Now showing items 21-30 of 275
A new biologically motivated framework for robust object recognition
In this paper, we introduce a novel set of features for robust object recognition, which exhibits outstanding performances on a variety ofobject categories while being capable of learning from only a fewtraining examples. ...
Learning to Trade with Insider Information
This paper introduces algorithms for learning how to trade usinginsider (superior) information in Kyle's model of financial markets.Prior results in finance theory relied on the insider having perfectknowledge of the ...
Learning Object-Independent Modes of Variation with Feature Flow Fields
We present a unifying framework in which "object-independent" modes of variation are learned from continuous-time data such as video sequences. These modes of variation can be used as "generators" to produce a manifold of ...
Learning with Deictic Representation
Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be ...
Corpus-Based Techniques for Word Sense Disambiguation
The need for robust and easily extensible systems for word sense disambiguation coupled with successes in training systems for a variety of tasks using large on-line corpora has led to extensive research into corpus-based ...
Lens Distortion Calibration Using Point Correspondences
This paper describes a new method for lens distortion calibration using only point correspondences in multiple views, without the need to know either the 3D location of the points or the camera locations. The standard ...
Visible Decomposition: Real-Time Path Planning in Large Planar Environments
We describe a method called Visible Decomposition for computing collision-free paths in real time through a planar environment with a large number of obstacles. This method divides space into local visibility graphs, ...
Parallel Function Application on a DNA Substrate
In this paper I present a new model that employs a biological (specifically DNA -based) substrate for performing computation. Specifically, I describe strategies for performing parallel function application in the ...
A Constant-Factor Approximation Algorithm for Embedding Unweighted Graphs into Trees
We present a constant-factor approximation algorithm for computing an embedding of the shortest path metric of an unweighted graph into a tree, that minimizes the multiplicative distortion.
How People Re-find Information When the Web Changes
This paper investigates how people return to information in a dynamic information environment. For example, a person might want to return to Web content via a link encountered earlier on a Web page, only to learn that the ...