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 291–300 of 4891 papers

TitleStatusHype
Towards Reliable Medical Image Segmentation by utilizing Evidential Calibrated UncertaintyCode1
Exploiting Deblurring Networks for Radiance FieldsCode1
Estimating Koopman operators with sketching to provably learn large scale dynamical systemsCode1
Accel-GCN: High-Performance GPU Accelerator Design for Graph Convolution NetworksCode1
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic ModelsCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language ModelsCode1
Event-based Video Reconstruction via Potential-assisted Spiking Neural NetworkCode1
Exploiting Redundancy: Separable Group Convolutional Networks on Lie GroupsCode1
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified