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 1471–1480 of 4891 papers

TitleStatusHype
TDDBench: A Benchmark for Training data detection—0
Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money LaunderingCode1
SpiDR: A Reconfigurable Digital Compute-in-Memory Spiking Neural Network Accelerator for Event-based Perception—0
DroidSpeak: KV Cache Sharing for Cross-LLM Communication and Multi-LLM Serving—0
You are out of context!—0
Deep operator neural network applied to efficient computation of asteroid surface temperature and the Yarkovsky effect—0
How Analysis Can Teach Us the Optimal Way to Design Neural Operators—0
Real-Time Polygonal Semantic Mapping for Humanoid Robot Stair ClimbingCode2
An algorithm for two-player repeated games with imperfect public monitoringCode0
Quantum Rationale-Aware Graph Contrastive Learning for Jet Discrimination—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified