2019年机器学习框架回顾

The State of Machine Learning Frameworks in 2019

In 2019, the war for ML frameworks has two remaining main contenders: PyTorch and TensorFlow. My analysis suggests that researchers are abandoning TensorFlow and flocking to PyTorch in droves. Meanwhile in industry, Tensorflow is currently the platform of choice, but that may not be true for long.

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When I started out, I had a strong quantitative background (chemical engineering undergrad, was taking PhD courses in chemical engineering) and some functional skills in programming. From there, I first dove deep into one type of machine learning (Gaussian processes) along with general ML practice (how to set up ML experiments in order to evaluate your models) because that was what I needed for my project. I learned mostly online and by reading papers, but I also took one class on data analysis for biologists that wasn’t ML-focused but did cover programming and statistical thinking. Later, I took a linear algebra class, an ML survey class, and an advanced topics class on structured learning at Caltech. Those helped me obtain a broad knowledge of ML, and then I’ve gained deeper understandings of some subfields that interest me or are especially relevant by reading papers closely (chasing down references and anything I don’t understand and/or implementing the core algorithms myself).

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最优化 · 链路预测 · Integration · MoDELS · CLUSTER ·

2019 年 5 月 31 日

End to end learning and optimization on graphs

Bryan Wilder,Eric Ewing,Bistra Dilkina,Milind Tambe

Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. We propose an approach to integrate a differentiable proxy for common graph optimization problems into training of machine learning models for tasks such as link prediction. This allows the model to focus specifically on the downstream task that its predictions will be used for. Experimental results show that our end-to-end system obtains better performance on example optimization tasks than can be obtained by combining state of the art link prediction methods with expert-designed graph optimization algorithms.

BERT · GitHub · TensorFlow · 机器学习 · PyTorch ·

2018 年 12 月 21 日

盘点 | 2018年度机器学习开源项目及框架（附链接）

THU数据派

清华大数据产业联合会(数据派)成员秉承“自强不息、行胜于言”,以扎实的理工功底闯荡“数据江湖”,独树一帜.本号定期组织线下活动,独家发布干货内容,分享前沿产业动态.在数据派,一切基于数据说话!

带你浏览排名靠前的顶级AI开源项目，聊聊今年年机器学习都有哪些发展及展望。

BERT · GitHub · TensorFlow · PyTorch · Google DeepMind ·

2018 年 12 月 19 日

2018年度盘点：机器学习开源项目及框架（附链接）

新智元

智能+中国主平台,致力于推动中国从互联网+迈向智能+新纪元.重点关注人工智能、机器人等前沿领域发展,关注人机融合、人工智能和机器人革命对人类社会与文明进化的影响,领航中国新智能时代.

先来看看Mybridge AI 中排名靠前的顶级开源项目

BERT · GitHub · TensorFlow · PyTorch · 机器学习 ·

2018 年 12 月 17 日

年度大盘点：机器学习开源项目及框架

云栖社区

云栖社区是由阿里云负责运营、阿里巴巴技术协会和阿里巴巴集团各技术团队提供内容支持的开放式技术社区。

2018年马上就要结束了，我们来回顾一下过去的这一年中，机器学习领域有哪些有趣的事情吧！

GRU · RNN · Performance · MoDELS · 再缩放 ·

2018 年 10 月 30 日

利用动态深度学习预测金融时间序列基于Python

量化投资与机器学习

介绍关于量化投资和机器学习方面的知识.通过文摘,报告,论坛,博文,代码,还有编辑部成员自己研究的领域等方式,给大家提供无偿的知识干粮.

good！

泛化理论 · Networking · 深度学习 · AI · Neural Networks ·

2018 年 10 月 17 日

Relational inductive biases, deep learning, and graph networks

Peter W. Battaglia,Jessica B. Hamrick,Victor Bapst,Alvaro Sanchez-Gonzalez,Vinicius Zambaldi,Mateusz Malinowski,Andrea Tacchetti,David Raposo,Adam Santoro,Ryan Faulkner,Caglar Gulcehre,Francis Song,Andrew Ballard,Justin Gilmer,George Dahl,Ashish Vaswani,Kelsey Allen,Charles Nash,Victoria Langston,Chris Dyer,Nicolas Heess,Daan Wierstra,Pushmeet Kohli,Matt Botvinick,Oriol Vinyals,Yujia Li,Razvan Pascanu

Artificial intelligence (AI) has undergone a renaissance recently, making major progress in key domains such as vision, language, control, and decision-making. This has been due, in part, to cheap data and cheap compute resources, which have fit the natural strengths of deep learning. However, many defining characteristics of human intelligence, which developed under much different pressures, remain out of reach for current approaches. In particular, generalizing beyond one's experiences--a hallmark of human intelligence from infancy--remains a formidable challenge for modern AI. The following is part position paper, part review, and part unification. We argue that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective. Just as biology uses nature and nurture cooperatively, we reject the false choice between "hand-engineering" and "end-to-end" learning, and instead advocate for an approach which benefits from their complementary strengths. We explore how using relational inductive biases within deep learning architectures can facilitate learning about entities, relations, and rules for composing them. We present a new building block for the AI toolkit with a strong relational inductive bias--the graph network--which generalizes and extends various approaches for neural networks that operate on graphs, and provides a straightforward interface for manipulating structured knowledge and producing structured behaviors. We discuss how graph networks can support relational reasoning and combinatorial generalization, laying the foundation for more sophisticated, interpretable, and flexible patterns of reasoning. As a companion to this paper, we have released an open-source software library for building graph networks, with demonstrations of how to use them in practice.

迁移学习 · Bioinformatics · 数据获取 · Neural Networks · 注意力机制 ·

2018 年 8 月 6 日

A Survey on Deep Transfer Learning

Chuanqi Tan,Fuchun Sun,Tao Kong,Wenchang Zhang,Chao Yang,Chunfang Liu

from arxiv, The 27th International Conference on Artificial Neural Networks (ICANN 2018)

As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.