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61. Learning Linear, Sparse, Factorial Codes

Olshausen, Bruno A.,

In previous work (Olshausen & Field 1996), an algorithm was described for learning linear sparse codes which, when trained on natural images, produces a set of basis functions that are spatially local...

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62. Learning from Incomplete Data

Ghahramani, Zoubin, Jordan, Michael I.,

Real-world learning tasks often involve high-dimensional data sets with complex patterns of missing features. In this paper we review the problem of learning from incomplete data from two statistical ...

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63. Learning-Based Approach to Estimation of Morphable Model Parameters

Kumar, Vinay, Poggio, Tomaso,

We describe the key role played by partial evaluation in the Supercomputing Toolkit, a parallel computing system for scientific applications that effectively exploits the vast amount of parallelism ex...

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64. Learning-Based Approach to Real Time Tracking and Analysis of Faces

Kumar, Vinay P., Poggio, Tomaso,

This paper describes a trainable system capable of tracking faces and facialsfeatures like eyes and nostrils and estimating basic mouth features such as sdegrees of openness and smile in real time. In...

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65. Measure Fields for Function Approximation

Marroquin, Jose L.,

The computation of a piecewise smooth function that approximates a finite set of data points may be decomposed into two decoupled tasks: first, the computation of the locally smooth models, and hence,...

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66. Model Selection in Summary Evaluation

Perez-Breva, Luis, Yoshimi, Osamu,

A difficulty in the design of automated text summarization algorithms is in the objective evaluation. Viewing summarization as a tradeoff between length and information content, we introduce a techniq...

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67. Model-Based Matching by Linear Combinations of Prototypes

Jones, Michael J., Poggio, Tomaso,

We describe a method for modeling object classes (such as faces) using 2D example images and an algorithm for matching a model to a novel image. The object class models are "learned'' from example ima...

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68. Model-Based Matching of Line Drawings by Linear Combinations of Prototypes

Jones, Michael J., Poggio, Tomaso,

We describe a technique for finding pixelwise correspondences between two images by using models of objects of the same class to guide the search. The object models are 'learned' from example images (...

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69. Modeling Invariances in Inferotemporal Cell Tuning

Riesenhuber, Maximilian, Poggio, Tomaso,

In macaque inferotemporal cortex (IT), neurons have been found to respond selectively to complex shapes while showing broad tuning ("invariance") with respect to stimulus transformations such as trans...

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70. Modeling Stock Order Flows and Learning Market-Making from Data

Kim, Adlar J., Shelton, Christian R.,

Stock markets employ specialized traders, market-makers, designed to provide liquidity and volume to the market by constantly supplying both supply and demand. In this paper, we demonstrate a novel me...

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