This problem appeared as an assignment in the online coursera course Convolution Neural Networks by Prof Andrew Ng, (deeplearing.ai). The description of the problem is taken straightway from the assignment. Most of the algorithms we've studied optimize a cost function to get a set of parameter values. In Neural Style Transfer, we shall optimize a cost function to get pixel values! Neural Style Transfer (NST) is one of the most fun techniques in deep learning.

In recent years, the number of applications to the UTCS Ph.D. program has become too large to manage with a traditional review process. GRADE uses historical admissions data to predict how likely the committee is to admit each new applicant. It reports each prediction as a score similar to those used by human reviewers, and accompanies each by an explanation of what applicant features most influenced its prediction. GRADE makes the review process more efficient by enabling reviewers to spend most of their time on applicants near the decision boundary and by focusing their attention on parts of each applicant's file that matter the most. An evaluation over two seasons of Ph.D. admissions indicates that the system leads to dramatic time savings, reducing the total time spent on reviews by at least 74 percent.

The team's software was able to find, recognize, and retrieve all six items used in the preliminary round. Because there was no other competitor for the final round, it was turned into a demonstration with four items found and retrieved. NOMAD is a PENTIUM computer with a hard drive.

This article gives an overview of current research on animated pedagogical agents at the Center for Advanced Research in Technology for Education (CARTE) at the University of Southern California/Information Sciences Institute. Animated pedagogical agents, nicknamed guidebots, interact with learners to help keep learning activities on track. They combine the pedagogical expertise of intelligent tutoring systems with the interpersonal interaction capabilities of embodied conversational characters. They can support the acquisition of team skills as well as skills performed alone by individuals. At CARTE, we have been developing guidebots that help learners acquire a variety of problem-solving skills in virtual worlds, in multimedia environments, and on the web.

Parametric tests are only valid if the data satisfy certain assumptions. If these assumptions hold, they will, however, typically give more accurate results. The analysis of statistical learning theory has very much the flavor of a nonparametric statistical test. The weakness of pac, therefore, is that its results must hold true even in worst-case distributions. There is, however, a new twist to this story in that the more recent pacstyle results are able to take account of observed attributes of the function that has been chosen by the learner, for example, its margin on the training set.

Robotics Team 1 from Kansas State University was the team that perfectly completed the Office Navigation event in the shortest time at the fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. The team, consisting of Michael Novak and Darrel Fossett, developed its code in an undergraduate softwareengineering course. The team, consisting of Michael Novak and Darrel Fossett, accomplished the complete Office Navigation event perfectly. In both the second and the final rounds, the software achieved the maximum points for successfully completing the event without hitting obstacles, hitting walls, incorrectly estimating the time for the meeting, or failing to enter rooms. The time for completion of the task was less than one-third the time of the only other team to perfectly complete the task.

R B. Abhyankar Emphasizing theory and implementation issues more than specific applications and Prolog programming techniques, Computing with Logic Logic Programming with Prolog (The Benjamin Cummings Publishing Company, Menlo Park, Calif., 1988, 535 pp., $27 95) by David Maier and David S. Warren, respected researchers in logic programming, is a superb book Offering an in-depth treatment of advanced topics, the book also includes the necessary background material on logic and automatic theorem proving, making it self-contained. The only real prerequisite is a first course in data structures, although it would be helpful if the reader has also had a first course in program translation. The book has a wealth of exercises and would make an excellent textbook for advanced undergraduate or graduate students in computer science; it is also appropriate for programmers interested in the implementation of Prolog The book presents the concepts of logic programming using theory presentation, implementation, and application of Proplog, Datalog, and Prolog, three logic programming languages of increasing complexity that are based on horn clause subsets of propositional, predicate, and functional logic, respectively This incremental approach, unique to this book, is effective in conveying a thorough understanding of the subject The book consists of 12 chapters grouped into three parts (Part 1 chapters 1 to 3, Part 2. chapters 4 to 6, and Part 3 chapters 7 to 12), an appendix, and an index The three parts, each dealing with one of these logic programming languages, are organized the same First, the authors informally present the language using examples; an interpreter is also presented. Then the formal syntax and semantics for the language and logic are presented, along with soundness and completeness results for the logic and the effects of various search strategies Next, they give optimization techniques for the interpreter Each chapter ends with exercises, brief comments regarding the material in the chapter, and a bibliography Chapter I presents top-down and bottom-up interpreters for Proplog Chapter 2 offers a good discussion of the related notions: negation as failure, closed-world assumption, minimal models, and stratified programs Chapter 3 considers clause indexing and lazy concatenation as optimization techniques for the Proplog interpreter in chapter 1 Chapter 4 explains the connection between Datalog and relational algebra. Chapter 5 contains a proof of Herbrand's theorem for predicate logic.

Part of the Media Laboratory's heritage (its origins are in the School of Architecture) is a startling receptivity to the arts, especially music and the visual arts, and Brand repeatedly returns to this subject. Even here, intellectualism reigns: It is symptomatic that the lab members' interest in literature seems to be limited to science fiction. This lopsidedness echoes Turkle's complaint that hackers ignore the texture (emotion) of music in favor of its structure (intellect). Not an engineer himself, Brand is not always in a position to critically evaluate what he saw; I was reminded of persons who, on seeing ELIZA, concluded that computerized psychotherapy was just around the corner. As Brand points out, the Media Lab replaces the publish-orperish imperative with demo or die, and anyone who has produced a demo knows something about practical mendacity.

To be considered exceptional, a textbook must satisfy three basic requirements. First, it must be authoritative, written by one with a broad range of experience in, and knowledge of, a subject. Second, it must effectively communicate to the reader, in the same manner in which a course instructor must be capable of imparting knowledge to students in a classroom. Third, it must stimulate the reader into thinking more deeply about the subject and into viewing it from fresh perspectives. In Artificial Intelligence: A Knowledge-Based Approach (Boyd & Fraser, Boston, 740 pp., $48.95), author Morris W. Firebaugh has succeeded in meeting each of these requirements.

Is reading Herb Simon's delightful autobiography worth boarding the wrong commuter train? Written in an informal style, Models of My Life presents a lively and insightful self-portrait of this father of AI. In keeping with his character, Simon uses the metaphor of the maze to describe his life: "In describing my life as mazelike, I do not mean that I have made a large number of deliberate, wrenching decisions to go off in one direction or another. On the contrary, I have made very few. Obvious responses to opportunities and circumstances, rather than studied decisions, have put me on the particular roads I have followed" (pp.