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Found 10 papers, 8 papers with code
Complex-Valued Convolutional Gated Recurrent Neural Network for Ultrasound Beamforming
The complex-valued network operations proposed in this study improve the beamforming accuracy of complex-valued ultrasound signals over traditional real-valued methods.
Mapping Network-Coordinated Stacked Gated Recurrent Units for Turbulence Prediction
In our experiments, MSU extracts one point from a velocity field containing 121 points and utilizes this point to accurately predict 100 pressure points on the cylinder.
Semi-Instruct: Bridging Natural-Instruct and Self-Instruct for Code Large Language Models
Presently, two dominant paradigms for collecting tuning data are natural-instruct (human-written) and self-instruct (automatically generated).
MultiPoT: Multilingual Program of Thoughts Harnesses Multiple Programming Languages
In this paper, we conduct comprehensive experiments on the programming languages used in PoT and find that no single language consistently delivers optimal performance across all tasks and models.
Dendritic Learning-incorporated Vision Transformer for Image Recognition
DVT is a groundbreaking Biomimetic Vision Transformer that combines dendritic learning and Vision Transformer architecture, showcasing superior image recognition performance through biologically inspired structures.
Pareto Dominance Archive and Coordinated Selection Strategy-Based Many-Objective Optimizer for Protein Structure Prediction
One of the efficient tools to describe this information is protein energy functions.
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
Our code and dataset are available at https://github. com/zmzhang1998/Real-RawVSR.
Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic
Machine learning approaches have been widely used for discovering the underlying physics of dynamical systems from measured data.
Parsimony-Enhanced Sparse Bayesian Learning for Robust Discovery of Partial Differential Equations
Compared with the conventional Sparse Bayesian Learning (SBL) method, the PeSBL method promotes parsimony of the learned model in addition to its sparsity.
Condition Assessment of Stay Cables through Enhanced Time Series Classification Using a Deep Learning Approach
A deep learning model, long short term memory fully convolutional network (LSTM-FCN), is leveraged by assigning appropriate inputs and representative class labels for the TSC problem, First, a TSC classifier is trained and validated using the data collected under intact conditions of stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels.
