简介:

传统机器学习:

  • 感知机
  • 逻辑回归
  • 线性回归

多层感知机:

  • dropout多层感知机
  • 归一化与多层感知机
  • 反向传播与多层感知机

卷积神经网络:

  • 基础
  • 全连接层
  • LeNet
  • AlexNet
  • VGG
  • DenseNet
  • ResNet
  • 归一化层

自编码器

GANs

GNNs

RNNs

成为VIP会员查看完整内容
0
35

相关内容

机器学习的一个分支,它基于试图使用包含复杂结构或由多重非线性变换构成的多个处理层对数据进行高层抽象的一系列算法。

知识荟萃

精品入门和进阶教程、论文和代码整理等

更多

查看相关VIP内容、论文、资讯等

Over the past few years, we have seen fundamental breakthroughs in core problems in machine learning, largely driven by advances in deep neural networks. At the same time, the amount of data collected in a wide array of scientific domains is dramatically increasing in both size and complexity. Taken together, this suggests many exciting opportunities for deep learning applications in scientific settings. But a significant challenge to this is simply knowing where to start. The sheer breadth and diversity of different deep learning techniques makes it difficult to determine what scientific problems might be most amenable to these methods, or which specific combination of methods might offer the most promising first approach. In this survey, we focus on addressing this central issue, providing an overview of many widely used deep learning models, spanning visual, sequential and graph structured data, associated tasks and different training methods, along with techniques to use deep learning with less data and better interpret these complex models --- two central considerations for many scientific use cases. We also include overviews of the full design process, implementation tips, and links to a plethora of tutorials, research summaries and open-sourced deep learning pipelines and pretrained models, developed by the community. We hope that this survey will help accelerate the use of deep learning across different scientific domains.

0
26
下载
预览

《Deep Learning》作为深度学习界的圣经,又名“花书”。英文版由全球知名的三位专家Ian Goodfellow、Yoshua Bengio 和Aaron Courville撰写,是深度学习领域奠基性的经典教材,中文版由北京大学教授张志华审校出版。

全书的内容包括3个部分:第1部分介绍基本的数学工具和机器学习的概念,它们是深度学习的预备知识;第2部分系统深入地讲解现今已成熟的深度学习方法和技术;第3部分讨论某些具有前瞻性的方向和想法,它们被公认为是深度学习未来的研究重点。 《深度学习》适合各类读者阅读,包括相关专业的大学生或研究生,以及不具有机器学习或统计背景、但是想要快速补充深度学习知识,以便在实际产品或平台中应用的软件工程师。

中文版链接:https://github.com/yanshengjia/ml-road/blob/master/resources/深度学习.pdf

英文版链接:https://github.com/yanshengjia/ml-road/blob/master/resources/Deep%20Learning.pdf

成为VIP会员查看完整内容
0
141

Meta learning is a promising solution to few-shot learning problems. However, existing meta learning methods are restricted to the scenarios where training and application tasks share the same out-put structure. To obtain a meta model applicable to the tasks with new structures, it is required to collect new training data and repeat the time-consuming meta training procedure. This makes them inefficient or even inapplicable in learning to solve heterogeneous few-shot learning tasks. We thus develop a novel and principled HierarchicalMeta Learning (HML) method. Different from existing methods that only focus on optimizing the adaptability of a meta model to similar tasks, HML also explicitly optimizes its generalizability across heterogeneous tasks. To this end, HML first factorizes a set of similar training tasks into heterogeneous ones and trains the meta model over them at two levels to maximize adaptation and generalization performance respectively. The resultant model can then directly generalize to new tasks. Extensive experiments on few-shot classification and regression problems clearly demonstrate the superiority of HML over fine-tuning and state-of-the-art meta learning approaches in terms of generalization across heterogeneous tasks.

0
6
下载
预览

Deep Learning is applied to energy markets to predict extreme loads observed in energy grids. Forecasting energy loads and prices is challenging due to sharp peaks and troughs that arise due to supply and demand fluctuations from intraday system constraints. We propose deep spatio-temporal models and extreme value theory (EVT) to capture theses effects and in particular the tail behavior of load spikes. Deep LSTM architectures with ReLU and $\tanh$ activation functions can model trends and temporal dependencies while EVT captures highly volatile load spikes above a pre-specified threshold. To illustrate our methodology, we use hourly price and demand data from 4719 nodes of the PJM interconnection, and we construct a deep predictor. We show that DL-EVT outperforms traditional Fourier time series methods, both in-and out-of-sample, by capturing the observed nonlinearities in prices. Finally, we conclude with directions for future research.

0
4
下载
预览

Deep learning has been shown successful in a number of domains, ranging from acoustics, images to natural language processing. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs. Recently, a significant amount of research efforts have been devoted to this area, greatly advancing graph analyzing techniques. In this survey, we comprehensively review different kinds of deep learning methods applied to graphs. We divide existing methods into three main categories: semi-supervised methods including Graph Neural Networks and Graph Convolutional Networks, unsupervised methods including Graph Autoencoders, and recent advancements including Graph Recurrent Neural Networks and Graph Reinforcement Learning. We then provide a comprehensive overview of these methods in a systematic manner following their history of developments. We also analyze the differences of these methods and how to composite different architectures. Finally, we briefly outline their applications and discuss potential future directions.

0
38
下载
预览

Despite deep reinforcement learning has recently achieved great successes, however in multiagent environments, a number of challenges still remain. Multiagent reinforcement learning (MARL) is commonly considered to suffer from the problem of non-stationary environments and exponentially increasing policy space. It would be even more challenging to learn effective policies in circumstances where the rewards are sparse and delayed over long trajectories. In this paper, we study Hierarchical Deep Multiagent Reinforcement Learning (hierarchical deep MARL) in cooperative multiagent problems with sparse and delayed rewards, where efficient multiagent learning methods are desperately needed. We decompose the original MARL problem into hierarchies and investigate how effective policies can be learned hierarchically in synchronous/asynchronous hierarchical MARL frameworks. Several hierarchical deep MARL architectures, i.e., Ind-hDQN, hCom and hQmix, are introduced for different learning paradigms. Moreover, to alleviate the issues of sparse experiences in high-level learning and non-stationarity in multiagent settings, we propose a new experience replay mechanism, named as Augmented Concurrent Experience Replay (ACER). We empirically demonstrate the effects and efficiency of our approaches in several classic Multiagent Trash Collection tasks, as well as in an extremely challenging team sports game, i.e., Fever Basketball Defense.

0
4
下载
预览

Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

0
8
下载
预览

For extracting meaningful topics from texts, their structures should be considered properly. In this paper, we aim to analyze structured time-series documents such as a collection of news articles and a series of scientific papers, wherein topics evolve along time depending on multiple topics in the past and are also related to each other at each time. To this end, we propose a dynamic and static topic model, which simultaneously considers the dynamic structures of the temporal topic evolution and the static structures of the topic hierarchy at each time. We show the results of experiments on collections of scientific papers, in which the proposed method outperformed conventional models. Moreover, we show an example of extracted topic structures, which we found helpful for analyzing research activities.

0
7
下载
预览
小贴士
相关VIP内容
专知会员服务
28+阅读 · 2020年5月9日
专知会员服务
51+阅读 · 2020年2月3日
深度学习界圣经“花书”《Deep Learning》中文版来了
专知会员服务
141+阅读 · 2019年10月26日
相关资讯
深度学习TensorFlow实现集合
专知
9+阅读 · 2018年9月8日
入门 | 深度学习模型的简单优化技巧
机器之心
8+阅读 · 2018年6月10日
深度学习之CNN简介
Python技术博文
14+阅读 · 2018年1月10日
干货 | 深度学习之卷积神经网络(CNN)的模型结构
机器学习算法与Python学习
11+阅读 · 2017年11月1日
【推荐】深度学习目标检测全面综述
机器学习研究会
17+阅读 · 2017年9月13日
相关论文
A Survey of Deep Learning for Scientific Discovery
Maithra Raghu,Eric Schmidt
26+阅读 · 2020年3月26日
OmniNet: A unified architecture for multi-modal multi-task learning
Subhojeet Pramanik,Priyanka Agrawal,Aman Hussain
4+阅读 · 2019年7月17日
Yingtian Zou,Jiashi Feng
6+阅读 · 2019年4月19日
Deep Learning for Energy Markets
Michael Polson,Vadim Sokolov
4+阅读 · 2019年4月10日
Ziwei Zhang,Peng Cui,Wenwu Zhu
38+阅读 · 2018年12月11日
Occupancy Networks: Learning 3D Reconstruction in Function Space
Lars Mescheder,Michael Oechsle,Michael Niemeyer,Sebastian Nowozin,Andreas Geiger
9+阅读 · 2018年12月10日
Hierarchical Deep Multiagent Reinforcement Learning
Hongyao Tang,Jianye Hao,Tangjie Lv,Yingfeng Chen,Zongzhang Zhang,Hangtian Jia,Chunxu Ren,Yan Zheng,Changjie Fan,Li Wang
4+阅读 · 2018年9月25日
Andreas Kamilaris,Francesc X. Prenafeta-Boldu
8+阅读 · 2018年7月31日
Rem Hida,Naoya Takeishi,Takehisa Yairi,Koichi Hori
7+阅读 · 2018年5月6日
Tom Young,Devamanyu Hazarika,Soujanya Poria,Erik Cambria
7+阅读 · 2018年2月20日
Top