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 2301–2310 of 4891 papers

TitleStatusHype
A Physics-Informed Machine Learning Approach for Solving Distributed Order Fractional Differential Equations—0
Shuffle Vision Transformer: Lightweight, Fast and Efficient Recognition of Driver Facial Expression—0
Onboard Satellite Image Classification for Earth Observation: A Comparative Study of ViT ModelsCode0
State-space models are accurate and efficient neural operators for dynamical systemsCode0
InfraLib: Enabling Reinforcement Learning and Decision-Making for Large-Scale Infrastructure Management—0
Configurable Foundation Models: Building LLMs from a Modular Perspective—0
Fast, High-Quality and Parameter-Efficient Articulatory Synthesis using Differentiable DSP—0
Exact first passage time distribution for second-order reactions in chemical networks—0
Relative-Translation Invariant Wasserstein Distance—0
Efficient Image Compression Using Advanced State Space Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified