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 151–160 of 4891 papers

TitleStatusHype
SparseLLM: Towards Global Pruning for Pre-trained Language ModelsCode2
FuXi Weather: A data-to-forecast machine learning system for global weatherCode2
An Unforgeable Publicly Verifiable Watermark for Large Language ModelsCode2
A Survey on Diffusion Models for Anomaly DetectionCode2
Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-ResolutionCode2
A Simple Baseline for Efficient Hand Mesh ReconstructionCode2
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
HeadInfer: Memory-Efficient LLM Inference by Head-wise OffloadingCode2
I^2-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene ForecastingCode2
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified