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 131–140 of 4891 papers

TitleStatusHype
Deep Learning Accelerated Quantum Transport Simulations in Nanoelectronics: From Break Junctions to Field-Effect TransistorsCode2
Wavelet Latent Diffusion (Wala): Billion-Parameter 3D Generative Model with Compact Wavelet EncodingsCode2
Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair ClimbingCode2
Toward Automated Algorithm Design: A Survey and Practical Guide to Meta-Black-Box-OptimizationCode2
Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning ModelsCode2
Accelerating Direct Preference Optimization with Prefix SharingCode2
LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image RestorationCode2
Quamba: A Post-Training Quantization Recipe for Selective State Space ModelsCode2
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
Large Scale Longitudinal Experiments: Estimation and InferenceCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified