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Found 14 papers, 1 papers with code
Towards Robust Waveform-Based Acoustic Models
We study the problem of learning robust acoustic models in adverse environments, characterized by a significant mismatch between training and test conditions.
Shear Induced Orientational Ordering in Active Glass
Using a Fokker-Planck descriptionwe make testable predictions without any fit parameters for the joint distribution of single particleposition and orientation.
Soft Condensed Matter Disordered Systems and Neural Networks Statistical Mechanics
Fragmentation in trader preferences among multiple markets: Market coexistence versus single market dominance
These changes have re-emphasized the importance of understanding the effects of market competition: does proliferation of trading venues and increased competition lead to dominance of a single market or coexistence of multiple markets?
Systematic model reduction captures the dynamics of extrinsic noise in biochemical subnetworks
We consider the general problem of describing the dynamics of subnetworks of larger biochemical reaction networks, e. g. protein interaction networks involving complex formation and dissociation reactions.
Learning Waveform-Based Acoustic Models using Deep Variational Convolutional Neural Networks
We investigate the potential of stochastic neural networks for learning effective waveform-based acoustic models.
Dynamical selection of Nash equilibria using Experience Weighted Attraction Learning: emergence of heterogeneous mixed equilibria
We therefore compare with the results of Experience-Weighted Attraction (EWA) learning, which at long times leads to Nash equilibria in the appropriate limits of large intensity of choice, low noise (long agent memory) and perfect imputation of missing scores (fictitious play).
Phase Diagram of Restricted Boltzmann Machines and Generalised Hopfield Networks with Arbitrary Priors
Restricted Boltzmann Machines are described by the Gibbs measure of a bipartite spin glass, which in turn corresponds to the one of a generalised Hopfield network.
Phase transitions in Restricted Boltzmann Machines with generic priors
We study Generalised Restricted Boltzmann Machines with generic priors for units and weights, interpolating between Boolean and Gaussian variables.
A Subband-Based SVM Front-End for Robust ASR
This work proposes a novel support vector machine (SVM) based robust automatic speech recognition (ASR) front-end that operates on an ensemble of the subband components of high-dimensional acoustic waveforms.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Speech Recognition Front End Without Information Loss
Speech representation and modelling in high-dimensional spaces of acoustic waveforms, or a linear transformation thereof, is investigated with the aim of improving the robustness of automatic speech recognition to additive noise.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Learning curves for multi-task Gaussian process regression
We study the average case performance of multi-task Gaussian process (GP) regression as captured in the learning curve, i. e.\ the average Bayes error for a chosen task versus the total number of examples $n$ for all tasks.
Random walk kernels and learning curves for Gaussian process regression on random graphs
Our method for predicting the learning curves using belief propagation is significantly more accurate than previous approximations and should become exact in the limit of large random graphs.
Exact learning curves for Gaussian process regression on large random graphs
We study learning curves for Gaussian process regression which characterise performance in terms of the Bayes error averaged over datasets of a given size.
Kernels and learning curves for Gaussian process regression on random graphs
The fully correlated limit is reached only once loops become relevant, and we estimate where the crossover to this regime occurs.
