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

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Showing 951–960 of 4891 papers

TitleStatusHype
Learning to Search for Vehicle Routing with Multiple Time Windows—0
A New Deep-learning-Based Approach For mRNA Optimization: High Fidelity, Computation Efficiency, and Multiple Optimization FactorsCode0
AMBER: Adaptive Mesh Generation by Iterative Mesh Resolution PredictionCode0
LLaMA-XR: A Novel Framework for Radiology Report Generation using LLaMA and QLoRA Fine Tuning—0
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing—0
LeMoRe: Learn More Details for Lightweight Semantic SegmentationCode0
THINK-Bench: Evaluating Thinking Efficiency and Chain-of-Thought Quality of Large Reasoning Models—0
Improving QA Efficiency with DistilBERT: Fine-Tuning and Inference on mobile Intel CPUs—0
CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs—0
Improving Out-of-Distribution Detection with Markov Logic Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified