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 21–30 of 4891 papers

TitleStatusHype
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models—0
Decomposing the Time Series Forecasting Pipeline: A Modular Approach for Time Series Representation, Information Extraction, and ProjectionCode0
A Survey on Prompt TuningCode0
DC-AR: Efficient Masked Autoregressive Image Generation with Deep Compression Hybrid Tokenizer—0
High Order Collaboration-Oriented Federated Graph Neural Network for Accurate QoS Prediction—0
MVNet: Hyperspectral Remote Sensing Image Classification Based on Hybrid Mamba-Transformer Vision Backbone ArchitectureCode0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
Instant Particle Size Distribution Measurement Using CNNs Trained on Synthetic DataCode0
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed AugmentationCode1
FADRM: Fast and Accurate Data Residual Matching for Dataset DistillationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified