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Found 9 papers, 4 papers with code
Identification of Cognitive Decline from Spoken Language through Feature Selection and the Bag of Acoustic Words Model
Memory disorders are a central factor in the decline of functioning and daily activities in elderly individuals.
Toward Scalable and Transparent Multimodal Analytics to Study Standard Medical Procedures: Linking Hand Movement, Proximity, and Gaze Data
This study employed multimodal learning analytics (MMLA) to analyze behavioral dynamics during the ABCDE procedure in nursing education, focusing on gaze entropy, hand movement velocities, and proximity measures.
Minimal Learning Machine for Multi-Label Learning
Distance-based supervised method, the minimal learning machine, constructs a predictive model from data by learning a mapping between input and output distance matrices.
Knowledge Discovery from Atomic Structures using Feature Importances
Molecular-level understanding of the interactions between the constituents of an atomic structure is essential for designing novel materials in various applications.
An Additive Autoencoder for Dimension Estimation
An additive autoencoder for dimension reduction, which is composed of a serially performed bias estimation, linear trend estimation, and nonlinear residual estimation, is proposed and analyzed.
More ConvNets in the 2020s: Scaling up Kernels Beyond 51x51 using Sparsity
Transformers have quickly shined in the computer vision world since the emergence of Vision Transformers (ViTs).
Scalable Initialization Methods for Large-Scale Clustering
In the experiments, comparison of the proposed methods to the K-means++ and K-means|| methods is conducted using an extensive set of reference and synthetic large-scale datasets.
Mislabel Detection of Finnish Publication Ranks
The paper proposes to analyze a data set of Finnish ranks of academic publication channels with Extreme Learning Machine (ELM).
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection
Specifically, for a small number of reference points, the clustering-based methods outperformed the standard random selection of the original MLM formulation.
