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 1191–1200 of 4891 papers

TitleStatusHype
Spike-Kal: A Spiking Neuron Network Assisted Kalman Filter—0
RF-DETR Object Detection vs YOLOv12 : A Study of Transformer-based and CNN-based Architectures for Single-Class and Multi-Class Greenfruit Detection in Complex Orchard Environments Under Label Ambiguity—0
Hadamard product in deep learning: Introduction, Advances and Challenges—0
Non-Uniform Class-Wise Coreset Selection: Characterizing Category Difficulty for Data-Efficient Transfer Learning—0
Leave-One-Out Stable Conformal PredictionCode0
Communication Optimization for Decentralized Learning atop Bandwidth-limited Edge Networks—0
Efficient identification of linear, parameter-varying, and nonlinear systems with noise models—0
FedCanon: Non-Convex Composite Federated Learning with Efficient Proximal Operation on Heterogeneous Data—0
BitNet b1.58 2B4T Technical Report—0
Learning Transferable Friction Models and LuGre Identification via Physics Informed Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified