[PDF] Top 20 Generalisation error in learning with random features and the hidden manifold model
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Generalisation error in learning with random features and the hidden manifold model
... of the generalisation formula ...classification with logistic loss Among the surprising observations in modern machine learning is the fact that one can use ... Voir le document complet
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A Random Matrix Analysis of Learning with α-Dropout
... Conclusion and Discussion Leveraging on random matrix theory, we have analyzed the effect of the α-Dropout layer on a one layer neural network, which allowed us to have a deeper understanding ... Voir le document complet
10
Generalisation dynamics of online learning in over-parameterised neural networks
... stellar generalisation on a variety of problems, de- spite often being large enough to easily fit all their training ...study the generalisation dynamics of two-layer neural networks in a ... Voir le document complet
26
A Bayesian Non-Parametric Hidden Markov Random Model for Hemodynamic Brain Parcellation
... estimated in the VEM algorithm. The estimated parcellations obtained by the two JPDE versions are shown in ...comparison with the ground truth allows one to conclude that ... Voir le document complet
32
Learning spatiotemporal trajectories from manifold-valued longitudinal data
... mixed-effects model to learn typical scenarios of changes from longitudinal manifold-valued data, namely repeated measurements of the same objects or individuals at several points in ...time. ... Voir le document complet
10
A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation
... estimated in the VEM algorithm. The estimated parcellations obtained by the two JPDE versions are shown in ...comparison with the ground truth allows one to conclude that ... Voir le document complet
16
Modelling the influence of data structure on learning in neural networks: the hidden manifold model
... problems: the MNIST task, where one aims to discriminate odd from even digits in the MNIST data set; and the vanilla teacher-student ...setup. In this setup, inputs are drawn as ... Voir le document complet
44
Statistical mechanics of the spherical hierarchical model with random fields
... from the spherical model, it is commonly not possible to derive, within the non-mean-field region, analytical expressions for the critical exponents in the Ising counterpart of ... Voir le document complet
24
The random waypoint mobility model with uniform node spatial distribution
... defined in [11] for a wider class of mobility models, including RWP. While the “perfect” simulation methodology allows initializing a mobile net- work directly in its stationary conditions, ... Voir le document complet
23
Effects of Hebbian learning on the dynamics and structure of random networks with inhibitory and excitatory neurons
... consequences The former sections dealt with the effects of Hebbian learning on the structure and dynamics of the ...on the network function. The basic ... Voir le document complet
37
A dynamic contagion risk model with recovery features
... course, the configuration model is a graph where cycles become rare in the asymptotic limit, and therefore one has to be cautious about the scope its applicability to financial ... Voir le document complet
36
Some applications of compressed sensing in computational mechanics: model order reduction, manifold learning, data-driven applications and nonlinear dimensionality reduction
... make the LASSO algorithm efficient. Parameters employed in the solution of ...compiled in Table 1 . The first equation to be tested is Eq. ( 10 ). The global sys- tem of ... Voir le document complet
14
Multimodality Imaging Population Analysis using Manifold Learning
... on the image pixels intensities (Wolz et al. 2009), as we propose in this ...to the maximum of variance (Principal Com- ponent Analysis (PCA), kernel Principal Component Analysis (kPCA)), entropy ... Voir le document complet
7
Instanton calculus for the self-avoiding manifold model
... as the determinant of a non-local kernel operator in d- dimensional space, and derive the normalization factor for the large-order asymptotics ...completely the UV divergences of ... Voir le document complet
120
Activated Aging Dynamics and Effective Trap Model Description in the Random Energy Model
... stated the trap-like properties in terms of basins and not ...discussed in the previous sections a basin typically con- tains just one ...example the aging function considering ... Voir le document complet
27
Hidden Structure and Function in the Lexicon
... of the hidden structure of ...of the dictionary) from which all the words in the dictionary can be ...But the Kernel is not the smallest number of words that can ... Voir le document complet
11
Model-Free Reinforcement Learning with Continuous Action in Practice
... for the average of two independent runs, with each datapoint showing the binned average of twenty ...shown in Figures 2a–c, AC - S converged on a policy for its initial environment (first 200 ... Voir le document complet
7
Hyperspectral image unmixing using manifold learning: methods derivations and comparative tests
... INTRODUCTION In hyperspectral imagery, a pixel is a mixture of spectral com- ponents associated with a number of pure materials present in the ...scene. In order to reveal underlying ... Voir le document complet
5
Detection error exponent for spatially dependent samples in random networks
... supported in part by collaborative participation in Communications and Networks Consortium sponsored by ...DAAD19-01-2-0011 and by Army Research Office under Grant ARO-W911NF-06-1-0346. ... Voir le document complet
6
Neural Network Information Leakage through Hidden Learning
... In this work, we investigate what may be regarded as the most natural strategy to achieve the aforementioned goal of the attacker, by considering a simple scheme that trains a network for two ... Voir le document complet
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