Electrical Engineering and Systems Science, Systems and Control
Tag: regularization
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Computer Science, Machine Learning
Class-wise Generalization Errors: An Information-Theoretic Analysis
Computer Science, Data Structures and Algorithms
Optimal Hashing-Based Time-Space Trade-Offs for Approximate Near Neighbors
Computer Science, Computer Vision and Pattern Recognition
Robustness in Computer Vision: A Survey of Recent Approaches
Computer Science, Information Retrieval
Self-Supervised Learning: Generative or Contrastive?
Electrical Engineering and Systems Science, Systems and Control
Data-Driven Predictive Control: A Model-Free Approach
Atmospheric and Oceanic Physics, Physics
Physics-Informed Pixel-wise Self-Attention Generative Adversarial Network for Super-Resolution of Wind Fields
Computer Science, Machine Learning
Data Augmentation Techniques for Improved Machine Learning Models
Computer Science, Computer Vision and Pattern Recognition
Enhanced Data Augmentation via Mixup: A Comprehensive Review
Mathematics, Numerical Analysis
Regularization by Truncation for Ill-Posed Heat Equation Problems
Computer Science, Machine Learning
Exploring the Robustness of Deep Neural Networks: A Review of Methods and Applications
Mathematics, Numerical Analysis
Spectral Regularization for Inverse Problems: A Post-processing Approach
Computer Science, Computer Vision and Pattern Recognition
Data Augmentation Techniques for Improving Image Recognition
Computer Science, Computer Vision and Pattern Recognition
SlimmeRF Outperforms TensorRF in NeRF-based 3D Generation
Computer Science, Computer Vision and Pattern Recognition
Elegant Alternative to Prevent Semantic Mismatch in Text-to-Image Synthesis
Computer Science, Computer Vision and Pattern Recognition
Evasive Learning: Mitigating Backdoor Attacks in Multimodal Contrastive Learning
Uncertainty-Aware Learning to Address Residual Noise
Computer Science, Computer Vision and Pattern Recognition
Open Domain Generalization: A Comprehensive Review
Computer Science, Machine Learning
Evaluating the Quality of Learned Representations Without Explicit Task Evaluations
