Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

According to W. Edwards Deming, American companies require nothing less than a transformation of management style and of governmental relations with industry. In Out of the Crisis, originally published in 1982, Deming offers a theory of management based on his famous 14 Points for Management. Management's failure to plan for the future, he claims, brings about loss of market, which brings about loss of jobs. Management must be judged not only by the quarterly dividend, but by innovative plans to stay in business, protect investment, ensure future dividends, and provide more jobs through improved product and service. In simple, direct language, he explains the principles of management transformation and how to apply them.

Out of the Crisis

The concept of an agent has become important in both Artificial Intelligence (AI) and mainstream computer science. Our aim in this paper is to point the reader at what we perceive to be the most important theoretical and practical issues associated with the design and construction of intelligent agents. For convenience, we divide these issues into three areas (though as the reader will see, the divisions are at times somewhat arbitrary). Agent theory is concerned with the question of what an agent is, and the use of mathematical formalisms for representing and reasoning about the properties of agents. Agent architectures can be thought of as software engineering models of agents;researchers in this area are primarily concerned with the problem of designing software or hardware systems that will satisfy the properties specified by agent theorists. Finally, agent languages are software systems for programming and experimenting with agents; these languages may embody principles proposed by theorists. The paper is not intended to serve as a tutorial introduction to all the issues mentioned; we hope instead simply to identify the most important issues, and point to work that elaborates on them. The article includes a short review of current and potential applications of agent technology.

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Intelligent Agents: Theory and Practice

物件導向軟體之架構(Object-Oriented Software Construction)探討

From the Publisher: 
The accessible presentation of this book gives both a general view of the entire computer vision enterprise and also offers sufficient detail to be able to build useful applications. Users learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods. A CD-ROM with every copy of the text contains source code for programming practice, color images, and illustrative movies. Comprehensive and up-to-date, this book includes essential topics that either reflect practical significance or are of theoretical importance. Topics are discussed in substantial and increasing depth. Application surveys describe numerous important application areas such as image based rendering and digital libraries. Many important algorithms broken down and illustrated in pseudo code. Appropriate for use by engineers as a comprehensive reference to the computer vision enterprise.

Computer vision : a modern approach = 计算机视觉 : 一种现代的方法

On Algorithms and Complexity for Sets with Cardinality Constraints.

Efficient Software Transactions for Object-Oriented Languages

We study the problem of inferring communication structures that can solve cooperative multi-agent planning problems while minimizing the amount of communication. We quantify the amount of communication as the maximum degree of the communication graph; this metric captures settings where agents have limited bandwidth. Minimizing communication is challenging due to the combinatorial nature of both the decision space and the objective; for instance, we cannot solve this problem by training neural networks using gradient descent. We propose a novel algorithm that synthesizes a control policy that combines a programmatic communication policy used to generate the communication graph with a transformer policy network used to choose actions. Our algorithm first trains the transformer policy, which implicitly generates a "soft" communication graph; then, it synthesizes a programmatic communication policy that "hardens" this graph, forming a neurosymbolic transformer. Our experiments demonstrate how our approach can synthesize policies that generate low-degree communication graphs while maintaining near-optimal performance.

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Neurosymbolic Transformers for Multi-Agent Communication

Transferring data between integrated circuits accounts for a growing proportion of system power in wearable and mobile systems. The dynamic component of power dissipated in this data transfer can be reduced by reducing signal transitions. Techniques for reducing signal transitions on communication links have traditionally been targeted at parallel buses and can therefore not be applied when the transfer interfaces are serial buses. In this article, we address the issue of the best-case effectiveness of techniques to reduce signal transitions on serial buses, if these techniques also allow some error in the numeric interpretation of transmitted data. For many embedded applications, exchanging numeric accuracy for power reduction is a worthwhile tradeoff. We present a study of the efficiency of these value-deviation-bounded approximate serial data encoders (VDBS data encoders) and proofs of their properties. The bounds and proofs we present yield new insights into the best possible tradeoffs between dynamic power reduction and approximation error that can be achieved in practice. The insights are important regardless of whether actual practical VDBS data encoders are implemented in software or in hardware.

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Efficiency Limits for Value-Deviation-Bounded Approximate Communication

As shared-memory multiprocessors become the dominant commodity source of computation, parallelizing compilers must support mainstream computations that manipulate irregular, pointer-based data structures such as lists, trees and graphs, Our experience with a parallelizing compiler for this class of applications shows that their synchronization requirements differ significantly from those of traditional parallel computations. Instead of coarse-grain barrier synchronization, irregular computations require synchronization primitives that support efficient fine-grain atomic operations.The standard implementation mechanism for atomic operations uses mutual exclusion locks. But the overhead of acquiring and releasing locks can reduce the performance. Locks can also consume significant amourtts of memory. Optimistic synchronization primitives such as load linked/stor conditional are an attractive alternative. They require no additional memory and eliminate the use of heavyweight blocking synchronization constructs.This paper presents our experience using optimistic synchronization to implement fine-grain atomic operations in the context of a parallelizing compiler for irregular object-based programs. We have implemented two versions of the compiler. One version generates code that uses mutual exclusion locks to make operations execute atomically. The other version uses optimistic synchronization. This paper presents the first published algorithm that enables compilers to automatically generate optimistically synchronized parallel code. The presented experimental results indicate that optimistic synchronization is clearly the superior choice for our set of applications. Our results show that it can significantly reduce the memory consumption and improve the overall performance.

Martin Rinard

Papers

On Algorithms and Complexity for Sets with Cardinality Constraints.

Efficient Software Transactions for Object-Oriented Languages

Neurosymbolic Transformers for Multi-Agent Communication

Efficiency Limits for Value-Deviation-Bounded Approximate Communication

Effective fine-grain synchronization for automatically parallelized programs using optimistic synchronization primitives