Deep Learning Neural Networks Federated Learning Computer Vision Optimization Model Evaluation Model Training Algorithms Model Optimization Performance Evaluation Statistical Learning Supervised Learning Privacy Data Analysis Natural Language Processing Optimization Techniques Data Science Representation Learning Training Strategies Graph Theory Generative Models Simulation Techniques Hyperparameter Tuning Data Privacy Training Methods Computer Graphics Model Compression Optimization Algorithms Unsupervised Learning Experimental Methods Evaluation Metrics Image Processing 3D Representation Vision Transformers Model Aggregation Scene Perception Scene Reconstruction Simulation 3D Representation Learning Collaborative Learning Competitions Fairness Methods Mathematical Modelling Heterogeneous Data Optimal Transport Detection Algorithms Image Recognition Privacy-Preserving Computation Unlearning Methods Data Augmentation Defenses 3D Modeling Data-Driven Approaches Stochastic Methods Loss Functions Parallel Computing Image Classification Data Transmission Machine Unlearning Data Retrieval Domain Adaptation Data Representation Heterogeneous Models Data Integration Compositional Reasoning Adversarial Training Performance Metrics Feature Engineering Performance Optimization Foundation Models Data Efficiency Rendering Techniques Classifier Design Model Architecture Training Algorithms Graph Neural Networks Robustness Simulations Continual Learning Self-Training Empirical Studies Audio Processing Large Language Models Embedding Techniques Caching Techniques AI Safety Performance Analysis Incremental Learning Memory Architectures Research Task Delegation Edge Computing Transfer Learning Modeling Techniques Knowledge Retention Anomaly Detection Split Learning Model Interpretation Gradient-Based Methods Adversarial Learning
The authors report up to 450x faster training with accuracy maintained even at D=128.
