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 3481–3490 of 4891 papers

TitleStatusHype
A Multiple Source Hourglass Deep Network for Multi-Focus Image Fusion—0
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement—0
An Accelerated Camera 3DMA Framework for Efficient Urban GNSS Multipath Estimation—0
ARES: An Efficient Algorithm with Recurrent Evaluation and Sampling-Driven Inference for Maximum Independent Set—0
An ADRC-Incorporated Stochastic Gradient Descent Algorithm for Latent Factor Analysis—0
An Algorithm-Hardware Co-Optimized Framework for Accelerating N:M Sparse Transformers—0
YOCO: A Hybrid In-Memory Computing Architecture with 8-bit Sub-PetaOps/W In-Situ Multiply Arithmetic for Large-Scale AI—0
Analysis of high-dimensional Continuous Time Markov Chains using the Local Bouncy Particle Sampler—0
Analysis of Truncated Singular Value Decomposition for Koopman Operator-Based Lane Change Model—0
Analytical Discovery of Manifold with Machine Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified