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Browsing CBCL Memos (1993 - 2004) by Title

Research and Teaching Output of the MIT Community

Browsing CBCL Memos (1993 - 2004) by Title

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  • Cohn, David A.; Ghahramani, Zoubin; Jordan, Michael I. (1995-03-21)
    For many types of learners one can compute the statistically 'optimal' way to select data. We review how these techniques have been used with feedforward neural networks. We then show how the same principles may be ...
  • Alvira, Mariano; Paris, Jim; Rifkin, Ryan (2001-07-01)
    We design and implement a system that recommends musicians to listeners. The basic idea is to keep track of what artists a user listens to, to find other users with similar tastes, and to recommend other artists that these ...
  • Poggio, Tomaso; Rifkin, Ryan; Mukherjee, Sayan; Rakhlin, Alex (2002-03-01)
    Intuitively, we expect that averaging --- or bagging --- different regressors with low correlation should smooth their behavior and be somewhat similar to regularization. In this note we make this intuition precise. ...
  • Weiss, Yair (1997-11-01)
    Local belief propagation rules of the sort proposed by Pearl(1988) are guaranteed to converge to the optimal beliefs for singly connected networks. Recently, a number of researchers have empirically demonstrated good ...
  • Yu, Angela J.; Giese, Martin A.; Poggio, Tomaso A. (2001-09-01)
    Object recognition in the visual cortex is based on a hierarchical architecture, in which specialized brain regions along the ventral pathway extract object features of increasing levels of complexity, accompanied by greater ...
  • Giese, Martin Alexander; Poggio, Tomaso (2002-08-01)
    The visual recognition of complex movements and actions is crucial for communication and survival in many species. Remarkable sensitivity and robustness of biological motion perception have been demonstrated in ...
  • Louie, Jennifer (2003-06-01)
    Previous biological models of object recognition in cortex have been evaluated using idealized scenes and have hard-coded features, such as the HMAX model by Riesenhuber and Poggio [10]. Because HMAX uses the same set ...
  • Knoblich, Ulf; Freedman, David J.; Riesenhuber, Maximilian (2002-04-18)
    In a recent experiment, Freedman et al. recorded from inferotemporal (IT) and prefrontal cortices (PFC) of monkeys performing a "cat/dog" categorization task (Freedman 2001 and Freedman, Riesenhuber, Poggio, Miller ...
  • Schoelkopf, B.; Sung, K.; Burges, C.; Girosi, F.; Niyogi, P.; Poggio, T.; Vapnik, V. (1996-12-01)
    The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special ...
  • Morgenstern, Christian; Heisele, Bernd (2003-11-28)
    We present a component-based approach for recognizing objects under large pose changes. From a set of training images of a given object we extract a large number of components which are clustered based on the similarity ...
  • Riesenhuber, Maximilian; Poggio, Tomaso (2000-08-07)
    Understanding how biological visual systems perform object recognition is one of the ultimate goals in computational neuroscience. Among the biological models of recognition the main distinctions are between feedforward ...
  • Schyns, Philippe G.; Bulthoff, Heinrich H. (1993-08-01)
    Poggio and Vetter (1992) showed that learning one view of a bilaterally symmetric object could be sufficient for its recognition, if this view allows the computation of a symmetric, "virtual," view. Faces are roughly ...
  • Torralba, Antonio; Sinha, Pawan (2001-09-01)
    There is general consensus that context can be a rich source of information about an object's identity, location and scale. In fact, the structure of many real-world scenes is governed by strong configurational rules akin ...
  • Poggio, M.; Poggio, T. (1995-04-11)
    We have simulated the behavior of several artificial flies, interacting visually with each other. Each fly is described by a simple tracking system (Poggio and Reichardt, 1973; Land and Collett, 1974) which summarizes ...
  • Schneider, Robert; Riesenhuber, Maximilian (2002-08-01)
    The HMAX model has recently been proposed by Riesenhuber & Poggio as a hierarchical model of position- and size-invariant object recognition in visual cortex. It has also turned out to model successfully a number of ...
  • Torralba, Antonio; Sinha, Pawan (2001-11-05)
    The ability to detect faces in images is of critical ecological significance. It is a pre-requisite for other important face perception tasks such as person identification, gender classification and affect analysis. Here ...
  • Shimizu, Hiroaki; Poggio, Tomaso (2003-08-27)
    The capability of estimating the walking direction of people would be useful in many applications such as those involving autonomous cars and robots. We introduce an approach for estimating the walking direction of people ...
  • Balas, Benjamin J.; Sinha, Pawan (2003-08-13)
    A fundamental question in visual neuroscience is how to represent image structure. The most common representational schemes rely on differential operators that compare adjacent image regions. While well-suited to encoding ...
  • Niyogi, Partha; Berwick, Robert (1995-12-01)
    Formalizing linguists' intuitions of language change as a dynamical system, we quantify the time course of language change including sudden vs. gradual changes in languages. We apply the computer model to the historical ...
  • Chan, Nicholas Tung; Shelton, Christian (2001-04-17)
    This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. ...
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