Supporting clustering with contrastive learning
Unsupervised clustering aims at discovering the semantic categories of data according to
some distance measured in the representation space. However, different categories often …
some distance measured in the representation space. However, different categories often …
Som-vae: Interpretable discrete representation learning on time series
High-dimensional time series are common in many domains. Since human cognition is not
optimized to work well in high-dimensional spaces, these areas could benefit from …
optimized to work well in high-dimensional spaces, these areas could benefit from …
Deep embedding clustering based on contractive autoencoder
Clustering large and high-dimensional document data has got a great interest. However,
current clustering algorithms lack efficient representation learning. Implementing deep …
current clustering algorithms lack efficient representation learning. Implementing deep …
ResNet autoencoders for unsupervised feature learning from high-dimensional data: Deep models resistant to performance degradation
Efficient modeling of high-dimensional data requires extracting only relevant dimensions
through feature learning. Unsupervised feature learning has gained tremendous attention …
through feature learning. Unsupervised feature learning has gained tremendous attention …
You never cluster alone
Recent advances in self-supervised learning with instance-level contrastive objectives
facilitate unsupervised clustering. However, a standalone datum is not perceiving the …
facilitate unsupervised clustering. However, a standalone datum is not perceiving the …
Explainable unsupervised machine learning for cyber-physical systems
Cyber-Physical Systems (CPSs) play a critical role in our modern infrastructure due to their
capability to connect computing resources with physical systems. As such, topics such as …
capability to connect computing resources with physical systems. As such, topics such as …
Deep discriminative clustering analysis
Traditional clustering methods often perform clustering with low-level indiscriminative
representations and ignore relationships between patterns, resulting in slight achievements …
representations and ignore relationships between patterns, resulting in slight achievements …
Self‐supervised representation learning of metro interior noise based on variational autoencoder and deep embedding clustering
Y Wang, H **ao, Z Zhang, X Guo… - Computer‐Aided Civil …, 2025 - Wiley Online Library
The noise within train is a paradox; while harmful to passenger health, it is useful to
operators as it provides insights into the working status of vehicles and tracks. Recently …
operators as it provides insights into the working status of vehicles and tracks. Recently …
Comparing stormwater quality and watershed typologies across the United States: A machine learning approach
Watersheds continue to be urbanized across different regions of the United States,
increasing the number of impaired waterbodies due to urban stormwater. Using machine …
increasing the number of impaired waterbodies due to urban stormwater. Using machine …
GAN-based image-to-friction generation for tactile simulation of fabric material
The electrovibration tactile display could render the tactile feeling of different textured
surfaces by generating the frictional force through voltage modulation. When a user is …
surfaces by generating the frictional force through voltage modulation. When a user is …