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Found 10 papers, 3 papers with code
PMNN:Physical Model-driven Neural Network for solving time-fractional differential equations
It establishes a temporal iteration scheme based on physical model-driven neural networks which effectively combines deep neural networks (DNNs) with interpolation approximation of fractional derivatives.
HNS: An Efficient Hermite Neural Solver for Solving Time-Fractional Partial Differential Equations
However, we have discovered that neural network solvers based on L1 interpolation approximation are unable to fully exploit the benefits of neural networks, and the accuracy of these models is constrained to interpolation errors.
The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
In this paper, we summarize the winning solutions from the RoboDepth Challenge -- an academic competition designed to facilitate and advance robust OoD depth estimation.
EmotionGesture: Audio-Driven Diverse Emotional Co-Speech 3D Gesture Generation
In this work, we propose EmotionGesture, a novel framework for synthesizing vivid and diverse emotional co-speech 3D gestures from audio.
Community Detection Using Revised Medoid-Shift Based on KNN
During the process of finding the next medoid, the RMS algorithm is based on a neighborhood defined by KNN, while the original Medoid-Shift is based on a neighborhood defined by a distance parameter.
Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM
Cryo-electron microscopy (cryo-EM) has become a major experimental technique to determine the structures of large protein complexes and molecular assemblies, as evidenced by the 2017 Nobel Prize.
Semi-Supervised Learning for In-Game Expert-Level Music-to-Dance Translation
Previous works of this topic consider music-to-dance as a supervised motion generation problem based on time-series data.
A Brand-level Ranking System with the Customized Attention-GRU Model
In e-commerce websites like Taobao, brand is playing a more important role in influencing users' decision of click/purchase, partly because users are now attaching more importance to the quality of products and brand is an indicator of quality.
DeepSF: deep convolutional neural network for mapping protein sequences to folds
Almost all traditional fold recognition methods use sequence (homology) comparison to indirectly predict the fold of a tar get protein based on the fold of a template protein with known structure, which cannot explain the relationship between sequence and fold.
DeepQA: Improving the estimation of single protein model quality with deep belief networks
The deep belief network is trained on several large datasets consisting of models from the Critical Assessment of Protein Structure Prediction (CASP) experiments, several publicly available datasets, and models generated by our in-house ab initio method.
