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 1911–1920 of 4891 papers

TitleStatusHype
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
UOUO: Uncontextualized Uncommon Objects for Measuring Knowledge Horizons of Vision Language Models—0
LoRA-Pro: Are Low-Rank Adapters Properly Optimized?Code2
CSWin-UNet: Transformer UNet with Cross-Shaped Windows for Medical Image SegmentationCode1
SDoH-GPT: Using Large Language Models to Extract Social Determinants of Health (SDoH)—0
Embedding-Free Transformer with Inference Spatial Reduction for Efficient Semantic SegmentationCode1
An Efficient Procedure for Computing Bayesian Network Structure Learning—0
Scalify: scale propagation for efficient low-precision LLM trainingCode1
Evaluating Uncertainties in Electricity Markets via Machine Learning and Quantum Computing—0
CloudFixer: Test-Time Adaptation for 3D Point Clouds via Diffusion-Guided Geometric TransformationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified