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Browsing CSAIL Technical Reports (July 1, 2003 - present) by Title

Research and Teaching Output of the MIT Community

Browsing CSAIL Technical Reports (July 1, 2003 - present) by Title

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  • Learning and disrupting invariance in visual recognition 
    Isik, Leyla; Leibo, Joel Z; Poggio, Tomaso (2011-09-10)
    Learning by temporal association rules such as Foldiak's trace rule is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments ...
  • Learning and Invariance in a Family of Hierarchical Kernels 
    Wibisono, Andre; Bouvrie, Jake; Rosasco, Lorenzo; Poggio, Tomaso (2010-07-30)
    Understanding invariance and discrimination properties of hierarchical models is arguably the key to understanding how and why such models, of which the the mammalian visual system is one instance, can lead to good ...
  • Learning by Learning To Communicate 
    Beal, Jacob (2007-08-23)
    Human intelligence is a product of cooperation among many different specialists. Much of this cooperation must be learned, but we do not yet have a mechanism that explains how this might happen for the "high-level" agile ...
  • Learning Commonsense Categorical Knowledge in a Thread Memory System 
    Stamatoiu, Oana L. (2004-05-18)
    If we are to understand how we can build machines capable of broadpurpose learning and reasoning, we must first aim to build systemsthat can represent, acquire, and reason about the kinds of commonsenseknowledge that we ...
  • Learning complex cell invariance from natural videos: A plausibility proof 
    Masquelier, Timothee; Serre, Thomas; Thorpe, Simon; Poggio, Tomaso (2007-12-26)
    One of the most striking feature of the cortex is its ability to wire itself. Understanding how the visual cortex wires up through development and how visual experience refines connections into adulthood is a key question ...
  • Learning From Snapshot Examples 
    Beal, Jacob (2005-04-13)
    Examples are a powerful tool for teaching both humans and computers.In order to learn from examples, however, a student must first extractthe examples from its stream of perception. Snapshot learning is ageneral approach ...
  • Learning Generic Invariances in Object Recognition: Translation and Scale 
    Leibo, Joel Z; Mutch, Jim; Rosasco, Lorenzo; Ullman, Shimon; Poggio, Tomaso (2010-12-30)
    Invariance to various transformations is key to object recognition but existing definitions of invariance are somewhat confusing while discussions of invariance are often confused. In this report, we provide an operational ...
  • Learning Grammatical Models for Object Recognition 
    Aycinena, Meg; Kaelbling, Leslie Pack; Lozano-Perez, Tomas (2008-02-25)
    Many object recognition systems are limited by their inability to share common parts or structure among related object classes. This capability is desirable because it allows information about parts and relationships in ...
  • Learning object segmentation from video data 
    Ross, Michael G.; Kaelbling, Leslie Pack (2003-09-08)
    This memo describes the initial results of a project to create aself-supervised algorithm for learning object segmentation from videodata. Developmental psychology and computational experience havedemonstrated that the ...
  • Learning Semantic Scene Models by Trajectory Analysis 
    Wang, Xiaogang; Tieu, Kinh; Grimson, Eric (2006-02-10)
    In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two ...
  • Learning Solutions of Similar Linear Programming Problems using Boosting Trees 
    Banerjee, Ashis Gopal; Roy, Nicholas (2010-09-18)
    In many optimization problems, similar linear programming (LP) problems occur in the nodes of the branch and bound trees that are used to solve integer (mixed or pure, deterministic or stochastic) programming problems. ...
  • Learning to Trade with Insider Information 
    Das, Sanmay (2005-10-07)
    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 using the Born Rule 
    Wolf, Lior (2006-05-16)
    In Quantum Mechanics the transition from a deterministic descriptionto a probabilistic one is done using a simple rule termed the Bornrule. This rule states that the probability of an outcome ($a$)given a state ($\Psi$) ...
  • Learning with Matrix Factorizations 
    Srebro, Nathan (2004-11-22)
    Matrices that can be factored into a product of two simpler matricescan serve as a useful and often natural model in the analysis oftabulated or high-dimensional data. Models based on matrixfactorization (Factor Analysis, ...
  • Learning with Online Constraints: Shifting Concepts and Active Learning 
    Monteleoni, Claire E. (2006-09-01)
    Many practical problems such as forecasting, real-time decisionmaking, streaming data applications, and resource-constrainedlearning, can be modeled as learning with online constraints. Thisthesis is concerned with analyzing ...
  • The Levels of Understanding framework, revised 
    Poggio, Tomaso (2012-05-31)
    I discuss the "levels of understanding" framework described in Marr's Vision and propose a revised and updated version of it to capture the changes in computation and neuroscience over the last 30 years.
  • Leveraging Player Knowledge in Combinatorial Auctions (and Implementation in Surviving Strategies) 
    Chen, Jing; Micali, Silvio (2008-06-17)
    None
  • Lexical Chains and Sliding Locality Windows in Content-based Text Similarity Detection 
    Nahnsen, Thade; Uzuner, Ozlem; Katz, Boris (2005-05-19)
    We present a system to determine content similarity of documents. More specifically, our goal is to identify book chapters that are translations of the same original chapter; this task requires identification of not only ...
  • LIBPMK: A Pyramid Match Toolkit 
    Lee, John J. (2008-04-07)
    LIBPMK is a C++ implementation of Grauman and Darrell's pyramid match algorithm. This toolkit provides a flexible framework with which developers can quickly match sets of image features and run experiments. LIBPMK provides ...
  • Library Cache Coherence 
    Shim, Keun Sup; Cho, Myong Hyon; Lis, Mieszko; Khan, Omer; Devadas, Srinivas (2011-05-02)
    Directory-based cache coherence is a popular mechanism for chip multiprocessors and multicores. The directory protocol, however, requires multicast for invalidation messages and the collection of acknowledgement messages, ...
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