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 2331–2340 of 4891 papers

TitleStatusHype
C-RADAR: A Centralized Deep Learning System for Intrusion Detection in Software Defined Networks—0
Modelling Behaviour of Sensors using a Novel β-divergence based Adaptive Filter—0
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy—0
American option pricing using generalised stochastic hybrid systems—0
Tiny-Toxic-Detector: A compact transformer-based model for toxic content detection—0
The Role of Fibration Symmetries in Geometric Deep Learning—0
A Simple Baseline with Single-encoder for Referring Image Segmentation—0
Learning-Based Adaptive Dynamic Routing with Stability Guarantee for a Single-Origin-Single-Destination Network—0
Step-by-Step Unmasking for Parameter-Efficient Fine-tuning of Large Language ModelsCode0
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified