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Found 9 papers, 5 papers with code
Exponential weight averaging as damped harmonic motion
The exponential moving average (EMA) is a commonly used statistic for providing stable estimates of stochastic quantities in deep learning optimization.
Anomaly Detection with Variance Stabilized Density Estimation
Then, we design a variance stabilized density estimation problem for maximizing the likelihood of the observed samples while minimizing the variance of the density around normal samples.
Autoregressive Generative Modeling with Noise Conditional Maximum Likelihood Estimation
We introduce a simple modification to the standard maximum likelihood estimation (MLE) framework.
Neural Inverse Transform Sampler
Any explicit functional representation $f$ of a density is hampered by two main obstacles when we wish to use it as a generative model: designing $f$ so that sampling is fast, and estimating $Z = \int f$ so that $Z^{-1}f$ integrates to 1.
Support Recovery with Stochastic Gates: Theory and Application for Linear Models
Consider the problem of simultaneous estimation and support recovery of the coefficient vector in a linear data model with additive Gaussian noise.
Practical Phase Retrieval: Low-Photon Holography with Untrained Priors
To the best of our knowledge, this is the first work to consider a dataset-free machine learning approach for holographic phase retrieval.
Phase Retrieval with Holography and Untrained Priors: Tackling the Challenges of Low-Photon Nanoscale Imaging
Phase retrieval is the inverse problem of recovering a signal from magnitude-only Fourier measurements, and underlies numerous imaging modalities, such as Coherent Diffraction Imaging (CDI).
Variational Diffusion Autoencoders with Random Walk Sampling
Variational autoencoders (VAEs) and generative adversarial networks (GANs) enjoy an intuitive connection to manifold learning: in training the decoder/generator is optimized to approximate a homeomorphism between the data distribution and the sampling space.
SpectralNet: Spectral Clustering using Deep Neural Networks
Moreover, the map learned by SpectralNet naturally generalizes the spectral embedding to unseen data points.
