Sciweavers

572 search results - page 101 / 115
» A Neural Probabilistic Language Model
Sort
View
CORR
2012
Springer
170views Education» more  CORR 2012»
13 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson

Book
778views
16 years 7 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
ICML
2007
IEEE
15 years 10 months ago
Learning to rank: from pairwise approach to listwise approach
The paper is concerned with learning to rank, which is to construct a model or a function for ranking objects. Learning to rank is useful for document retrieval, collaborative fil...
Zhe Cao, Tao Qin, Tie-Yan Liu, Ming-Feng Tsai, Han...
ICALP
1990
Springer
15 years 1 months ago
Analytic Variations on the Common Subexpression Problem
Any tree can be represented in a max/ma//y compact form as a directed acyclic graph where common subtrees are factored and shared, being represented only once. Such a compaction ca...
Philippe Flajolet, Paolo Sipala, Jean-Marc Steyaer...
ACL
1994
14 years 11 months ago
Similarity-Based Estimation of Word Cooccurrence Probabilities
In many applications of natural language processing it is necessary to determine the likelihood of a given word combination. For example, a speech recognizer may need to determine...
Ido Dagan, Fernando C. N. Pereira, Lillian Lee