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 131–140 of 4891 papers

TitleStatusHype
Agent Attention: On the Integration of Softmax and Linear AttentionCode2
Grappa -- A Machine Learned Molecular Mechanics Force FieldCode2
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence ModelingCode2
GotenNet: Rethinking Efficient 3D Equivariant Graph Neural NetworksCode2
GoMAvatar: Efficient Animatable Human Modeling from Monocular Video Using Gaussians-on-MeshCode2
BEBLID: Boosted efficient binary local image descriptorCode2
Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary DomainsCode2
SparseLLM: Towards Global Pruning for Pre-trained Language ModelsCode2
Accelerating Direct Preference Optimization with Prefix SharingCode2
Free Video-LLM: Prompt-guided Visual Perception for Efficient Training-free Video LLMsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified