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

Showing 541550 of 4891 papers

TitleStatusHype
AI Accelerator Survey and TrendsCode1
FViT: A Focal Vision Transformer with Gabor FilterCode1
Boosting Light-Weight Depth Estimation Via Knowledge DistillationCode1
GaraMoSt: Parallel Multi-Granularity Motion and Structural Modeling for Efficient Multi-Frame Interpolation in DSA ImagesCode1
Generative Multiplane Neural Radiance for 3D-Aware Image GenerationCode1
Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional NetworksCode1
Activation-Informed Merging of Large Language ModelsCode1
Fully Attentional Network for Semantic SegmentationCode1
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNCode1
HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance ScalingCode1
Show:102550
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05Unverified