Sökt på: Serie neural information processing series
totalt 10 träffar
Learning Machine Translation
The Internet gives us access to a wealth of information in languages we don't understand. The investigation of automated or semi-automated approaches to translation has become a …
Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different …
Dataset Shift in Machine Learning
An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different …
Practical Applications of Sparse Modeling
Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision. Sparse …
Optimization for Machine Learning
An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities.The interplay between optimization and …
Log-Linear Models, Extensions, and Applications
Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Log-linear models play a key role in …
Introduction to Lifted Probabilistic Inference
Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the …
Probabilistic Models of the Brain
A survey of probabilistic approaches to modeling and understanding brain function.Neurophysiological, neuroanatomical, and brain imaging studies have helped to shed light on how …
Advances in Large-Margin Classifiers
The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and …
Perturbations, Optimization, and Statistics
A description of perturbation-based methods developed in machine learning to augment novel optimization methods with strong statistical guarantees. In nearly all machine learning, …