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 591–600 of 4891 papers

TitleStatusHype
Birdie: Advancing State Space Models with Reward-Driven Objectives and CurriculaCode1
Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image ClassificationCode1
Complete Dictionary Learning via _p-norm MaximizationCode1
Complex Neural Network based Joint AoA and AoD Estimation for Bistatic ISACCode1
AIM 2024 Challenge on UHD Blind Photo Quality AssessmentCode1
CE-SSL: Computation-Efficient Semi-Supervised Learning for ECG-based Cardiovascular Diseases DetectionCode1
Improved Protein-ligand Binding Affinity Prediction with Structure-Based Deep Fusion InferenceCode1
Improved Techniques for Training Adaptive Deep NetworksCode1
Improving Facade Parsing with Vision Transformers and Line IntegrationCode1
GLiNER-BioMed: A Suite of Efficient Models for Open Biomedical Named Entity RecognitionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified