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 2721–2730 of 4891 papers

TitleStatusHype
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly—0
Privacy-preserving Multi-biometric Indexing based on Frequent Binary Patterns—0
Efficient Quantification and Representation of Aggregate Flexibility in Electric VehiclesCode0
xVal: A Continuous Numerical Tokenization for Scientific Language ModelsCode1
Learning to Reach Goals via DiffusionCode0
Fast, Expressive SE(n) Equivariant Networks through Weight-Sharing in Position-Orientation SpaceCode1
An Integer Clustering Approach for Modeling Large-Scale EV Fleets with Guaranteed Performance—0
DeepHGCN: Toward Deeper Hyperbolic Graph Convolutional NetworksCode0
The Inhibitor: ReLU and Addition-Based Attention for Efficient Transformers—0
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor CoresCode1
Show:102550
← PrevPage 273 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified