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ExploringtheLimitsofTransferLearningwithaUnified Text-to ...

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A Neural Probabilistic Language Model

jmlr.org

Journal of Machine Learning Research 3 (2003) 1137–1155 Submitted 4/02; Published 2/03 A Neural Probabilistic Language Model Yoshua Bengio BENGIOY@IRO.UMONTREAL.

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Journal of Mac hine Learning Researc h 1 (2001) 211{244 ...

jmlr.org

tro duction In sup ervise d le arning w e are giv en a set of examples input v ectors f x n g N n =1 along with corresp onding targets f t n g N n =1, the latter of whic h migh be real v alues (in r e gr ession) or class lab els (classi c ation). F rom this `training' set w e wish to learn a mo del of the dep endency of the targets on the ...

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Statistical Comparisons of Classifiers over Multiple Data Sets

jmlr.org

Introduction Over the last years, the machine learning community has become increasingly aware of the need for ... friendly graphs. In Section 4 we shall provide some empirical insights into the properties of the tests. 2. Previous Work ... random resampling 11 29 44 32 54 separate subset 5 11 0 13 9 Score function [%] classification accuracy ...

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Scikit-learn: Machine Learning in Python

jmlr.org

Cython: a language for combining C in Python. Cython makes it easy to reach the performance of compiled languages with Python-like syntax and high-level operations. It is also used to bind compiled libraries, eliminating the boilerplate code of Python/C extensions. 4. Code Design Objects specified by interface, not by inheritance.

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Latent Dirichlet Allocation

jmlr.org

LATENT DIRICHLET ALLOCATION This line of thinking leads to the latent Dirichlet allocation (LDA) model that we present in the current paper. It is important to emphasize that an assumption of exchangeability is not equivalent to an as-

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A Kernel Two-Sample Test

jmlr.org

Kolmogorov-Smirnov and Earth-Mover’s distances, which are based ondifferent function classes; collectively these are known as integral probability metrics (Muller, 1997). On a more practical¨ note, the MMD has a reasonable computational cost, when compared with …

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Visualizing Data using t-SNE

jmlr.org

VISUALIZING DATA USING T-SNE 2. Stochastic Neighbor Embedding Stochastic Neighbor Embedding (SNE) starts by converting the high-dimensional Euclidean dis-tances between datapoints into conditional probabilities that represent similarities.1 The similarity of datapoint xj to datapoint xi is the conditional probability, pjji, that xi would pick xj as its neighbor

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Latent Dirichlet Allocation

jmlr.org

discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over …

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Dropout: A Simple Way to Prevent Neural Networks from …

jmlr.org

probability pduring training, the outgoing weights of that unit are multiplied by pat test time as shown in Figure 2. This ensures that for any hidden unit the expected output (under the distribution used to drop units at training time) is the same as the actual output at test time.

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A Neural Probabilistic Language Model - Journal of Machine ...

jmlr.org

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 …

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