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Poll的笔记

徐晗曦

Koth

brmson Open Source Question Answering https://github.com/brmson Xserpy Python implementation of Shift-Reduce semantic parser: http://ceur-ws.org/Vol-1180/CLEF2014wn-QA-XuEt2014.pdf Household Intelligent Assistant This is a prototype of a speech-controlled personal assistant - easy to deploy (on Linux), can talk about the weather, tell you (very dry) jokes, the time and The Guardian news summary. Attention word scanning using PocketSphinx (locally, no background streaming to the cloud), speech reco powered by Google, intent detection by wit.ai. It responds to the name Phoenix /ˈfiːnɪks/. Enjoy!

Denny Britz http://blog.dennybritz.com/ http://www.wildml.com/ Google Brain team

CATEGORIES
    Conversational Agents
        DEEP LEARNING FOR CHATBOTS, PART 2 – IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW
        DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
    Convolutional Neural Networks
        IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW
        UNDERSTANDING CONVOLUTIONAL NEURAL NETWORKS FOR NLP
    Deep Learning
        LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
        DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
        ATTENTION AND MEMORY IN DEEP LEARNING AND NLP
        IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW
        UNDERSTANDING CONVOLUTIONAL NEURAL NETWORKS FOR NLP
        RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 3 – BACKPROPAGATION THROUGH TIME AND VANISHING GRADIENTS
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS
        SPEEDING UP YOUR NEURAL NETWORK WITH THEANO AND THE GPU
        IMPLEMENTING A NEURAL NETWORK FROM SCRATCH IN PYTHON – AN INTRODUCTION
    GPU
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
        SPEEDING UP YOUR NEURAL NETWORK WITH THEANO AND THE GPU
    Language Modeling
        RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
        ATTENTION AND MEMORY IN DEEP LEARNING AND NLP
        RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 3 – BACKPROPAGATION THROUGH TIME AND VANISHING GRADIENTS
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS
    Memory
    Neural Networks
    NLP
        DEEP LEARNING FOR CHATBOTS, PART 2 – IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW
        DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
        ATTENTION AND MEMORY IN DEEP LEARNING AND NLP
        IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW
    Recurrent Neural Networks
        RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
        DEEP LEARNING FOR CHATBOTS, PART 2 – IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW
        RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 3 – BACKPROPAGATION THROUGH TIME AND VANISHING GRADIENTS
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS
    Reinforcement Learning
        LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
    RNNs
        DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
        RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 3 – BACKPROPAGATION THROUGH TIME AND VANISHING GRADIENTS
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
        RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS
    Tensorflow
        RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
        
    LEARNING REINFORCEMENT LEARNING (WITH CODE, EXERCISES AND SOLUTIONS)
    RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
    DEEP LEARNING FOR CHATBOTS, PART 2 – IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW
    DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
    
    IMPLEMENTING A CNN FOR TEXT CLASSIFICATION IN TENSORFLOW
    SPEEDING UP YOUR NEURAL NETWORK WITH THEANO AND THE GPU
    IMPLEMENTING A NEURAL NETWORK FROM SCRATCH IN PYTHON – AN INTRODUCTION
    UNDERSTANDING CONVOLUTIONAL NEURAL NETWORKS FOR NLP
    
    
    DEEP LEARNING FOR CHATBOTS, PART 1 – INTRODUCTION
    RNNS IN TENSORFLOW, A PRACTICAL GUIDE AND UNDOCUMENTED FEATURES
    DEEP LEARNING FOR CHATBOTS, PART 2 – IMPLEMENTING A RETRIEVAL-BASED MODEL IN TENSORFLOW
    RECURRENT NEURAL NETWORKS TUTORIAL, PART 1 – INTRODUCTION TO RNNS
    RECURRENT NEURAL NETWORKS TUTORIAL, PART 2 – IMPLEMENTING A RNN WITH PYTHON, NUMPY AND THEANO
    RECURRENT NEURAL NETWORKS TUTORIAL, PART 3 – BACKPROPAGATION THROUGH TIME AND VANISHING GRADIENTS
    RECURRENT NEURAL NETWORK TUTORIAL, PART 4 – IMPLEMENTING A GRU/LSTM RNN WITH PYTHON AND THEANO

Christopher Olah https://christopherolah.wordpress.com/ http://colah.github.io/

Neural Networks (General)
    Neural Networks, Manifolds, and Topology
    Deep Learning, NLP, and Representations
    Calculus on Computational Graphs: Backpropagation
    Neural Networks, Types, and Functional Programming

Recurrent Neural Networks
    Understanding LSTM Networks
    Attention and Augmented Recurrent Neural Networks On Distill

Convolutional Neural Networks
    Conv Nets A Modular Perspective
    Understanding Convolutions
    Groups & Group Convolutions
    Deconvolution and Checkerboard Artifacts On Distill

Visualizing Neural Networks
    Visualizing MNIST An Exploration of Dimensionality Reduction
    Visualizing Representations Deep Learning and Human Beings
    Inceptionism Going Deeper into Neural Networks On the Google Research Blog
    Four Experiments in Handwriting with a Neural Network On Distill

Miscellaneous
    Fanfiction, Graphs, and PageRank
    Data.List Recursion Illustrated
    Visual Information Theory

Traditional Papers
    Document embedding with paragraph vectors On ArXiv [PDF]
    TensorFlow: Large-scale machine learning on heterogeneous systems On TensorFlow.org [PDF]
    Concrete Problems in AI Safety On ArXiv [PDF]
    Conditional Image Synthesis with Auxiliary Classifier GANs On ArXiv [PDF]

小石头的码疯窝 https://zhuanlan.zhihu.com/burness-DL

Andrej Karparthy

曹建平 男 湖南省 长沙市 我是一名研究僧。。。。。。苦逼的老博士。。。 http://blog.sciencenet.cn/u/cjpnudt http://blog.sciencenet.cn/blog-656867-994497.html

[读论文]---089 在隐形车联网上的出租车驾驶行文分析：一个社交 Taxi Driving Behavior Analysis in Latent Vehicle-to-Vehicle Networks: ASocial Influence Perspective
[读论文]---088 不是所有的神经（网络）嵌入都是天生平等的 Not All Neural Embeddings are Born Equal
[读论文]---087 文本分类的清晰和模糊的句法特征 Explicit and Implicit Syntactic Features for Text Classification
[读论文]---086 为什么一个乘积的混合包含了某个混合的乘积？ When Does a Mixture of Products Contain a Product of Mixtures?
[读论文]---085 词和短语的分布式表示以及他们的合成 Distributed representations of words and phrases and theircompositionality
[读论文]---084 基于神经网络的序列-序列学习 Sequence to Sequence Learning with Neural Networks
[读论文]---083 通过标齐和翻译的联合学习实现神经机器翻译 NEURAL MACHINE TRANSLATION BY JOINTLY LEARNING TO ALIGN AND TRANSLATE
[读论文]---082 卷积神经网络的最新进展 Recent Advances in Convolutional Neural Networks
[读论文]---081 匹配自然语言句子的卷积神经网络结构 Convolutional Neural Network Architectures for Matching NaturalLanguage Sentences
[读论文]---080 基于深度卷积神经网络的短文本情感分析 Deep Convolutional Neural Networks for Sentiment Analysis of ShortTexts
[读论文]---079 用一个简单的CNN建模、可视化以及总结文档 Modelling, Visualising and Summarising Documents with a SingleConvolutional Neural Network
[读论文]---078 使用点卷积神经网络学习网络搜索的语义表示 Learning Semantic Representations Using Convolutional Neural Networks for Web Search
[读论文]---077 自然语言处理的统一框架：多任务学习的深度神经 A Unified Architecture for Natural Language Processing: Deep NeuralNetworks with Multitask Learning
[读论文]---076 基于双向LSTM-CNNs的命名实体识别（NER） Named Entity Recognition with Bidirectional LSTM-CNNs
[读论文]---075 通过动态多池化CNN进行事件抽取 Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks
[读论文]---074 神经网络用于句子分类 Convolutional Neural Networks for Sentence Classification
[读论文]---073 局部中心机制的高维动态方程诅咒 The Curse of Highly Variable Functions for Local Kernel Machines
[读论文]---072 深度学习-自然语言处理（NLP）综述 Deep learning on NLP
[读论文]---071 Deep learning 深度学习综述 Deep learning
[读论文]---070 通过神经网络降维 Reducing the Dimensionality of Data with Neural Networks
[读论文]---069 内里朝外：单词表示和短语表示的两个联合预测模 Inside Out: Two Jointly Predictive Models for Word Representations andPhrase Representations
[读论文]---068 实现社会计算的模式转换：ACP方法 Word Embeddings through Hellinger PCA
[读论文]---067 实现社会计算的模式转换：ACP方法 Toward a Paradigm Shift in Social Computing: The ACP Approach
[读论文]---066 重返词嵌入 Word Embedding Revisited: A New Representation Learning and ExplicitMatrix Factorization Perspective
[读论文]---065 句子和文档的分布式表示 Distributed Representations of Sentences and Documents
[读论文]---064 别统计，预测 Don’t count, predict! A systematiccomparison of context-counting vs. context-predicting semantic vectors
[读论文]---063 NLP从零开始 Natural Language Processing (Almost) from Scratch 
[读论文]---062 一个自然语言处理的统一的架构：面向多种任务的 A Unified Architecture for Natural Language Processing: Deep NeuralNetworks with Multitask Learning
[读论文]---061 基于邻居的协同过滤的设计选择的一个实验分析 An Empirical  Analysis of Design  Choices in Neighborhood-Based Collaborative  Filtering Algorithms
[读论文]---060 植入性分割模型的图划分算法 Algorithms for  Graph Partitioning on the Planted Partition Model
[读论文]--WWW-059 多限制图分割的多层次算法METIS Multilevel  Algorithms for Multi-Constraint Graph Partitioning
[读论文]--WWW11-058 使用Tweets作事件摘要 Event  Summarization using Tweets
[读论文]--WWW13-057 时空动态在线信息素 Spatio-Temporal  Dynamics of Online Memes: A Study of Geo-Tagged Tweets
[读论文]--KDD15-056 将世界知识通过异构信息网络综合到文档聚类 Incorporating  World Knowledge to Document Clustering via Heterogeneous Information Networks
[读论文]--KDD12-055 多标签假设的重复应用 Multi-Label  Hypothesis Reuse
[读论文]--IJCAI-054学习社交知识的多模式的贝叶斯植入 Multi-Modal  Bayesian Embeddings for Learning Social Knowledge Graphs
[读论文]--WSDM -053推特排名：寻找话题敏感又影响力的推特用户 TwitterRank:  Finding Topic-sensitive Influential Twitterers
[读论文]--10KDD -052 社交沟通网络中的信息路径结构 The Structure of  Information Pathways in a Social Communication Network
[读论文]--10KDD -051 链接预测上的新视角和方法 New Perspectives and Methods in Link Prediction
[读论文]--03CIKM -050 社交网络的链接预测 The Link Prediction Problem for Social Networks
[读论文]--04WWW -049 博客空间的信息扩散 Information Diffusion Through Blogspace
[读论文]--ICML -048 图匹配算法 Similarity  Flooding: A Versatile Graph Matching Algorithm and its Application to Schema  Matching
[读论文]--ICML -047 使用梯度下降来学习排序 Learning to Rank using Gradient Descent
[读论文]--KDD 09-046 线之间的连接：用文本扩大社会网络 Connections  between the Lines: Augmenting Social Networks with Text
[读论文]--045 综合链接和内容的社区发现：一个歧视性的方法 Combining  Link and Content for Community Detection: A Discriminative Approach
[读论文]--044 大型稀疏网络对齐问题的算法 Algorithms  for Large, Sparse Network Alignment Problems
[读论文]-KDD08-043 社交网络的微小演化 Microscopic  Evolution of Social Networks
[读论文]-KDD15-042 推断多个异构社交网络之间的锚链接 Inferring  Anchor Links across Multiple Heterogeneous Social Networks
[读论文]-KDD15-041 组织结构图的推断 Organizational  Chart Inference
[读论文]-KDD15-040 一个为预测推文流行性自励点处理模型 Title：SEISMIC:  A Self-Exciting Point Process Model for Predicting Tweet Popularity
[读论文]-SIGIR03-039 基于非负矩阵分解的文档聚类 Title：Document Clustering Based On Non-negative Matrix Factorization
[读论文]-KDD01-038利用双向谱图分解同时对文档和词聚类 Coclustering documents and words using Bipartite Spectral Graph  Partitioning
[读论文]-MP13-037 一个解决正交限制的最优化的可行方法 A feasible method for optimization with orthogonality constraints
[读论文]-WWW13-036 一个双词汇的短文本话题模型 A Biterm Topic Model for Short Texts
[读论文]-VLDB-035 多时间序列中的流式模式发现 Streaming Pattern Discovery in Multiple Time-Series
[读论文]-arXiv-034 构建时间动态图中事件探测的选择异常集合 Less  is More: Building Selective Anomaly Ensembles with Application to Event  Detection in Temporal Graphs
[读论文]-IEEE TBD-033 微博的聚类和打标签 Embracing  Information Explosion without Choking: Clustering and Labeling in  Microblogging
[读论文]-TKDE-032 推文分割以及它在命名实体识别上的应用 Tweet  Segmentation and its Application to Named Entity Recognition
[读论文]-EMNLP-031 推文中的命名实体识别（NER）：一个实验性的 Named  Entity Recognition in Tweets: An Experimental Study
[读论文]-KDD14-030 故障诊断中将事件和时间序列关联 Correlating  Events with Time Series for Incident Diagnosis
[读论文]-KDD14-029 社会事件组织 On social  event organization
[读论文]-KDD15-028 活跃网路中的事件探测 Event  Detection in Activity Networks
[读论文]-KDD15-027 新闻事件和社交网络的动力学 Dynamics  of News Events and Social Media Reaction
[读论文]-KDD15-026 异构信息网络挖掘活跃边为中心的多标签分类 Activity-edge Centric Multi-label Classification for  Mining Heterogeneous Information Networks
[读论文]-KDD15-25 异构社交媒体图事件探测和预测的非参数扫描 Non-parametric  scan statistics for event detection and forecasting in heterogeneous social  media graphs
[读论文]-XX 将Tweets和News结合 丰富社交网络短文的框架 Linking  Tweets to News: A Framework to Enrich Short Text Data in Social Media
[读论文]-KDD15-024 在异构信息网络上歧视性地扩散的概率主题模 ProbabilisticTopic Models with Biased Propagation on Heterogeneous Information Networks
[读论文]-KDD15-023 从n-gram重新构建文本文档 Reconstructing Textual Documents from n-grams
[读论文]-KDD15-022 时空事件预测的多任务学习 Multi-Task Learning for Spatio-Temporal Event Forecasting
[读论文]-KDD15-021 社会事件动因的识别与建模 Why It Happened: Identifying and Modeling the Reasons of the Happening of Social Events
[读论文]-KDD15-020 本地事件推荐的集合的贝叶斯泊松分解模型 A Collective Bayesian Poisson Factorization Model for Cold-start Local Event Recommendation
[读论文]-KDD15-019 贝叶斯泊松张量分解 Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from
[读论文]-KDD15-018 考虑“劫持话题”的实时 Top-R Twitter话题 Real-Time Top-R Topic Detection on Twitter with Topic Hijack Filtering
[读论文]-KDD15-017 使用Apache Spark来处理大规模分布式数据 Large Scale Distributed Data Science using Apache Spark
[读论文]-KDD15-016 一个加速动态卷聚类兼容性的提纯策略 Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy
[读论文]-KDD15-015 工作市场雇佣模型的用户聚类 Client Clustering for Hiring Modeling in Work Marketplaces
[读论文]-KDD15-014 基于co-clustering 的货物价格优化 Co-Clustering based Dual Prediction for Cargo Pricing Optimization
[读论文]-KDD15-013 话题轨迹模式挖掘 TOPTRAC: Topical Trajectory Pattern Mining
[读论文]-KDD15-012 综合点中心聚类和边中心聚类 Integrating Vertex-centric Clustering with Edge-centric Clustering for Meta Path Graph Analysis
[读论文]-KDD15-011 有效率的半监督的聚类算法 An Efficient Semi-Supervised Clustering Algorithm with Sequential Constraints
[读论文]-KDD15-010 时光机-为知识库实体产生时间表 TimeMachine: Timeline Generation for Knowledge-Base Entities
[读论文]-KDD15-009 异构信息网络用于文档聚类 Incorporating World Knowledge to Document Clustering via Heterogeneous Information Networks
[读论文]-KDD15-008 同时对脏数据进行聚类和清洗 Turn Waste into Wealth On Simultaneous Clustering and Cleaning over Dirty Data
[读论文]-KDD15-007 实体识别和类别判定 ClusType: Effective Entity Recognition and Typing by Relation Phrase-Based Clustering
[读论文]-KDD15-006 灵活和鲁棒的多网络聚类 Flexible and Robust Multi-Network Clustering
[读论文]-KDD15-005 整体聚类的谱方法 Spectral Ensemble Clustering
[读论文]-KDD15-004 非彻底的、有重叠的聚类 Non-exhaustive, Overlapping Clustering via Low-Rank Semidefinite Programming
[读论文]-KDD15-003 凸优化 Network Lasso: Clustering and Optimization in Large Graphs
[读论文]-KDD15-002 指代消解 A Clustering-Based Framework to Control Block Sizes for Entity Resolution
[读论文]-KDD15-001 文本聚类 Dirichlet-Hawkes Processes with Applications to Clustering Continuous-Time Document Streams

李沐 博士这五年 https://zhuanlan.zhihu.com/p/25099638 Rachel-Zhang http://blog.csdn.net/abcjennifer/article/details/8036018 csdn 第49名 Inside_Zhang http://blog.csdn.net/lanchunhui/article/details/51277608 csdn 第78名

Chungtow-Leo MapReduce实现大矩阵乘法 http://blog.csdn.net/xyilu/article/details/9066973

https://github.com/warmheartli https://github.com/warmheartli/MachineLearningCourse https://github.com/warmheartli/lspider https://github.com/warmheartli/shareditor.com https://github.com/warmheartli/FullStackDeveloperCourse https://github.com/warmheartli/ChatBotCourse 自己动手做聊天机器人教程

GarfieldEr007 http://my.csdn.net/GarfieldEr007

冯超 猿辅导研究团队

张开旭 博士论文题目是《使用压缩表示的中文分词词性标注研究》 技能专长: Machine Learning Natural Language Processing Linux Python Java C/C++ Deep Learning Hadoop

博客 火光摇曳Flickering http://www.flickering.cn/

腾讯 Rickjin

莫烦 python 多线程 skicit-learn tkinter GUI numpy pandas tensorflow np.random.rand(100).astype(np.float32) session tf.constant matmul placeholder

@南大周志华 微博 http://weibo.com/zhouzh2012 @陈天奇怪 微博 http://homes.cs.washington.edu/~tqchen @小土刀 微博 http://weibo.com/wdxtub @winsty 微博 http://winsty.net @王威廉 微博 http://www.cs.cmu.edu/~yww/ @phunter_lau 微博 http://weibo.com/phunterlau @西瓜大丸子汤 微博 http://weibo.com/xiguadawanzitang @杨静lillian 微博 http://weibo.com/bayang @刘知远THU 微博 http://weibo.com/zibuyu9 @Copper_PKU 微博 http://weibo.com/u/1758509357 @包云岗 微博 http://weibo.com/baoyungang

@老师木 微博 http://weibo.com/dr4x @星空下的巫师 微博 http://weibo.com/138147022 @52nlp 微博 http://weibo.com/52nlp @JerryLead 微博 http://weibo.com/jerrylead @学生古 微博 http://weibo.com/truth4sex @龙星镖局 微博 http://weibo.com/1830516311/ 邓力 深度 | 微软人工智能首席科学家邓力：深度强化学习如何助力聊天机器人 张俊林 微博 CSDN http://blog.csdn.net/malefactor

杨军 知乎 https://www.zhihu.com/people/yang-jun-14/activities

曾国藩 修身治国平天下。事业高于修身。 儒家一般要求：对自己道德要求高，对别人道德要求也高。例如，海瑞。 儒家更高追求：对自己道德要求高，对别人不要求。内圣外王，利用一切可以利用的力量，做一番事业。例如，曾国藩。 如果一个人合理地追求自己利益，这是正常的，可以理解。如果过于压榨别人利益，不可原谅。 实事求是 不占他人便宜，不损自己利益。 不可强行施加自己的意志。 人好美名，他人亦是。如果自己美名过甚，他人难堪。 不给他人难堪。 考虑到别人的面子、利益。 不直接拒绝 儒家标准太高，普通大众达不到。 儒家+实事求是 对人感情丰富的人，对人伤害越大。对人感情稀缺的人，对人没有伤害。

李纪为 http://web.stanford.edu/~jiweil/ Hey!! Thanks for stopping by. I am a second-year Ph.D. student in the Computer Science Department at Stanford University, where I am affiliated with the Stanford NLP group. I work with Prof. Dan Jurafsky and Prof. Eduard Hovy, also closely with Prof. Alan Ritter from OSU and the NLP group at Microsoft Research. My research deals with Natural Lanuguage Processing and Machine Learning, with a focus on deep learning. I received B.S. in Biology from Peking University in 2012. Neural Net Models for Open-Domain Discourse Coherence Deep Reinforcement Learning for Dialogue Generation https://github.com/jiweil/Visualizing-and-Understanding-Neural-Models-in-NLP Do Multi-Sense Embeddings Improve Natural Language Understanding? Recursive Deep Models for Discourse Parsing

When Are Tree Structures Necessary for Deep Learning of Representations?
    在需要长距离的任务中，tree-based 递归较好，其它情况，无太好表现。
Deep Learning
Scocial Networks
Deceptive Opinion Spam

Andrew NG E:\文理科_计算机\理论_机器学习_课程_Andrew NG视频教程 170个视频 每天20个视频 新书《Machine Learning Yearning》

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