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 191–200 of 4891 papers

TitleStatusHype
Fast Adversarial Attacks on Language Models In One GPU MinuteCode2
FastBlend: a Powerful Model-Free Toolkit Making Video Stylization EasierCode2
BitDecoding: Unlocking Tensor Cores for Long-Context LLMs Decoding with Low-Bit KV CacheCode2
Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair ClimbingCode2
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image ClassificationCode2
Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning ModelsCode2
Emulating Self-attention with Convolution for Efficient Image Super-ResolutionCode2
Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive SurveyCode2
Advances in 4D Generation: A SurveyCode2
Enhancing Autonomous Driving Systems with On-Board Deployed Large Language ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified