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Found 14 papers, 9 papers with code
Combining Statistical Depth and Fermat Distance for Uncertainty Quantification
The method is applicable to any classification model as it is applied directly in feature space at test time and does not intervene in training process.
On the Nash equilibrium of moment-matching GANs for stationary Gaussian processes
Generative Adversarial Networks (GANs) learn an implicit generative model from data samples through a two-player game.
Generalized Rectifier Wavelet Covariance Models For Texture Synthesis
State-of-the-art maximum entropy models for texture synthesis are built from statistics relying on image representations defined by convolutional neural networks (CNN).
Leveraging Joint-Diagonalization in Transform-Learning NMF
Non-negative matrix factorization with transform learning (TL-NMF) is a recent idea that aims at learning data representations suited to NMF.
Particle gradient descent model for point process generation
This paper presents a statistical model for stationary ergodic point processes, estimated from a single realization observed in a square window.
Data Assimilation Networks
Data assimilation (DA) aims at forecasting the state of a dynamical system by combining a mathematical representation of the system with noisy observations taking into account their uncertainties.
Maximum Entropy Models from Phase Harmonic Covariances
The covariance of a stationary process $X$ is diagonalized by a Fourier transform.
Kymatio: Scattering Transforms in Python
The wavelet scattering transform is an invariant signal representation suitable for many signal processing and machine learning applications.
Statistical learning of geometric characteristics of wireless networks
To approximate (interpolate) the marking function, in our baseline approach, we build a statistical regression model of the marks with respect some local point distance representation.
Phase Harmonic Correlations and Convolutional Neural Networks
For wavelet filters, we show numerically that signals having sparse wavelet coefficients can be recovered from few phase harmonic correlations, which provide a compressive representation
Distributed stochastic optimization for deep learning (thesis)
We also find a surprising connection between the momentum SGD and the EASGD method with a negative moving average rate.
Deep learning with Elastic Averaging SGD
We empirically demonstrate that in the deep learning setting, due to the existence of many local optima, allowing more exploration can lead to the improved performance.
Regularization of Neural Networks using DropConnect
When training with Dropout, a randomly selected subset of activations are set to zero within each layer.
Ranked #6 on
Image Classification
on MNIST
No More Pesky Learning Rates
The performance of stochastic gradient descent (SGD) depends critically on how learning rates are tuned and decreased over time.
