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Found 7 papers, 5 papers with code
Deep Learning-Based Least Square Forward-Backward Stochastic Differential Equation Solver for High-Dimensional Derivative Pricing
We propose a new forward-backward stochastic differential equation solver for high-dimensional derivatives pricing problems by combining deep learning solver with least square regression technique widely used in the least square Monte Carlo method for the valuation of American options.
CREPE: A Convolutional Representation for Pitch Estimation
To date, the best performing techniques, such as the pYIN algorithm, are based on a combination of DSP pipelines and heuristics.
Superhuman Accuracy on the SNEMI3D Connectomics Challenge
For the past decade, convolutional networks have been used for 3D reconstruction of neurons from electron microscopic (EM) brain images.
Flood-Filling Networks
State-of-the-art image segmentation algorithms generally consist of at least two successive and distinct computations: a boundary detection process that uses local image information to classify image locations as boundaries between objects, followed by a pixel grouping step such as watershed or connected components that clusters pixels into segments.
Automatic Instrument Recognition in Polyphonic Music Using Convolutional Neural Networks
Traditional methods to tackle many music information retrieval tasks typically follow a two-step architecture: feature engineering followed by a simple learning algorithm.
The Stan Math Library: Reverse-Mode Automatic Differentiation in C++
As computational challenges in optimization and statistical inference grow ever harder, algorithms that utilize derivatives are becoming increasingly more important.
Mathematical Software G.1.0; G.1.3; G.1.4; F.2.1
Combinatorial Energy Learning for Image Segmentation
We introduce a new machine learning approach for image segmentation that uses a neural network to model the conditional energy of a segmentation given an image.
