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Found 19 papers, 2 papers with code
An Analysis Framework for Understanding Deep Neural Networks Based on Network Dynamics
Advancing artificial intelligence demands a deeper understanding of the mechanisms underlying deep learning.
Unsupervised Machine Learning for Detecting and Locating Human-Made Objects in 3D Point Cloud
Experimental results demonstrate that the proposed ground filtering method outperforms previous techniques, and the LIE method successfully distinguishes between points representing trees and human-made objects.
Exploring a Physics-Informed Decision Transformer for Distribution System Restoration: Methodology and Performance Analysis
Driven by advancements in sensing and computing, deep reinforcement learning (DRL)-based methods have demonstrated significant potential in effectively tackling distribution system restoration (DSR) challenges under uncertain operational scenarios.
Methodology and Real-World Applications of Dynamic Uncertain Causality Graph for Clinical Diagnosis with Explainability and Invariance
After verifications, the 46 DUCG models were applied in the real-world in China.
Multi-scale Alternated Attention Transformer for Generalized Stereo Matching
Recent stereo matching networks achieves dramatic performance by introducing epipolar line constraint to limit the matching range of dual-view.
Faithful Question Answering with Monte-Carlo Planning
In this paper, we propose FAME (FAithful question answering with MontE-carlo planning) to answer questions based on faithful reasoning steps.
How and what to learn:The modes of machine learning
We consider weights in pathways that link neurons longitudinally from input neurons to output neurons, or simply weight pathways, as the basic units for understanding a neural network, and decompose a neural network into a series of subnetworks of such weight pathways.
Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimation
Self-supervised learning has shown very promising results for monocular depth estimation.
Inferring Global Dynamics Using a Learning Machine
It is found that following an appropriate training strategy that monotonously decreases the cost function, the learning machine in different training stage can mimic the system at different parameter set.
Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization).
Inferring Global Dynamics of a Black-Box System Using Machine Learning
We present that, instead of establishing the equations of motion, one can model-freely reveal the dynamical properties of a black-box system using a learning machine.
Copy the dynamics using a learning machine
Trained by a set of input-output responses or a segment of time series of a black system, a learning machine can be served as a copy system to mimic the dynamics of various black systems.
A General Theory for Training Learning Machine
The principle of learning, the role of the a prior knowledge, the role of neuron bias, and the basis for choosing neural transfer function and cost function, etc., are still far from clear.
Image Fusion With Cosparse Analysis Operator
In our approach, we formulate the multi-focus image fusion problem in terms of an analysis sparse model, and simultaneously perform the restoration and fusion of multi-focus images.
General Vector Machine
The support vector machine (SVM) is an important class of learning machines for function approach, pattern recognition, and time-serious prediction, etc.
Rough matroids based on coverings
First, we investigate some properties of the definable sets with respect to a covering.
Cost-Sensitive Feature Selection of Data with Errors
In this paper, we study the cost-sensitive feature selection problem on numerical data with measurement errors, test costs and misclassification costs.
Minimal cost feature selection of data with normal distribution measurement errors
In this paper, we consider numerical data with measurement errors and study minimal cost feature selection in this model.
Test-cost-sensitive attribute reduction of data with normal distribution measurement errors
In this paper, we introduce normal distribution measurement errors to covering-based rough set model, and deal with test-cost-sensitive attribute reduction problem in this new model.
