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Found 9 papers, 7 papers with code
Highly accurate protein structure prediction with AlphaFold
Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics.
Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at our disposal for Monte Carlo gradient estimation in machine learning and across the statistical sciences: the problem of computing the gradient of an expectation of a function with respect to parameters defining the distribution that is integrated; the problem of sensitivity analysis.
Variational Autoencoder with Arbitrary Conditioning
We propose a single neural probabilistic model based on variational autoencoder that can be conditioned on an arbitrary subset of observed features and then sample the remaining features in "one shot".
Implicit Reparameterization Gradients
By providing a simple and efficient way of computing low-variance gradients of continuous random variables, the reparameterization trick has become the technique of choice for training a variety of latent variable models.
Tensor Train decomposition on TensorFlow (T3F)
Tensor Train decomposition is used across many branches of machine learning.
Mathematical Software Numerical Analysis
Probabilistic Adaptive Computation Time
We present a probabilistic model with discrete latent variables that control the computation time in deep learning models such as ResNets and LSTMs.
Spatially Adaptive Computation Time for Residual Networks
This paper proposes a deep learning architecture based on Residual Network that dynamically adjusts the number of executed layers for the regions of the image.
PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
We propose a novel approach to reduce the computational cost of evaluation of convolutional neural networks, a factor that has hindered their deployment in low-power devices such as mobile phones.
