional-network-2017

• PR-003: Learning phrase representations using RNN encoder-decoder for statistical machine translation

  • 발표자: Keunbong Kwak
  • https://arxiv.org/pdf/1406.1078.pdf
    • https://www.slideshare.net/keunbongkwak/learning-phrase-representations-using-rnn-encoder-decoder-for-statistical-machine-translation

• PR-004: Image Super-Resolution Using Deep Convolutional Networks

  • 발표자: Taegyun Jeon
  • https://arxiv.org/pdf/1501.00092

• PR-005: Playing Atari with Deep Reinforcement Learning

  • 발표자: Sung Kim
  • https://arxiv.org/abs/1312.5602
    • https://slack-files.com/T1UHM6UEN-F5ALD2W6T-7306fcba70

• PR-006: Neural Turing Machine

  • 발표자: Kiho Suh
  • https://arxiv.org/abs/1410.5401

• PR-007: Deep Photo Style Transfer

  • 발표자: Seungil Kim
  • https://arxiv.org/abs/1703.07511
    • http://www.modulabs.co.kr/DeepLAB_Paper/15096

• PR-008: Reverse Classification Accuracy

  • 발표자: Dongjun Jung
  • https://arxiv.org/abs/1702.03407

• PR-009: Distilling the Knowledge in a Neural Network

  • 발표자: Youngjae Choung
  • https://arxiv.org/abs/1503.02531

• PR-010: Auto-Encoding Variational Bayes

  • 발표자: 차준범
  • https://arxiv.org/abs/1312.6114

• PR-011: Spatial Transformer Networks

  • 발표자: Jiyang Kang
  • https://arxiv.org/abs/1506.02025

• PR-012: Faster R-CNN : Towards Real-Time Object Detection with Region Proposal Networks

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1506.01497

• PR-013: Domain Adversarial Training of Neural Network

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1505.07818
    • https://www.slideshare.net/thinkingfactory/pr12-dann-jaejun-yoo

• PR-014: On Human Motion Prediction using RNNs

  • 발표자: Terry Taewoong Um
  • https://arxiv.org/abs/1705.02445
    • https://www.slideshare.net/TerryTaewoongUm/human-motion-forecasting-generation-with-rnns

• PR-015:Convolutional Neural Networks for Sentence Classification

  • 발표자: Keunbong Kwak
  • https://arxiv.org/abs/1408.5882
    • https://www.slideshare.net/keunbongkwak/convolutional-neural-networks-for-sentence-classification

• PR-016: You only look once: Unified, real-time object detection

  • 발표자: Taegyun Jeon
  • https://arxiv.org/abs/1506.02640
    • https://www.slideshare.net/TaegyunJeon1/pr12-you-only-look-once-yolo-unified-realtime-object-detection

• PR-017: Neural Architecture Search with Reinforcement Learning

  • 발표자: Kiho Suh
  • https://arxiv.org/abs/1611.01578
    • https://www.slideshare.net/KihoSuh/neural-architecture-search-with-reinforcement-learning-76883153
    • https://www.slideshare.net/JungHoonSeo2/understanding-deep-learning-requires-rethinking-generalization-2017-12

• PR-018: A Simple Neural Network Module for Relational Reasoning (DeepMind)

  • 발표자: Sung Kim
  • https://arxiv.org/abs/1706.01427

• PR-019: Continuous Control with Deep Reinforcement Learning

  • 발표자: Seungil Kim
  • https://arxiv.org/abs/1509.02971
    • http://www.modulabs.co.kr/DeepLAB_Paper/15107

• PR-020: Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification

  • 발표자: Jiyang Kang
  • https://arxiv.org/abs/1502.01852

• PR-021: Batch Normalization

  • 발표자: Youngjae Choung
  • https://arxiv.org/abs/1502.03167

• PR-022: Infogan: Interpretable representation learning by information maximizing generative adversarial nets

  • 발표자: 차준범
  • https://arxiv.org/abs/1606.03657

• PR-023: YOLO9000: Better, Faster, Stronger

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1612.08242
    • https://www.slideshare.net/JinwonLee9/pr12-yolo9000

• PR-024: Pixel Recurrent Neural Network

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1601.06759
    • https://www.slideshare.net/thinkingfactory/pr12-pixelrnn-jaejun-yoo

• PR-025: Learning with side information through modality hallucination

  • 발표자: Terry Taewoong Um
  • https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Hoffman_Learning_With_Side_CVPR_2016_paper.pdf
    • https://www.slideshare.net/TerryTaewoongUm/learning-with-side-information-through-modality-hallucination-2016

• PR-026: Notes for CVPR Machine Learning Session

  • 발표자: Taegyun Jeon
  • https://arxiv.org/abs/1702.08690
  • https://arxiv.org/abs/1612.04844
  • http://openaccess.thecvf.com/content_cvpr_2017/papers/Yu_On_Compressing_Deep_CVPR_2017_paper.pdf

• PR-027: GloVe - Global vectors for word representation

  • 발표자: Keunbong Kwak
  • https://www.aclweb.org/anthology/D14-1162
    • https://www.slideshare.net/keunbongkwak/gloveglobal-vectors-for-word-representation

• PR-028: Densely Connected Convolutional Networks

  • 발표자: Sung Kim
  • https://arxiv.org/abs/1608.06993

• PR-029: Apprenticeship Learning via Inverse Reinforcement Learning

  • 발표자: Kiho Suh
  • https://ai.stanford.edu/~ang/papers/icml04-apprentice.pdf
    • https://drive.google.com/file/d/0B1ay5kXWGF_9VHc2N2VERThxMWs/view

• PR-030: Photo-Realistic Single Image Super Resolution Using a Generative Adversarial Network

  • 발표자: Seungil Kim
  • https://arxiv.org/pdf/1609.04802.pdf
    • http://www.modulabs.co.kr/DeepLAB_Paper/15591

• PR-031: Learning to learn by gradient descent by gradient descent

  • 발표자: 차준범
  • https://arxiv.org/abs/1606.04474

• PR-032: Deep Visual-Semantic Alignments for Generating Image Descriptions

  • 발표자: Jiyang Kang
  • https://cs.stanford.edu/people/karpathy/cvpr2015.pdf

• PR-033: PVANet: Lightweight Deep Neural Networks for Real-time Object Detection

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1611.08588 -https://www.slideshare.net/JinwonLee9/pr12-pva-net

• PR-034: Inception and Xception

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1409.4842
  • https://arxiv.org/abs/1610.02357
    • https://www.slideshare.net/thinkingfactory/pr12-inception-and-xception-jaejun-yoo

• PR-035: Understanding Black-box Predictions via Influence Functions

  • 발표자: Terry Taewoong Um
  • https://arxiv.org/abs/1703.04730
  • https://www.slideshare.net/TerryTaewoongUm/understanding-blackbox-predictions-via-influence-functions-2017

• PR-036: Learning to Remember Rare Events

  • 발표자: Taegyun Jeon
  • https://arxiv.org/abs/1703.03129
    • https://www.slideshare.net/TaegyunJeon1/pr12-pr036-learning-to-remember-rare-events

• PR-037: Ask me anything: Dynamic memory networks for natural language processing

  • 발표자: Keunbong Kwak
  • https://arxiv.org/abs/1506.07285
    • https://www.slideshare.net/keunbongkwak/ask-me-anything-dynamic-memory-networks-for-natural-language-processing

• PR-038: Explaining and Harnessing Adversarial Examples

  • 발표자: Kiho Suh
  • https://arxiv.org/abs/1412.6572
    • https://drive.google.com/file/d/0B1ay5kXWGF_9WnBmd3NuemN5M2s/view

• PR-039: Dropout as a Bayesian approximation:Representing Model Uncertainty in Deep Learning

  • 발표자: 차준범
  • https://arxiv.org/abs/1506.02142

• PR-040: WaveNet - A Generative Model for Raw Audio

  • 발표자: Seungil Kim
  • https://arxiv.org/abs/1609.03499
  • http://www.modulabs.co.kr/DeepLAB_Paper/16552

• PR-041: Show and Tell: A Neural Image Caption Generator

  • 발표자: Jiyang Kang
  • https://arxiv.org/abs/1411.4555

• PR-042: Adam: A Method for Stochastic Optimization

  • 발표자: Jihoon Kim
  • https://arxiv.org/abs/1412.6980

• PR-043: HyperNetworks

  • 발표자: Taesu Kim
  • https://arxiv.org/abs/1609.09106
    • https://www.slideshare.net/TaesuKim3/pr043-hypernetworks

• PR-044: MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1704.04861
    • https://www.slideshare.net/JinwonLee9/mobilenet-pr044

• PR-045: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

  • 발표자: Taeoh Kim
  • https://arxiv.org/abs/1606.00915
    • https://www.slideshare.net/TaeohKim4/pr045-deep-labsemanticsegmentation

• PR-046: Deep Knowledge Tracing

  • 발표자: Byung-hak Kim
  • https://arxiv.org/abs/1506.05908

• PR-047: Learning Deep Features for Discriminative Localization

  • 발표자: 이광희
  • http://cnnlocalization.csail.mit.edu/Zhou_Learning_Deep_Features_CVPR_2016_paper.pdf

• PR-048: Towards Principled Methods for Training Generative Adversarial Networks

  • 발표자: Jihoon Kim
  • https://arxiv.org/abs/1701.04862

• PR-049: Attention is All You Need

  • 발표자: Kiho Suh
  • https://arxiv.org/pdf/1706.03762.pdf
    • https://drive.google.com/file/d/1b_nak_UQVgLua5z1cBas3q3ppd6SX7ud/view?usp=sharing

• PR-050: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

  • 발표자: Taegyun Jeon
  • https://arxiv.org/abs/1506.04214
    • https://www.slideshare.net/TaegyunJeon1/pr12-pr050-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting

• PR-051: Conditional Generative Adversarial Nets

  • 발표자: Seungil Kim
  • https://arxiv.org/pdf/1411.1784.pdf
    • http://www.modulabs.co.kr/DeepLAB_Paper/17216

• PR-052: not found

• PR-053: Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

  • 발표자: Taesu Kim
  • https://arxiv.org/abs/1610.02391
    • https://www.slideshare.net/TaesuKim3/pr052-gradcam-visual-explanations-from-deep-networks-via-gradientbased-localization

• PR-054: ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1707.01083
    • https://www.slideshare.net/JinwonLee9/shufflenet-pr054

• PR-055: Neural Machine Translation by Jointly Learning to Align and Translate

  • 발표자: Jiyang Kang
  • https://arxiv.org/abs/1409.0473

• PR-056: Capsule Network(Dynamic Routing Between Capsules)

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1710.09829
    • https://www.slideshare.net/thinkingfactory/pr12-capsule-networks-jaejun-yoo

• PR-057: Mask R-CNN

  • 발표자: Taeoh Kim
  • https://arxiv.org/abs/1703.06870
    • https://www.slideshare.net/TaeohKim4/pr057-mask-rcnn

• PR-058: The Consciousness Prior

  • 발표자: Byung-hak Kim
  • https://arxiv.org/abs/1709.08568

• PR-059: Style Transfer from Non-Parallel Text by Cross-Alignment

  • 발표자: Kiho Suh
  • https://arxiv.org/pdf/1705.09655.pdf
    • https://drive.google.com/file/d/1zuQuhMaIJ1Qs7OKmLXamBweTzYO4jXVw/view?usp=sharing

• PR-060: Deep Neural Networks for YouTube Recommendations

  • 발표자: Keunbong Kwak
  • https://research.google.com/pubs/pub45530.html
    • https://www.slideshare.net/keunbongkwak/deep-neural-networks-for-youtube-recommendations

• PR-061: Understanding Deep Learning Requires Rethinking Generalization

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1611.03530
    • https://www.slideshare.net/thinkingfactory/pr12-understanding-deep-learning-requires-rethinking-generalization

• PR-062: Deep Learning: A Critical Appraisal

  • 발표자: Terry Taewoong Um
  • https://arxiv.org/abs/1801.00631
    • https://www.slideshare.net/TerryTaewoongUm/deep-learning-a-critical-appraisal-2018

• PR-063: Peephole: Predicting Network Performance Before Training

  • 발표자: Taegyun Jeon
  • https://arxiv.org/abs/1712.03351

• PR-064: Wide&Deep Learning for Recommender Systems

  • 발표자: Keunbong Kwak
  • https://arxiv.org/abs/1606.07792
    • https://www.slideshare.net/keunbongkwak/widedeep-learning-for-recommender-systems

• PR-065: High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

  • 발표자: 이광희
  • https://arxiv.org/abs/1711.11585

• PR-066: Don't decay the learning rate, increase the batch size

  • 발표자: 차준범
  • https://arxiv.org/abs/1711.00489

• PR-067: Audio Super Resolution using Neural Networks

  • 발표자: Seungil Kim
  • https://arxiv.org/abs/1708.00853
    • http://www.modulabs.co.kr/DeepLAB_Paper/18909
    • https://kuleshov.github.io/audio-super-res/

• PR-068: DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks

  • 발표자: Jiyang Kim
  • https://arxiv.org/abs/1704.04110

• PR-069: Efficient Neural Architecture Search via Parameter Sharing

  • 발표자: Jinwon Lee
  • https://arxiv.org/abs/1802.03268
    • https://www.slideshare.net/JinwonLee9/efficient-neural-architecture-search-via-parameter-sharing

• PR-070: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud

  • 발표자: Taesu Kim
  • https://arxiv.org/abs/1706.10268
    • https://www.slideshare.net/TaesuKim3/pr070-safetynets-verifiable-execution-of-deep-neural-networks-on-an-untrusted-cloud

• PR-071: Categorical Reparameterization with Gumbel Softmax

  • 발표자: Jaejun Yoo
  • https://arxiv.org/abs/1611.01144
  • https://arxiv.org/abs/1611.00712
    • https://www.slideshare.net/thinkingfactory/pr12-categorical-reparameterization-with-gumbel-softmax

• PR-072: Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

  • 발표자: Taeoh Kim
  • https://arxiv.org/abs/1510.00149
    • https://www.slideshare.net/TaeohKim4/pr072-deep-compression

• PR-073: Generative Semantic Manipulation with Contrasting GAN

  • 발표자: 이광희
  • https://arxiv.org/abs/1708.00315
    • https://www.slideshare.net/KwangHeeLee12/pr073-generative-semantic-manipulation-with-contrasting-gan

• PR-074: ObamaNet: Photo-realistic lip-sync from text

  • 발표자: Byung-hak Kim
  • https://nips2017creativity.github.io/doc/ObamaNet.pdf

• PR-075: On Calibration of Modern Neural Networks (2017)

  • 발표자: Terry Taewoong Um
  • https://arxiv.org/abs/1706.04599
    • https://vimeo.com/238242536
    • https://www.slideshare.net/TerryTaewoongUm/on-calibration-of-modern-neural-networks-2017