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 1801–1810 of 4891 papers

TitleStatusHype
American option pricing using generalised stochastic hybrid systems—0
Tiny-Toxic-Detector: A compact transformer-based model for toxic content detection—0
Gradient-free variational learning with conditional mixture networksCode1
A Minibatch-SGD-Based Learning Meta-Policy for Inventory Systems with Myopic Optimal Policy—0
LLaVA-MoD: Making LLaVA Tiny via MoE Knowledge DistillationCode3
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
MSFMamba: Multi-Scale Feature Fusion State Space Model for Multi-Source Remote Sensing Image ClassificationCode1
Research Advances and New Paradigms for Biology-inspired Spiking Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified