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 221–230 of 4891 papers

TitleStatusHype
Partition Generative Modeling: Masked Modeling Without MasksCode4
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
LoTA-QAF: Lossless Ternary Adaptation for Quantization-Aware Fine-TuningCode0
Message-Passing State-Space Models: Improving Graph Learning with Modern Sequence Modeling—0
Scalable Gaussian Processes with Low-Rank Deep Kernel Decomposition—0
Tropical Geometry Based Edge Detection Using Min-Plus and Max-Plus Algebra—0
Composable Cross-prompt Essay Scoring by Merging Models—0
Learning Latent Variable Models via Jarzynski-adjusted Langevin Algorithm—0
Hard Negative Mining for Domain-Specific Retrieval in Enterprise Systems—0
Improved Algorithms for Overlapping and Robust Clustering of Edge-Colored Hypergraphs: An LP-Based Combinatorial Approach—0
Show:102550
← PrevPage 23 of 490Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified