Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

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Latent dirichlet allocation

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hof-mann's aspect model, also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is generated as a mixture of topics, where the continuous-valued mixture proportions are distributed as a latent Dirichlet random variable. Inference and learning are carried out efficiently via variational algorithms. We present empirical results on applications of this model to problems in text modeling, collaborative filtering, and text classification.

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Latent Dirichlet Allocation

Data Mining: Practical Machine Learning Tools and Techniques offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. *Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects *Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods *Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks-in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Mining: Practical Machine Learning Tools and Techniques

抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

In this paper we present a method ofcomputing the posterior probability ofconditional independence of two or morecontinuous variables from data,examined at several resolutions. Ourapproach is motivated by theobservation that the appearance ofcontinuous data varies widely atvarious resolutions, producing verydifferent independence estimatesbetween the variablesinvolved. Therefore, it is difficultto ascertain independence withoutexamining data at several carefullyselected resolutions. In our paper, weaccomplish this using the exactcomputation of the posteriorprobability of independence, calculatedanalytically given a resolution. Ateach examined resolution, we assume amultinomial distribution with Dirichletpriors for the discretized tableparameters, and compute the posteriorusing Bayesian integration. Acrossresolutions, we use a search procedureto approximate the Bayesian integral ofprobability over an exponential numberof possible histograms. Our methodgeneralizes to an arbitrary numbervariables in a straightforward manner.The test is suitable for Bayesiannetwork learning algorithms that useindependence tests to infer the networkstructure, in domains that contain anymix of continuous, ordinal andcategorical variables.

A Bayesian Multiresolution Independence Test for Continuous Variables

Real-time tracking algorithms often suffer from low accuracy and poor robustness when confronted with difficult, real-world data. We present a tracker that combines 3D shape, color when available, and motion cues to accurately track moving objects in real-time. Our tracker allocates computational effort based on the shape of the posterior distribution. Starting with a coarse approximation to the posterior, the tracker successively refines this distribution, increasing in tracking accuracy over time. The tracker can thus be run for any amount of time, after which the current approximation to the posterior is returned. Even at a minimum runtime of 0.37 ms per object, our method outperforms all of the baseline methods of similar speed by at least 25% in root-mean-square RMS tracking error. If our tracker is allowed to run for longer, the accuracy continues to improve, and it continues to outperform all baseline methods. Our tracker is thus anytime, allowing the speed or accuracy to be optimized based on the needs of the application. By combining 3D shape, color when available, and motion cues in a probabilistic framework, our tracker is able to robustly handle changes in viewpoint, occlusions, and lighting variations for moving objects of a variety of shapes, sizes, and distances.

Robust real-time tracking combining 3D shape, color, and motion

Virtually all existing mobile robot localization techniques operate on a static map of the environment. When the environment changes (e.g., doors are opened or closed), there is an opportunity to simultaneously estimate the robot's pose and the state of the environment. The resulting estimation problem is high-dimensional, rendering current localization techniques inapplicable. This paper proposes an efficient, factored estimation algorithm for mixed discrete-continuous state estimation. Our algorithm integrates particle filters for robot localization, and conditional binary Bayes filters for estimating the dynamic state of the environment. Experimental results illustrate that our algorithm is highly effective in estimating the status of doors, and outperforms a state-of-the-art localizer in dynamic environments.

A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments

The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions However, selecting the best speed is also important - beyond its relationship to stopping distance and lateral maneuverability Consider a high-speed (35 mph) autonomous vehicle driving off-road through challenging desert terrain The vehicle should drive slowly on terrain that poses substantial risk However, it should not dawdle on safe terrain In this paper we address one aspect of risk - shock to the vehicle We present an algorithm for trading-off shock and speed in realtime and without human intervention The trade-off is optimized using supervised learning to match human driving The learning process is essential due to the discontinuous and spatially correlated nature of the control problem - classical techniques do not directly apply We evaluate performance over hundreds of miles of autonomous driving, including performance during the 2005 DARPA Grand Challenge This approach was the deciding factor in our vehicle's speed for nearly 20% of the DARPA competition - more than any other constraint except the DARPA-imposed speed limits - and resulted in the fastest finishing time

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Online speed adaptation using supervised learning for high-speed, off-road autonomous driving

We consider the problem of segmenting and tracking deformable objects in color video with depth (RGBD) data available from commodity sensors such as the Asus Xtion Pro Live or Microsoft Kinect. We frame this problem with very few assumptions-no prior object model, no stationary sensor, and no prior 3-D map-thus making a solution potentially useful for a large number of applications, including semi-supervised learning, 3-D model capture, and object recognition. Our approach makes use of a rich feature set, including local image appearance, depth discontinuities, optical flow, and surface normals to inform the segmentation decision in a conditional random field model. In contrast to previous work in this field, the proposed method learns how to best make use of these features from ground-truth segmented sequences. We provide qualitative and quantitative analyses which demonstrate substantial improvement over the state of the art. This paper is an extended version of our previous work. Building on our previous work, we show that it is possible to achieve an order of magnitude speedup and thus real-time performance ( ~ 20 FPS) on a laptop computer by applying simple algorithmic optimizations to the original work. This speedup comes at only a minor cost in overall accuracy and thus makes this approach applicable to a broader range of tasks. We demonstrate one such task: real-time, online, interactive segmentation to efficiently collect training data for an off-the-shelf object detector.

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Sebastian Thrun

Papers

A Bayesian Multiresolution Independence Test for Continuous Variables

Robust real-time tracking combining 3D shape, color, and motion

A probabilistic technique for simultaneous localization and door state estimation with mobile robots in dynamic environments

Online speed adaptation using supervised learning for high-speed, off-road autonomous driving

Learning to Segment and Track in RGBD