We investigate using gradient descent methods for learning ranking functions; we propose a simple probabilistic cost function, and we introduce RankNet, an implementation of these ideas using a neural network to model the underlying ranking function. We present test results on toy data and on data from a commercial internet search engine. 1.

Manually querying search engines in order to accumulate a large bodyof factual information is a tedious, error-prone process of piecemealsearch. Search engines retrieve and rank potentially relevantdocuments for human perusal, but do not extract facts, assessconfidence, or fuse information from multiple documents. This paperintroduces KnowItAll, a system that aims to automate the tedious process ofextracting large collections of facts from the web in an autonomous,domain-independent, and scalable manner.The paper describes preliminary experiments in which an instance of KnowItAll, running for four days on a single machine, was able to automatically extract 54,753 facts. KnowItAll associates a probability with each fact enabling it to trade off precision and recall. The paper analyzes KnowItAll's architecture and reports on lessons learned for the design of large-scale information extraction systems.

This paper presents a new timing driven force directed placement algorithm that meets physical net length constraints as well as constraints on specific pin sets. It is the first force directed placement algorithm that meets precise half perimeter bounding box constraints on critical nets. It builds on the work of Eisenmann et al. [12], adding a new net model that changes the contribution of constrained nets in the quadratic programming problem, during solving for each force generation step. We propose several methods for selecting and constraining critical nets to achieve improved timing. Our work suggests that the force directed method with net constraints is a powerful tool for placement and timing convergence, achieving an average worst negative slack optimization exploitation of 64% and average total negative slack optimization exploitation of 48% results on 16 industry circuits from a 1.5GHz microprocessor.