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 3261–3270 of 4891 papers

TitleStatusHype
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference—0
Flover: A Temporal Fusion Framework for Efficient Autoregressive Model Parallel InferenceCode0
Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation—0
RDA-INR: Riemannian Diffeomorphic Autoencoding via Implicit Neural Representations—0
GELU Activation Function in Deep Learning: A Comprehensive Mathematical Analysis and Performance—0
Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation—0
The Deep Promotion Time Cure ModelCode0
CageViT: Convolutional Activation Guided Efficient Vision Transformer—0
DualFL: A Duality-based Federated Learning Algorithm with Communication Acceleration in the General Convex Regime—0
Time Series Clustering With Random Convolutional KernelsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified