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 631–640 of 4891 papers

TitleStatusHype
Novel Deep Neural OFDM Receiver Architectures for LLR EstimationCode0
SAFE: Self-Adjustment Federated Learning Framework for Remote Sensing Collaborative Perception—0
Burst Image Super-Resolution with Mamba—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
SITA: Structurally Imperceptible and Transferable Adversarial Attacks for Stylized Image GenerationCode0
SuperFlow++: Enhanced Spatiotemporal Consistency for Cross-Modal Data PretrainingCode2
Language Model Uncertainty Quantification with Attention ChainCode1
Anomaly Detection Using Computer Vision: A Comparative Analysis of Class Distinction and Performance Metrics—0
PALATE: Peculiar Application of the Law of Total Expectation to Enhance the Evaluation of Deep Generative ModelsCode0
AMD-Hummingbird: Towards an Efficient Text-to-Video ModelCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified