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Results 1 - 10
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608
A fast learning algorithm for deep belief nets
- Neural Computation
, 2006
"... We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer at a ..."
Abstract
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Cited by 970 (49 self)
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We show how to use “complementary priors ” to eliminate the explaining away effects that make inference difficult in densely-connected belief nets that have many hidden layers. Using complementary priors, we derive a fast, greedy algorithm that can learn deep, directed belief networks one layer
Biased assimilation and attitude polarization: The effects of prior theories on subsequently considered evidence
- Journal of Personality and Social Psychology
, 1979
"... People who hold strong opinions on complex social issues are likely to examine relevant empirical evidence in a biased manner. They are apt to accept "confirming" evidence at face value while subjecting "discontinuing " evidence to critical evaluation, and as a result to draw und ..."
Abstract
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Cited by 477 (8 self)
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punishment rated those results and procedures that confirmed their own beliefs to be the more convincing and probative ones, and they reported corresponding shifts in their beliefs as the various results and procedures were presented. The net effect of such evaluations
Learning Bayesian belief networks: An approach based on the MDL principle
- Computational Intelligence
, 1994
"... A new approach for learning Bayesian belief networks from raw data is presented. The approach is based on Rissanen's Minimal Description Length (MDL) principle, which is particularly well suited for this task. Our approach does not require any prior assumptions about the distribution being lear ..."
Abstract
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Cited by 254 (7 self)
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learned. In particular, our method can learn unrestricted multiply-connected belief networks. Furthermore, unlike other approaches our method allows us to tradeo accuracy and complexity in the learned model. This is important since if the learned model is very complex (highly connected) it can
Sparse deep belief net model for visual area V2
- Advances in Neural Information Processing Systems 20
, 2008
"... Abstract 1 Motivated in part by the hierarchical organization of the neocortex, a number of recently proposed algorithms have tried to learn hierarchical, or “deep, ” structure from unlabeled data. While several authors have formally or informally compared their algorithms to computations performed ..."
Abstract
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Cited by 164 (19 self)
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some intriguing hypotheses about V2 computations. 1 This thesis is an extended version of an earlier paper by Honglak Lee, Chaitanya Ekanadham, and Andrew Ng titled “Sparse deep belief net model for visual area V2.” 1
Learning Deep Architectures for AI
"... Theoretical results suggest that in order to learn the kind of complicated functions that can represent highlevel abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as i ..."
Abstract
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Cited by 183 (30 self)
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, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable
in densely connected systems
, 2005
"... PACS. 89.70.+c – Information theory and communication theory. PACS. 75.10.Nr – Spin-glass and other random models. PACS. 64.60.Cn – Order-disorder transformations; statistical mechanics of model systems. Abstract. – An improved inference method for densely connected systems is presented. The approac ..."
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PACS. 89.70.+c – Information theory and communication theory. PACS. 75.10.Nr – Spin-glass and other random models. PACS. 64.60.Cn – Order-disorder transformations; statistical mechanics of model systems. Abstract. – An improved inference method for densely connected systems is presented
3-d object recognition with deep belief nets
- Advances in Neural Information Processing Systems 22
, 2009
"... We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB d ..."
Abstract
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Cited by 63 (8 self)
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We introduce a new type of top-level model for Deep Belief Nets and evaluate it on a 3D object recognition task. The top-level model is a third-order Boltzmann machine, trained using a hybrid algorithm that combines both generative and discriminative gradients. Performance is evaluated on the NORB
Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers
- In Proceedings of the Eighteenth Annual National Conference on Artificial Intelligence (AAAI-02
, 2002
"... Abstract. Bayesian belief nets (BNs) are often used for classification tasks — typically to return the most likely class label for each specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function — viz., likelihood, rather than classification a ..."
Abstract
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Cited by 76 (8 self)
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Abstract. Bayesian belief nets (BNs) are often used for classification tasks — typically to return the most likely class label for each specified instance. Many BN-learners, however, attempt to find the BN that maximizes a different objective function — viz., likelihood, rather than classification
Pseudo Prior Belief Propagation for Densely Connected Discrete Graphs
"... Abstract—This paper proposes a new algorithm for the linear least squares problem where the unknown variables are con-strained to be in a finite set. The factor graph that corresponds to this problem is very loopy; in fact, it is a complete graph. Hence, applying the Belief Propagation (BP) algorith ..."
Abstract
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Cited by 3 (2 self)
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information on each variable. Next we integrate this information into a loopy Belief Propagation (BP) algorithm as a pseudo prior. We show that, unlike current paradigms, the Belief Propagation (BP) algorithm can be ad-vantageous even for dense graphs with many short loops. The performance of the proposed
Results 1 - 10
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608
