人工智能资料库：第68辑（20170916）

最新推荐文章于 2025-11-26 15:49:07 发布

原创最新推荐文章于 2025-11-26 15:49:07 发布 · 343 阅读

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作者：chen_h
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微信公众号：coderpai

1.【博客】Heart Disease Diagnosis with Deep Learning

简介：

A human heart is an astounding machine that is designed to continually function for up to a century without failure. One of the key ways to measure how well your heart is functioning is to compute its ejection fraction: after your heart relaxes at its diastole to fully fill with blood, what percentage does it pump out upon contracting to its systole? The first step of getting at this metric relies on segmenting (delineating the area of) the ventricles from cardiac images.

During my time at the Insight AI Program in NYC, I decided to tackle the right ventricle segmentation challenge from the calls for research hosted by the AI Open Network. I managed to achieve state of the art results with over an order of magnitude less parameters; below is a brief account of how.

原文链接：https://blog.insightdatascience.com/heart-disease-diagnosis-with-deep-learning-c2d92c27e730

2.【博客】Learning to Model Other Minds

简介：

LOLA, a collaboration by researchers at OpenAI and the University of Oxford, lets an RL agent take account of the learning of others when updating its own strategy. Each LOLA agent adjusts its policy in order to shape the learning of the other agents in a way that is advantageous. This is possible since the learning of the other agents depends on the rewards and observations occurring in the environment, which in turn can be influenced by the agent.

This means that the LOLA agent, ‘Alice’, models how the parameter updates of the other agent, ‘Bob’, depend on its own policy and how Bob’s parameter update impacts its own future expected reward. Alice then updates its own policy in order to make the learning step of the other agents, like Bob, more beneficial to its own goals.

原文链接：https://blog.openai.com/learning-to-model-other-minds/

3.【论文】A Tutorial on Deep Learning for Music Information Retrieval

简介：

Following their success in Computer Vision and other areas, deep learning techniques have recently become widely adopted in Music Information Retrieval (MIR) research. However, the majority of works aim to adopt and assess methods that have been shown to be effective in other domains, while there is still a great need for more original research focusing on music primarily and utilising musical knowledge and insight. The goal of this paper is to boost the interest of beginners by providing a comprehensive tutorial and reducing the barriers to entry into deep learning for MIR. We lay out the basic principles and review prominent works in this hard to navigate field. We then outline the network structures that have been successful in MIR problems and facilitate the selection of building blocks for the problems at hand. Finally, guidelines for new tasks and some advanced topics in deep learning are discussed to stimulate new research in this fascinating field.

原文链接：https://arxiv.org/pdf/1709.04396.pdf

4.【博客】Machine Learning for Creativity and Design

简介：

In the last year, generative machine learning and machine creativity have gotten a lot of attention in the non-research world. At the same time there have been significant advances in generative models for media creation and for design. This one-day workshop explores several issues in the domain of generative models for creativity and design. We will look at algorithms for generation and creation of new media and new designs, engaging researchers building the next generation of generative models (GANs, RL, etc) and also from a more information-theoretic view of creativity (compression, entropy, etc). We will investigate the social and cultural impact of these new models, engaging researchers from HCI/UX communities. We’ll also hear from some of the artists and musicians who are adopting machine learning approaches like deep learning and reinforcement learning as part of their artistic process. We’ll leave ample time for discussing both the important technical challenges of generative models for creativity and design, as well as the philosophical and cultural issues that surround this area of research.

The goal of this workshop is to bring together researchers and creative practitioners interested in advancing art and music generation to present new work, foster collaborations and build networks.

原文链接：https://nips2017creativity.github.io/

5.【论文集】Deep Learning Papers Reading Roadmap

简介：

The roadmap is constructed in accordance with the following four guidelines: