AT&T Labs

About: AT&T Labs is a based out in . It is known for research contribution in the topics: Network packet & The Internet. The organization has 1879 authors who have published 5595 publications receiving 483151 citations.

Advances in Collaborative Filtering

Abstract: The collaborative filtering (CF) approach to recommenders has recently enjoyed much interest and progress. The fact that it played a central role within the recently completed Netflix competition has contributed to its popularity. This chapter surveys the recent progress in the field. Matrix factorization techniques, which became a first choice for implementing CF, are described together with recent innovations. We also describe several extensions that bring competitive accuracy into neighborhood methods, which used to dominate the field. The chapter demonstrates how to utilize temporal models and implicit feedback to extend models accuracy. In passing, we include detailed descriptions of some the central methods developed for tackling the challenge of the Netflix Prize competition.

Referral Web: combining social networks and collaborative filtering

Abstract: Part of the success of social networks can be attributed to the “six degrees of separation’’ phenomena that means the distance between any two individuals in terms of direct personal relationships is relatively small. An equally important factor is there are limits to the amount and kinds of information a person is able or willing to make available to the public at large. For example, an expert in a particular field is almost certainly unable to write down all he knows about the topic, and is likely to be unwilling to make letters of recommendation he or she has written for various people publicly available. Thus, searching for a piece of information in this situation becomes a matter of searching the social network for an expert on the topic together with a chain of personal referrals from the searcher to the expert. The referral chain serves two key functions: It provides a reason for the expert to agree to respond to the requester by making their relationship explicit (for example, they have a mutual collaborator), and it provides a criteria for the searcher to use in evaluating the trustworthiness of the expert. Nonetheless, manually searching for a referral chain can be a frustrating and time-consuming task. One is faced with the trade-off of contacting a large number of individuals at each step, and thus straining both the time and goodwill of the possible respondents, or of contacting a smaller, more focused set, and being more likely to fail to locate an appropriate expert. In response to these problems we are building ReferralWeb, an interactive system for reconstructing, visualizing, and searching social networks on the World-Wide Web. Simulation experiments we ran before we began construction of ReferralWeb showed that automatically generated referrals can be highly

Network function virtualization: Challenges and opportunities for innovations

Abstract: Network function virtualization was recently proposed to improve the flexibility of network service provisioning and reduce the time to market of new services. By leveraging virtualization technologies and commercial off-the-shelf programmable hardware, such as general-purpose servers, storage, and switches, NFV decouples the software implementation of network functions from the underlying hardware. As an emerging technology, NFV brings several challenges to network operators, such as the guarantee of network performance for virtual appliances, their dynamic instantiation and migration, and their efficient placement. In this article, we provide a brief overview of NFV, explain its requirements and architectural framework, present several use cases, and discuss the challenges and future directions in this burgeoning research area.

Multiagent Systems: A Survey from a Machine Learning Perspective

Abstract: Distributed Artificial Intelligence (DAI) has existed as a subfield of AI for less than two decades. DAI is concerned with systems that consist of multiple independent entities that interact in a domain. Traditionally, DAI has been divided into two sub-disciplines: Distributed Problem Solving (DPS) focuses on the information management aspects of systems with several components working together towards a common goals Multiagent Systems (MAS) deals with behavior management in collections of several independent entities, or agents. This survey of MAS is intended to serve as an introduction to the field and as an organizational framework. A series of general multiagent scenarios are presented. For each scenario, the issues that arise are described along with a sampling of the techniques that exist to deal with them. The presented techniques are not exhaustive, but they highlight how multiagent systems can be and have been used to build complex systems. When options exist, the techniques presented are biased towards machine learning approaches. Additional opportunities for applying machine learning to MAS are highlighted and robotic soccer is presented as an appropriate test bed for MAS. This survey does not focus exclusively on robotic systems. However, we believe that much of the prior research in non-robotic MAS is relevant to robotic MAS, and we explicitly discuss several robotic MAS, including all of those presented in this issue.

Finite-state transducers in language and speech processing

Abstract: Finite-machines have been used in various domains of natural language processing. We consider here the use of a type of transducer that supports very efficient programs: sequential transducers. We recall classical theorems and give new ones characterizing sequential string-to-string transducers. Transducers that outpur weights also play an important role in language and speech processing. We give a specific study of string-to-weight transducers, including algorithms for determinizing and minizizing these transducers very efficiently, and characterizations of the transducers admitting determinization and the corresponding algorithms. Some applications of these algorithms in speech recognition are described and illustrated.