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 81–90 of 4891 papers

TitleStatusHype
SOAP: Improving and Stabilizing Shampoo using AdamCode3
I^2-World: Intra-Inter Tokenization for Efficient Dynamic 4D Scene ForecastingCode2
Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene RepresentationCode2
Integrating Neural Operators with Diffusion Models Improves Spectral Representation in Turbulence ModelingCode2
HeadInfer: Memory-Efficient LLM Inference by Head-wise OffloadingCode2
Attentive Merging of Hidden Embeddings from Pre-trained Speech Model for Anti-spoofing DetectionCode2
3D-RCNet: Learning from Transformer to Build a 3D Relational ConvNet for Hyperspectral Image ClassificationCode2
Harder Tasks Need More Experts: Dynamic Routing in MoE ModelsCode2
InteractRank: Personalized Web-Scale Search Pre-Ranking with Cross Interaction FeaturesCode2
SparseLLM: Towards Global Pruning for Pre-trained Language ModelsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified