A Fast Learning Algorithm for Deep Belief Nets
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. U...
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1,481 publications
Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories
Functionality-specific vulnerabilities, which mainly occur in Application Programming Interfaces (APIs) with specific functionalities, are crucial for software developers to det...
Density Estimation for Statistics and Data Analysis
Introduction. Survey of Existing Methods. The Kernel Method for Univariate Data. The Kernel Method for Multivariate Data. Three Important Methods. Density Estimation in Action.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. T...
Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information
This paper considers the model problem of reconstructing an object from incomplete frequency samples. Consider a discrete-time signal f ∈ C N and a randomly chosen set of freque...
Integrative Analysis of Complex Cancer Genomics and Clinical Profiles Using the cBioPortal
The cBioPortal enables integration, visualization, and analysis of multidimensional cancer genomic and clinical data.
Parallel Distributed Processing
What makes people smarter than computers? These volumes by a pioneering neurocomputing group suggest that the answer lies in the massively parallel architecture of the human min...
Tissue-based map of the human proteome
Protein expression across human tissues Sequencing the human genome gave new insights into human biology and disease. However, the ultimate goal is to understand the dynamic exp...
A new optimizer using particle swarm theory
The optimization of nonlinear functions using particle swarm methodology is described. Implementations of two paradigms are discussed and compared, including a recently develope...