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 2271–2280 of 4891 papers

TitleStatusHype
DELTA: Decomposed Efficient Long-Term Robot Task Planning using Large Language Models—0
On the Efficiency of Convolutional Neural Networks—0
Attention is Naturally Sparse with Gaussian Distributed Input—0
CATP: Cross-Attention Token Pruning for Accuracy Preserved Multimodal Model Inference—0
Improved model-free bounds for multi-asset options using option-implied information and deep learning—0
Tensorized NeuroEvolution of Augmenting Topologies for GPU AccelerationCode3
Learning-based model augmentation with LFRsCode0
Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISACCode1
MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with its Application for Colonic Polyp Image Segmentation—0
A Novel Feature Map Enhancement Technique Integrating Residual CNN and Transformer for Alzheimer Diseases Diagnosis—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified