feature extraction
Feature extraction is the process of identifying and extracting relevant attributes or characteristics from raw data that can enhance model performance, often simplifying the input for machine learning algorithms.
- A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
- Evolving and Regularizing Meta-Environment Learner for Fine-Grained Few-Shot Class-Incremental Learning
- Fin3R: Fine-tuning Feed-forward 3D Reconstruction Models via Monocular Knowledge Distillation
- FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
- GPSToken: Gaussian Parameterized Spatially-adaptive Tokenization for Image Representation and Generation
- IDOL: Meeting Diverse Distribution Shifts with Prior Physics for Tropical Cyclone Multi-Task Estimation
- ImageSentinel: Protecting Visual Datasets from Unauthorized Retrieval-Augmented Image Generation
- LLM Layers Immediately Correct Each Other
- LLM-DAMVC: A Large Language Model Assisted Dynamic Agent for Multi-View Clustering
- Learning Memory-Enhanced Improvement Heuristics for Flexible Job Shop Scheduling
- Measuring and Guiding Monosemanticity
- MixSignGraph: A Sign Sequence is Worth Mixed Graphs of Nodes
- Native Segmentation Vision Transformers
- SDPGO: Efficient Self-Distillation Training Meets Proximal Gradient Optimization
- ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding