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 51–75 of 4891 papers

TitleStatusHype
Residual Connection-Enhanced ConvLSTM for Lithium Dendrite Growth Prediction—0
Scalable Machine Learning Algorithms using Path Signatures—0
OmniReflect: Discovering Transferable Constitutions for LLM agents via Neuro-Symbolic Reflections—0
Optimal Depth of Neural Networks—0
PET Tracer Separation Using Conditional Diffusion Transformer with Multi-latent Space LearningCode0
Client Selection Strategies for Federated Semantic Communications in Heterogeneous IoT Networks—0
Speeding up Local Optimization in Vehicle Routing with Tensor-based GPU Acceleration—0
Multimodal Fused Learning for Solving the Generalized Traveling Salesman Problem in Robotic Task Planning—0
Cross-Modal Obfuscation for Jailbreak Attacks on Large Vision-Language Models—0
MoiréXNet: Adaptive Multi-Scale Demoiréing with Linear Attention Test-Time Training and Truncated Flow Matching Prior—0
Efficient Transformations in Deep Learning Convolutional Neural Networks—0
FlowRAM: Grounding Flow Matching Policy with Region-Aware Mamba Framework for Robotic Manipulation—0
Islanding Strategy for Smart Grids Oriented to Resilience Enhancement and Its Power Supply Range Optimization—0
Brain Stroke Classification Using Wavelet Transform and MLP Neural Networks on DWI MRI Images—0
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference—0
Efficient Malware Detection with Optimized Learning on High-Dimensional Features—0
Adaptive Action Duration with Contextual Bandits for Deep Reinforcement Learning in Dynamic EnvironmentsCode0
Swarm-STL: A Framework for Motion Planning in Large-Scale, Multi-Swarm Systems—0
sHGCN: Simplified hyperbolic graph convolutional neural networksCode0
Expressive Score-Based Priors for Distribution Matching with Geometry-Preserving RegularizationCode0
Accurate and scalable exchange-correlation with deep learning—0
A Comprehensive Survey on Deep Learning Solutions for 3D Flood Mapping—0
Improving Prostate Gland Segmenting Using Transformer based Architectures—0
Finding Optimal Kernel Size and Dimension in Convolutional Neural Networks An Architecture Optimization Approach—0
Mitigating loss of variance in ensemble data assimilation: machine learning-based and distance-free localizations for better covariance estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified