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 2761–2770 of 4891 papers

TitleStatusHype
Accelerating Machine Learning Algorithms with Adaptive Sampling—0
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
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science ApplicationsCode1
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
Prompt Tuned Embedding Classification for Multi-Label Industry Sector AllocationCode1
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam—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