Results 1  10
of
103
Think Globally, Fit Locally: Unsupervised Learning of Low Dimensional Manifolds
 Journal of Machine Learning Research
, 2003
"... The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation. ..."
Abstract

Cited by 252 (8 self)
 Add to MetaCart
The problem of dimensionality reduction arises in many fields of information processing, including machine learning, data compression, scientific visualization, pattern recognition, and neural computation.
A Neural Probabilistic Language Model
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2003
"... A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen ..."
Abstract

Cited by 145 (12 self)
 Add to MetaCart
A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on ngrams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on stateoftheart ngram models, and that the proposed approach allows to take advantage of longer contexts.
Torch: A Modular Machine Learning Software Library
, 2002
"... Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches. However, these machine learning algorithms are often complex to implement and to use pro ..."
Abstract

Cited by 108 (20 self)
 Add to MetaCart
Many scientific communities have expressed a growing interest in machine learning algorithms recently, mainly due to the generally good results they provide, compared to traditional statistical or AI approaches. However, these machine learning algorithms are often complex to implement and to use properly and efficiently. We thus present in this paper a new machine learning software library in which most stateoftheart algorithms have already been implemented and are available in a unified framework, in order for scientists to be able to use them, compare them, and even extend them. More interestingly, this library is freely available under a BSD license and can be retrieved on the web by everyone.
Covariate shift adaptation by importance weighted cross validation
, 2000
"... A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapo ..."
Abstract

Cited by 71 (39 self)
 Add to MetaCart
A common assumption in supervised learning is that the input points in the training set follow the same probability distribution as the input points that will be given in the future test phase. However, this assumption is not satisfied, for example, when the outside of the training region is extrapolated. The situation where the training input points and test input points follow different distributions while the conditional distribution of output values given input points is unchanged is called the covariate shift. Under the covariate shift, standard model selection techniques such as cross validation do not work as desired since its unbiasedness is no longer maintained. In this paper, we propose a new method called importance weighted cross validation (IWCV), for which we prove its unbiasedness even under the covariate shift. The IWCV procedure is the only one that can be applied for unbiased classification under covariate shift, whereas alternatives to IWCV exist for regression. The usefulness of our proposed method is illustrated by simulations, and furthermore demonstrated in the braincomputer interface, where strong nonstationarity effects can be seen between training and test sessions. c2000 Masashi Sugiyama, Matthias Krauledat, and KlausRobert Müller.
Large scale transductive svms
 JMLR
"... We show how the ConcaveConvex Procedure can be applied to Transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is a ..."
Abstract

Cited by 62 (5 self)
 Add to MetaCart
We show how the ConcaveConvex Procedure can be applied to Transductive SVMs, which traditionally require solving a combinatorial search problem. This provides for the first time a highly scalable algorithm in the nonlinear case. Detailed experiments verify the utility of our approach. Software is available at
Trading convexity for scalability
 ICML06, 23rd International Conference on Machine Learning
, 2006
"... Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how nonconvexity can provide scalability advantages over convexity. We show h ..."
Abstract

Cited by 51 (3 self)
 Add to MetaCart
Convex learning algorithms, such as Support Vector Machines (SVMs), are often seen as highly desirable because they offer strong practical properties and are amenable to theoretical analysis. However, in this work we show how nonconvexity can provide scalability advantages over convexity. We show how concaveconvex programming can be applied to produce (i) faster SVMs where training errors are no longer support vectors, and (ii) much faster Transductive SVMs. 1.
A tutorial on energybased learning
 Predicting Structured Data
, 2006
"... EnergyBased Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in ..."
Abstract

Cited by 42 (6 self)
 Add to MetaCart
EnergyBased Models (EBMs) capture dependencies between variables by associating a scalar energy to each configuration of the variables. Inference consists in clamping the value of observed variables and finding configurations of the remaining variables that minimize the energy. Learning consists in finding an energy function in which observed configurations of the variables are given lower energies than unobserved ones. The EBM approach provides a common theoretical framework for many learning models, including traditional discriminative and generative approaches, as well as graphtransformer networks, conditional random fields, maximum margin Markov networks, and several manifold learning methods. Probabilistic models must be properly normalized, which sometimes requires evaluating intractable integrals over the space of all possible variable configurations. Since EBMs have no requirement for proper normalization, this problem is naturally circumvented. EBMs can be viewed as a form of nonprobabilistic factor graphs, and they provide considerably more flexibility in the design of architectures and training criteria than probabilistic approaches. 1
Improving the Rprop Learning Algorithm
 PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON NEURAL COMPUTATION (NC 2000)
, 2000
"... The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing firstorder learning methods for neural networks. We introduce modifications of the algorithm that improve its learning speed. The resulting speedup is experimentally shown for a set of neural network learning tasks a ..."
Abstract

Cited by 41 (7 self)
 Add to MetaCart
The Rprop algorithm proposed by Riedmiller and Braun is one of the best performing firstorder learning methods for neural networks. We introduce modifications of the algorithm that improve its learning speed. The resulting speedup is experimentally shown for a set of neural network learning tasks as well as for artificial error surfaces.
Natural language processing (almost) from scratch. arXiv:1103.0398v1
, 2011
"... We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including partofspeech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid taskspecific eng ..."
Abstract

Cited by 39 (6 self)
 Add to MetaCart
We propose a unified neural network architecture and learning algorithm that can be applied to various natural language processing tasks including partofspeech tagging, chunking, named entity recognition, and semantic role labeling. This versatility is achieved by trying to avoid taskspecific engineering and therefore disregarding a lot of prior knowledge. Instead of exploiting manmade input features carefully optimized for each task, our system learns internal representations on the basis of vast amounts of mostly unlabeled training data. This work is then used as a basis for building a freely available tagging system with good performance and minimal computational requirements.