We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

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Going deeper with convolutions

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

Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability. The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

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Reinforcement Learning: An Introduction

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

https://www.cambridge.org/core/services/aop-cambridge-core/content/view/S0140525X0007984X

A non-empiricist perspective on learning in layered networks

We propose a class of first-order gradient-type optimization algorithms to solve structured \textit{filtering-clustering problems}, a class of problems which include trend filtering and $\ell_1$-convex clustering as special cases. Our first main result establishes the linear convergence of deterministic gradient-type algorithms despite the extreme ill-conditioning of the difference operator matrices in these problems. This convergence result is based on a convex-concave saddle point formulation of filtering-clustering problems and the fact that the dual form of the problem admits a global error bound, a result which is based on the celebrated Hoffman bound for the distance between a point and its projection onto an optimal set. The linear convergence rate also holds for stochastic variance reduction gradient-type algorithms. Finally, we present empirical results to show that the algorithms that we analyze perform comparable to state-of-the-art algorithms for trend filtering, while presenting advantages for scalability.

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and &#120001; 1 -Convex Clustering.

Given a multidimensional data set and a model of its density, we consider how to deene the optimal interpolation between two points. This is done by assigning a cost to each path through space, based on two competing goals|one to interpolate through regions of high density, the other to minimize arc length. From this path functional, we derive the Euler-Lagrange equations for extremal motion; given two points, the desired interpolation is found by solving a boundary value problem. We show that this interpolation can be done eeciently, in high dimensions, for Gaussian, Dirichlet, and mixture models.

A variational principle for model-based interpolation

We consider Q-learning with knowledge transfer, using samples from a target reinforcement learning (RL) task as well as source samples from different but related RL tasks. We propose transfer learning algorithms for both batch and online Q-learning with offline source studies. The proposed transferred Q-learning algorithm contains a novel re-targeting step which enables vertical information-cascading along multiple steps in an RL task, besides the usual horizontal information-gathering as transfer learning (TL) for supervised learning. We establish first theoretical justifications of TL in RL tasks by showing a faster rate of convergence of the Q function estimation in the offline RL transfer, and a lower regret bound in the offline-to-online RL transfer under certain similarity assumptions. Empirical evidences from both synthetic and real datasets are presented to backup the proposed algorithm and our theoretical results.

Transferred Q-learning

/pdf/dr-ai-or-how-i-learned-to-stop-worrying-and-love-economics-3ddo506muo.pdf

Michael I. Jordan

Papers

A non-empiricist perspective on learning in layered networks

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁 1 -Convex Clustering.

A variational principle for model-based interpolation

Transferred Q-learning

Dr. AI or: How I Learned to Stop Worrying and Love Economics

Michael I. Jordan

Papers

A non-empiricist perspective on learning in layered networks

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and &#120001; 1 -Convex Clustering.

A variational principle for model-based interpolation

Transferred Q-learning

Dr. AI or: How I Learned to Stop Worrying and Love Economics

Global Error Bounds and Linear Convergence for Gradient-Based Algorithms for Trend Filtering and 𝓁 1 -Convex Clustering.