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 141–150 of 4891 papers

TitleStatusHype
I^2-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene ForecastingCode2
LightGNN: Simple Graph Neural Network for RecommendationCode2
A Simple Baseline for Efficient Hand Mesh ReconstructionCode2
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
An Unforgeable Publicly Verifiable Watermark for Large Language ModelsCode2
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-MeshCode2
SparseLLM: Towards Global Pruning for Pre-trained Language ModelsCode2
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
Flow Matching in Latent SpaceCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified