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 381–390 of 4891 papers

TitleStatusHype
Time-independent Spiking Neuron via Membrane Potential Estimation for Efficient Spiking Neural NetworksCode1
Cross-attention Inspired Selective State Space Models for Target Sound ExtractionCode1
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
Prompt Compression with Context-Aware Sentence Encoding for Fast and Improved LLM InferenceCode1
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge DistillationCode1
Gradient-free variational learning with conditional mixture networksCode1
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image ClassificationCode1
Leveraging Fine-Tuned Retrieval-Augmented Generation with Long-Context Support: For 3GPP StandardsCode1
ExpoMamba: Exploiting Frequency SSM Blocks for Efficient and Effective Image EnhancementCode1
Implicit Grid Convolution for Multi-Scale Image Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified