SOTAVerified

Computational Efficiency

Methods and optimizations to reduce the computational resources (e.g., time, memory, or power) needed for training and inference in models. This involves techniques that streamline processing, optimize algorithms, or leverage hardware to enhance performance without compromising accuracy.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 4026–4050 of 4891 papers

TitleStatusHype
PRE-Mamba: A 4D State Space Model for Ultra-High-Frequent Event Camera Deraining—0
PreMixer: MLP-Based Pre-training Enhanced MLP-Mixers for Large-scale Traffic Forecasting—0
Pretrained Event Classification Model for High Energy Physics Analysis—0
Pre-Trained Models: Past, Present and Future—0
Pre-Training Identification of Graph Winning Tickets in Adaptive Spatial-Temporal Graph Neural Networks—0
Principal Ellipsoid Analysis (PEA): Efficient non-linear dimension reduction & clustering—0
Prioritizing Modalities: Flexible Importance Scheduling in Federated Multimodal Learning—0
Priority Queue Formulation of Agent-Based Bathtub Model for Network Trip Flows in the Relative Space—0
PrisonBreak: Jailbreaking Large Language Models with Fewer Than Twenty-Five Targeted Bit-flips—0
Privacy-preserving Decentralized Federated Learning over Time-varying Communication Graph—0
Privacy-Preserving Federated Convex Optimization: Balancing Partial-Participation and Efficiency via Noise Cancellation—0
Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns—0
Privacy Preserving Visual SLAM—0
Private PAC Learning May be Harder than Online Learning—0
Private Selection from Private Candidates—0
Proactive Emergency Collision Avoidance for Automated Driving in Highway Scenarios—0
Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning—0
Improving Generalization of Medical Image Registration Foundation ModelCode0
Oblique Predictive Clustering TreesCode0
Union of Experts: Adapting Hierarchical Routing to Equivalently Decomposed TransformerCode0
Improving Hyper-Relational Knowledge Graph CompletionCode0
Improving Korean NLP Tasks with Linguistically Informed Subword Tokenization and Sub-character DecompositionCode0
Union Subgraph Neural NetworksCode0
Efficient Ternary Weight Embedding Model: Bridging Scalability and PerformanceCode0
Improving (α, f)-Byzantine Resilience in Federated Learning via layerwise aggregation and cosine distanceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified