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 31–40 of 4891 papers

TitleStatusHype
Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of ExpertsCode4
Partition Generative Modeling: Masked Modeling Without MasksCode4
An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language ModelsCode4
On the limits of agency in agent-based modelsCode4
RaDe-GS: Rasterizing Depth in Gaussian SplattingCode4
Dataset Distillation with Neural Characteristic Function: A Minmax PerspectiveCode3
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge DistillationCode3
Consistency Models Made EasyCode3
HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation ModelCode3
CoverM: Read alignment statistics for metagenomicsCode3
Show:102550
← PrevPage 4 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified