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简介：
利用TensorFlow实现了苹果的第一篇人工智能论文 Learning from Simulated and Unsupervised Images through Adversarial Training
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2.【博客】The Major Advancements in Deep Learning in 2016
简介：
该博客主要陈述了深度学习在2016年的主要进展，包括以下几个方面：
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3.【视频】XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
简介：
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary values resulting in 32x memory saving. In XNOR-Networks, both the filters and the input to convolutional layers are binary. XNOR-Networks approximate convolutions using primarily binary operations. This results in 58x faster convolutional operations and 32x memory savings. XNOR-Nets offer the possibility of running state-of-the-art networks on CPUs (rather than GPUs) in real-time. Our binary networks are simple, accurate, efficient, and work on challenging visual tasks. We evaluate our approach on the ImageNet classification task. The classification accuracy with a Binary-Weight-Network version of AlexNet is only 2.9% less than the full-precision AlexNet (in top-1 measure). We compare our method with recent network binarization methods, BinaryConnect and BinaryNets, and outperform these methods by large margins on ImageNet, more than 16% in top-1 accuracy.
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4.【课程】伯克利大学2017年春季最新课程：深度增强学习
简介：
下面列出了本课程的大纲。PPT等参考材料将随课程进度放出。
1 1/18 导论和课程概述 Schulman，Levine，Finn
2 1/23 监督学习：动力系统和行为克隆 Levine 2 1/25 优化控制背景：LQR，规划 Levine 2 1/27 复习：autodiff，反向传播，优化 Finn 3 1/30 用数据学习动力系统模型 Levine 3 2/1 优化控制与从优化控制器学习 Levine 4 2/6 客座讲座：Igor Mordatch，OpenAI Mordatch 4 2/8 RL的定义，值迭代，策略迭代 Schulman 5 2/13 增强学习与策略梯度 Schulman 5 2/15 Q函数：Q学习，SARSA，等 Schulman 6 2/22 高级Q函数：重放缓冲，目标网络，双Q学习 Schulman 7 2/27 高级模型学习：从图像和视频学习 7 3/1 高级模拟：policy distillation Finn 8 3/6 反向RL Finn 8 3/8 高级策略梯度：自然梯度和TPRO Schulman 9 3/13 策略梯度方差缩减与 actor-critic算法 Schulman 9 3/15 策略梯度和时间差分法小结 Schulman 10 3/20 探索问题 Schulman 10 3/22 深度增强学习中存在的问题和挑战 Levine 11 3/27 春假 11 3/29 12 4/3 深度增强学习中的平行和异步 Levine 12 4/5 客座讲座：Mohammad Norouzi，Google Brain Norouzi 13 4/10 客座讲座：Pieter Abbeel，UC Berkeley & OpenAI Abbeel 13 4/12 项目成果报告 14 4/17 高级模拟学习和反向RL算法 Finn 14 4/19 客座讲座（待定） 待定 15 4/24 客座讲座：Aviv Tamar，UC Berkeley Tamar 15 4/26 期末项目presentation 16 5/1 期末项目presentation 16 5/3 期末项目presentation原文链接：
5.【NIPS 2016 论文】Learning feed-forward one-shot learners
简介：
One-shot learning is usually tackled by using generative models or discriminative embeddings. Discriminative methods based on deep learning, which are very effective in other learning scenarios, are ill-suited for one-shot learning as they need large amounts of training data. In this paper, we propose a method to learn the parameters of a deep model in one shot. We construct the learner as a second deep network, called a learnet, which predicts the parameters of a pupil network from a single exemplar. In this manner we obtain an efficient feed-forward one-shot learner, trained end-to-end by minimizing a one-shot classification objective in a learning to learn formulation. In order to make the construction feasible, we propose a number of factorizations of the parameters of the pupil network. We demonstrate encouraging results by learning characters from single exemplars in Omniglot, and by tracking visual objects from a single initial exemplar in the Visual Object Tracking benchmark.
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视频讲解：
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