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Found 16 papers, 1 papers with code
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
In sub-track 1, the practical runtime performance of the submissions was evaluated, and the corresponding score was used to determine the ranking.
Synergizing Human-AI Agency: A Guide of 23 Heuristics for Service Co-Creation with LLM-Based Agents
This empirical study serves as a primer for interested service providers to determine if and how Large Language Models (LLMs) technology will be integrated for their practitioners and the broader community.
Health Monitoring of Movement Disorder Subject based on Diamond Stacked Sparse Autoencoder Ensemble Model
Machine learning-based processing of data collected from the human with movement disorders using wearable sensors is an effective method currently available for health monitoring.
Overlapping oriented imbalanced ensemble learning method based on projective clustering and stagewise hybrid sampling
Secondly, according to the characteristics of subset classes, a stage-wise hybrid sampling algorithm is designed to realize the de-overlapping and balancing of subsets.
A new Stack Autoencoder: Neighbouring Sample Envelope Embedded Stack Autoencoder Ensemble Model
Second, an embedded stack autoencoder (ESAE) is proposed and trained in each layer of sample space to consider the original samples during training and in the network structure, thereby better finding the relationship between original feature samples and deep feature samples.
Envelope imbalanced ensemble model with deep sample learning and local-global structure consistency
Based on the analysis above, an imbalanced ensemble algorithm with the deep sample pre-envelope network (DSEN) and local-global structure consistency mechanism (LGSCM) is proposed here to solve the problem. This algorithm can guarantee high-quality deep envelope samples for considering the local manifold and global structures information, which is helpful for imbalance learning.
Subject Enveloped Deep Sample Fuzzy Ensemble Learning Algorithm of Parkinson's Speech Data
Therefore, it is necessary to reconstruct the existing large segments within one subject into few segments even one segment within one subject, which can facilitate the extraction of relevant speech features to characterize diagnostic markers for the whole subject.
Envelope Imbalance Learning Algorithm based on Multilayer Fuzzy C-means Clustering and Minimum Interlayer discrepancy
However, all existing clustering methods are based on a one-time approach.
Subject Envelope based Multitype Reconstruction Algorithm of Speech Samples of Parkinson's Disease
The results show that the proposed algorithm is effective.
A Fast Algorithm for Computing the Deficiency Number of a Mahjong Hand
Face-to-face or online, each player begins with a hand of 13 tiles and players draw and discard tiles in turn until they complete a winning hand.
FA-GAN: Fused Attentive Generative Adversarial Networks for MRI Image Super-Resolution
High-resolution magnetic resonance images can provide fine-grained anatomical information, but acquiring such data requires a long scanning time.
Integrated Age Estimation Mechanism
Traditional age estimation mechanism focuses estimation age error, but ignores that there is a deviation between the estimated age and real age due to disease.
Deep Double-Side Learning Ensemble Model for Few-Shot Parkinson Speech Recognition
To solve these two problems, based on the existing Parkinson speech feature data set, a deep double-side learning ensemble model is designed in this paper that can reconstruct speech features and samples deeply and simultaneously.
Hybrid Embedded Deep Stacked Sparse Autoencoder with w_LPPD SVM Ensemble
For the issue that class representation ability of abstract information is limited by small sample problem, a feature fusion strategy has been designed aiming to combining abstract information learned by HFESAE with original feature and obtain hybrid features for feature reduction.
Classification Algorithm of Speech Data of Parkinsons Disease Based on Convolution Sparse Kernel Transfer Learning with Optimal Kernel and Parallel Sample Feature Selection
In this paper, a novel PD classification algorithm based on sparse kernel transfer learning combined with a parallel optimization of samples and features is proposed.
Two-Phase Object-Based Deep Learning for Multi-temporal SAR Image Change Detection
The prediction of this Phase is the set of changed and unchanged superpixels.
