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 1591–1600 of 4891 papers

TitleStatusHype
CluStRE: Streaming Graph Clustering with Multi-Stage Refinement—0
Federated Learning with Reservoir State Analysis for Time Series Anomaly DetectionCode0
Sequential Stochastic Combinatorial Optimization Using Hierarchal Reinforcement Learning—0
Graph Neural Network Enabled Pinching Antennas—0
Hybrid machine learning based scale bridging framework for permeability prediction of fibrous structures—0
Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian Neural NetworksCode0
Tighter sparse variational Gaussian processes—0
Flopping for FLOPs: Leveraging equivariance for computational efficiency—0
Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient—0
TQ-DiT: Efficient Time-Aware Quantization for Diffusion Transformers—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified