[PDF] Top 20 Learning with Noise-Contrastive Estimation: Easing training by learning to scale
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Learning with Noise-Contrastive Estimation: Easing training by learning to scale
... Abstract Noise-Contrastive Estimation (NCE) is a learning criterion that is regularly used to train neural language models in place of Maximum Likelihood Estimation, since it ... Voir le document complet
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An experimental analysis of Noise-Contrastive Estimation: the noise distribution matters
... difficulty to train neural language mod- els with NCE for large vocabularies, this paper aimed to get a better understanding of its mech- anisms and ...of noise samples and the effort we need ... Voir le document complet
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Deep Inference for Covariance Estimation: Learning Gaussian Noise Models for State Estimation
... original training dataset with the first 400 samples of the TUM dataset, and tuned the pre-trained network with the augmented dataset (for comparison purposes, we also augment the CELLO ... Voir le document complet
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Learning to sample from noise with deep generative models
... infusion training procedure on MNIST [ 17 ], Toronto Face Database [ 34 ], CIFAR-10 [ 14 ], and CelebA [ 21 ...was to scale the integer pixel values down to range ...layers with 1200 ... Voir le document complet
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Learning to plan with uncertain topological maps
... agent to navigate in 3D environments using a hierarchical strategy including a high-level graph based planner and a local ...driven learning based approach for planning under uncertainty in topological ... Voir le document complet
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Large-scale semi-supervised learning with online spectral graph sparsification
... impossible to store the full similarity matrix in memory. To this end we employ efficient online spectral graph sparsification tech- niques (Kelner & Levin, 2013) to incrementally process the ... Voir le document complet
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Learning brain regions via large-scale online structured sparse dictionary-learning
... Models compared and metrics. We compared our proposed Smooth-SODL model (2) against both the Canonical ICA –CanICA [23], a single-batch multi-subject PCA/ICA-based method, and the standard SODL (sparse online ... Voir le document complet
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Model Averaging in Large Scale Learning
... theory to high-dimensional ...come with the increasing number of data gathered by any connected object that can collect thousands to millions of different ...comparison with classical ... Voir le document complet
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Building efficient algorithms by learning to compress
... seek to map vectors in R 𝐽 to 𝐵-dimensional Hamming space, typically with 𝐵 < 𝐽 ...used to compute Hamming distances between eight byte vectors in as little as three ... Voir le document complet
152
A deep learning approach to state estimation from videos
... and training sets. The highest accuracy achieved by the CNN model is around ...84.27% with an average error of ...obtained by training the model on the old ...the training ... Voir le document complet
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Biologically-plausible learning algorithms can scale to large datasets
... CALE TO L ARGE D ATASETS A BSTRACT The backpropagation (BP) algorithm is often thought to be biologically implau- sible in the ...pathways. To address this “weight transport problem” (Grossberg, ... Voir le document complet
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Learning to run a power network challenge for training topology controllers
... out by early work ...state-of-the-art to control optimally such grid topology “at scale”, beyond the level of “branch switching” [5] ...[9] to discover steady-state tactical solutions ... Voir le document complet
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Biologically-Plausible Learning Algorithms Can Scale to Large Datasets
... need to have opposite directions but consistent ...brain with two additional yet plausible conditions: First, the feedforward and feed- back pathways must be specifically wired in this antiparallel ... Voir le document complet
9
Extensive deep neural networks for transferring small scale learning to large scale systems
... used to accurately learn the mapping from atomic coordinates to ...simulation? To investigate this we per- formed a Metropolis Monte Carlo (MC) simulation of pristine graphene, the same ...EDNN ... Voir le document complet
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Efficient Seismic fragility curve estimation by Active Learning on Support Vector Machines
... active learning gives a much better classifier than the standard practice of using a single parameter (usually the ...SVM with only 4 variables has initially the best performance on average, up to ... Voir le document complet
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Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection
... the training examples and the classes they belong ...them by 8 times downsampling in size to save time and memory resource during ...the training data is kept for validation. All data are ... Voir le document complet
6
The value of learning analytics to networked learning on a personal learning environment
... of learning event is called a „connectivist‟ course and is based on four major types of activity: 1) Aggregation: access to a wide variety of resources to read, watch or play, along with a ... Voir le document complet
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Learning by mirror averaging
... (1.2), with K min 1≤j≤M A(e j ) where K > 1, instead of min 1≤j≤M A(e j ) in ...and with a remainder term which is sometimes larger than the optimal one ...close to 1. However, the inequalities ... Voir le document complet
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Training Set Class Distribution Analysis for Deep Learning Model - Application to Cancer Detection
... done to facilitate computer-aided diagnosis for metastasis detection from ...machine learning techniques [13]. Studies on utilizing deep learning on this topic are comparatively few and ...[14]. ... Voir le document complet
7
Regional Learning Dynamics and Systems of Education and Training
... employee learning and regional education and training systems In this section I explore the impact of differences in regional education and training systems on the likelihood of the different forms ... Voir le document complet
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