Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

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Clustering in education, particularly in large-scale online environments like MOOCs, is essential for understanding and adapting to diverse student needs. However, the effectiveness of clustering depends on its interpretability, which becomes challenging w ...

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We introduce robust principal component analysis from a data matrix in which the entries of its columns have been corrupted by permutations, termed Unlabeled Principal Component Analysis (UPCA). Using algebraic geometry, we establish that UPCA is a well-de ...

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Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Unsupervised classification of snowflake images using a generative adversarial network and K-medoids classification

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Interpret3C: Interpretable Student Clustering Through Individualized Feature Selection

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Topics in statistical physics of high-dimensional machine learning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Unlabeled Principal Component Analysis and Matrix Completion

Extending Capabilities of Attention-based Models

Robust machine learning for neuroscientific inference

Uncertainty quantification for a deep learning models for image-based crack segmentation

Planning urban proximities: An empirical analysis of how residential preferences conflict with the urban morphologies and residential practices

Match Normalization: Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World

Detection of Malicious Agents in Social Learning

Boosting likelihood learning with event reweighting

Test-time adaptation for 6D pose tracking

GANDALF: Graph-based transformer and Data Augmentation Active Learning Framework with interpretable features for multi-label chest Xray classification

Social Opinion Formation and Decision Making Under Communication Trends

Understanding generalization and robustness in modern deep learning

Transfer learning application of self-supervised learning in ARPES

Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining

Self-Supervised Learning for Patient Stratification and Survival Analysis in Computational Pathology: An Application to Colorectal Cancer

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region