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 1331–1340 of 4891 papers

TitleStatusHype
A Temporal Linear Network for Time Series ForecastingCode0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture DesignCode0
Interpretable breast cancer classification using CNNs on mammographic imagesCode0
Reachability Analysis Using Constrained Polynomial Logical ZonotopesCode0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Generalized Adaptive Transfer Network: Enhancing Transfer Learning in Reinforcement Learning Across DomainsCode0
I-SplitEE: Image classification in Split Computing DNNs with Early ExitsCode0
Adapting Segment Anything Model (SAM) to Experimental Datasets via Fine-Tuning on GAN-based Simulation: A Case Study in Additive ManufacturingCode0
Deep Learning for Early Alzheimer Disease Detection with MRI ScansCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified