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Task clustering and gating for Bayesian multitask learning
 Journal of Machine Learning Research
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 156 (2 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Factor Graphs and the SumProduct Algorithm
 IEEE TRANSACTIONS ON INFORMATION THEORY
, 1998
"... A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple c ..."
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Cited by 1787 (72 self)
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A factor graph is a bipartite graph that expresses how a "global" function of many variables factors into a product of "local" functions. Factor graphs subsume many other graphical models including Bayesian networks, Markov random fields, and Tanner graphs. Following one simple
Hierarchically Classifying Documents Using Very Few Words
, 1997
"... The proliferation of topic hierarchies for text documents has resulted in a need for tools that automatically classify new documents within such hierarchies. Existing classification schemes which ignore the hierarchical structure and treat the topics as separate classes are often inadequate in text ..."
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Cited by 521 (8 self)
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classification where the there is a large number of classes and a huge number of relevant features needed to distinguish between them. We propose an approach that utilizes the hierarchical topic structure to decompose the classification task into a set of simpler problems, one at each node in the classification
Detecting faces in images: A survey
 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 2002
"... Images containing faces are essential to intelligent visionbased human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image se ..."
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Cited by 831 (4 self)
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Images containing faces are essential to intelligent visionbased human computer interaction, and research efforts in face processing include face recognition, face tracking, pose estimation, and expression recognition. However, many reported methods assume that the faces in an image or an image sequence have been identified and localized. To build fully automated systems that analyze the information contained in face images, robust and efficient face detection algorithms are required. Given a single image, the goal of face detection is to identify all image regions which contain a face regardless of its threedimensional position, orientation, and the lighting conditions. Such a problem is challenging because faces are nonrigid and have a high degree of variability in size, shape, color, and texture. Numerous techniques have been developed to detect faces in a single image, and the purpose of this paper is to categorize and evaluate these algorithms. We also discuss relevant issues such as data collection, evaluation metrics, and benchmarking. After analyzing these algorithms and identifying their limitations, we conclude with several promising directions for future research.
Fusion, Propagation, and Structuring in Belief Networks
 ARTIFICIAL INTELLIGENCE
, 1986
"... Belief networks are directed acyclic graphs in which the nodes represent propositions (or variables), the arcs signify direct dependencies between the linked propositions, and the strengths of these dependencies are quantified by conditional probabilities. A network of this sort can be used to repre ..."
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Cited by 482 (8 self)
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with the task of fusing and propagating the impacts of new information through the networks in such a way that, when equilibrium is reached, each proposition will be assigned a measure of belief consistent with the axioms of probability theory. It is shown that if the network is singly connected (e.g. tree
The Landscape of Parallel Computing Research: A View from Berkeley
 TECHNICAL REPORT, UC BERKELEY
, 2006
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Multitask Learning for Bayesian Neural Networks
"... This thesis introduces a new multitask learning model for Bayesian neural networks based on ideas borrowed from statistics: random regression coefficient models. The output of the model is a combination of a common hidden layer and a task specific hidden layer, one for each task. If the tasks are re ..."
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Cited by 3 (0 self)
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This thesis introduces a new multitask learning model for Bayesian neural networks based on ideas borrowed from statistics: random regression coefficient models. The output of the model is a combination of a common hidden layer and a task specific hidden layer, one for each task. If the tasks
MultiTask Learning for HIV Therapy Screening
"... We address the problem of learning classifiers for a large number of tasks. We derive a solution that produces resampling weights which match the pool of all examples to the target distribution of any given task. Our work is motivated by the problem of predicting the outcome of a therapy attempt for ..."
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Cited by 52 (5 self)
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for a patient who carries an HIV virus with a set of observed genetic properties. Such predictions need to be made for hundreds of possible combinations of drugs, some of which use similar biochemical mechanisms. Multitask learning enables us to make predictions even for drug combinations with few
Nonparametric Bayesian Mixedeffects Models for Multitask Learning
, 2013
"... In many real world problems we are interested in learning multiple tasks while the training set for each task is quite small. When the different tasks are related, one can learn all tasks simultaneously and aim to get improved predictive performance by taking advantage of the common aspects of all t ..."
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tasks. This general idea is known as multitask learning and it has been successfully investigated in several technical settings, with applications in many areas. In this thesis we explore a Bayesian realization of this idea especially using Gaussian Processes (GP) where sharing the prior and its
Results 1  10
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9,759