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 2251–2260 of 4891 papers

TitleStatusHype
Fast Fishing: Approximating BAIT for Efficient and Scalable Deep Active Image ClassificationCode1
Proof-of-Learning with Incentive Security—0
Diffusion-Based Joint Temperature and Precipitation Emulation of Earth System Models—0
Minimax Optimal Goodness-of-Fit Testing with Kernel Stein Discrepancy—0
TSLANet: Rethinking Transformers for Time Series Representation LearningCode3
BERT-LSH: Reducing Absolute Compute For AttentionCode0
Generalized Population-Based Training for Hyperparameter Optimization in Reinforcement LearningCode0
Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image DenoisingCode2
Attention-Aware Laparoscopic Image Desmoking Network with Lightness Embedding and Hybrid Guided EmbeddingCode0
Precoder Design for User-Centric Network Massive MIMO with Matrix Manifold Optimization—0
Show:102550
← PrevPage 226 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified