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 1211–1220 of 4891 papers

TitleStatusHype
Gated Texture CNN for Efficient and Configurable Image DenoisingCode0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Augmented Memory for Correlation Filters in Real-Time UAV TrackingCode0
Toward Reliable AR-Guided Surgical Navigation: Interactive Deformation Modeling with Data-Driven Biomechanics and PromptsCode0
GAMMA: A General Agent Motion Model for Autonomous DrivingCode0
GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money LaunderingCode0
Functional Autoencoder for Smoothing and Representation LearningCode0
All4One: Symbiotic Neighbour Contrastive Learning via Self-Attention and Redundancy ReductionCode0
ADAGIO: Fast Data-aware Near-Isometric Linear EmbeddingsCode0
Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and ClusteringCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified