Student-Research-Training-THU

SaturnLab-学术活动

2017年学术活动

• 2017年11月18日

宋丹丹：

• 2017年11月11日

闫泽禹：

• 2017年11月04日

常嘉辉：GAN

[x] Generative Adversarial Networks: An Overview, https://arxiv.org/abs/1710.07035.

• 2017年10月28日

陆昕：PixelRNN

[x] Oord, Aaron van den, Nal Kalchbrenner, and Koray Kavukcuoglu. "Pixel recurrent neural networks." arXiv preprint arXiv:1601.06759 (2016).

• 2017年10月21日

冯 杰：DeepST

[x] Deep Spatio-Temporal Residual Networks for Citywide Crowd Flows Prediction, AAAI 2017.

议题： AlphaGo Zero

[x] David Silver, Mastering the game of Go without human knowledge, nature 2017

[x] Xilinx Inc., Introduction to FPGA Design with Vivado High-Level Synthesis, UG998, 2013.

• 2017年10月14日

宋丹丹： [x] Matthieu Courbariaux et al., Binarized Neural Networks: Training Neural Networks withWeights and Activations Constrained to +1 or -1, arxiv, 2016.

贺子航： [x] E. Nurvitadhi et al., Accelerating Binarized Neural Networks: Comparison of FPGA, CPU, GPU, and ASIC, International Conference on Field-Programmable Technology (FPT), pp. 77-84, 2016.

陈 震： [x] Umuroglu, Yaman et al., FINN: A Framework for Fast, Scalable Binarized Neural Network Inference, ACM/SIGDA International Symposium on Field-Programmable Gate Arrays(FPGA), pp.65-74, 2017.

• 2017年10月7日

柳 荫： [x] Huang, Gao, Zhuang Liu, Kilian Q. Weinberger, and Laurens van der Maaten, Densely Connected Convolutional Networks, CVPR 2017.

陈 震： [x] Annett Ungethüm et al., Overview on Hardware Optimizations for Database Engines, BTW 2017.

• 2017年9月30日

郑文勋： [x] Graves, A., Wayne, G. and Danihelka, I., 2014. Neural turing machines. arXiv preprint arXiv:1410.5401.

陈 震： [x] Jouppi, N.P. et al., In-Datacenter Performance Analysis of a Tensor Processing Unit, ISCA 2017.

• 2017年9月23日

陈 震： [x] Jeff. Dean et al., Large scale distributed deep networks. In Advances in neural information processing systems, pp. 1223-1231, 2012.

• 2017年5月21日

郑文勋： [x] Yu Su et al., In-Situ Bitmaps Generation and Efficient Data Analysis based on Bitmaps, HPDC 2015.

• 2017年4月8日

郑文勋： [x] Nguyen, Anh, Jason Yosinski, and Jeff Clune. "Deep neural networks are easily fooled: High confidence predictions for unrecognizable images." CVPR. 2015.

供选论文清单

TensorFlow系列

DistBelief - large scale distributed deep networks, nips 2012.

TensorFlow - A system for large-scale machine learning, OSDI 2016.

TPU - In-Datacenter Performance Analysis of a Tensor Processing Unit, ISCA 2017.

TFX - A TensorFlow-Based Production-Scale Machine Learning Platform, kdd2017.

CV系列

Densely Connected Convolutional Networks,CVPR 2017.

ASR语音识别

Multichannel Signal Processing with Deep Neural Networks for Automatic Speech Recognition-TASLP-2017

XXXX

[Quasi-Recurrent Neural Networks](https://arxiv.org/abs/1611.01576)

[Training RNNs as Fast as CNNs](https://arxiv.org/abs/1709.02755)

[Semi-Supervised Classification with Graph Convolutional Networks](https://arxiv.org/abs/1609.02907)

[A Survey on Transfer Learning](http://www3.ntu.edu.sg/home/sinnopan/publications/TLsurvey_0822.pdf)

[How transferable are features in deep neuralnetworks?](https://arxiv.org/abs/1411.1792)

[Progressive Neural Networks](https://arxiv.org/abs/1606.04671)

[One-Shot Learning of Object Categories](http://vision.stanford.edu/documents/Fei-FeiFergusPerona2006.pdf)

[One-shot Learning with Memory-Augmented Neural Networks](https://arxiv.org/abs/1605.06065)

[Auto-Encoding Variational Bayes](https://arxiv.org/abs/1312.6114)

[Autoencoding beyond pixels using a learned similarity metric](https://arxiv.org/abs/1512.09300)