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 3091–3100 of 4891 papers

TitleStatusHype
Tackling Intertwined Data and Device Heterogeneities in Federated Learning with Unlimited StalenessCode0
Nonparametric mixed logit model with market-level parameters estimated from market share data—0
Efficient N:M Sparse DNN Training Using Algorithm, Architecture, and Dataflow Co-Design—0
Bridging Sensor Gaps via Attention Gated Tuning for Hyperspectral Image ClassificationCode0
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam—0
Source-free Active Domain Adaptation for Diabetic Retinopathy Grading Based on Ultra-wide-field Fundus ImageCode0
Learning Dynamic MRI Reconstruction with Convolutional Network Assisted Reconstruction Swin Transformer—0
Posterior sampling algorithms for unsupervised speech enhancement with recurrent variational autoencoder—0
A Dynamic Linear Bias Incorporation Scheme for Nonnegative Latent Factor Analysis—0
FRAMU: Attention-based Machine Unlearning using Federated Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified