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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2701–2710 of 4891 papers

TitleStatusHype
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
Learning-based model augmentation with LFRsCode0
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
Computation and Communication Efficient Lightweighting Vertical Federated Learning for Smart Building IoT—0
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay—0
P-Hologen: An End-to-End Generative Framework for Phase-Only HologramsCode0
Multi-scale Unified Network for Image Classification—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified