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 4426–4450 of 4891 papers

TitleStatusHype
Efficient surrogate modeling methods for large-scale Earth system models based on machine learning techniques—0
Robust and Adaptive Planning under Model Uncertainty—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Reproducibility Evaluation of SLANT Whole Brain Segmentation Across Clinical Magnetic Resonance Imaging Protocols—0
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement LearningCode0
Efficient Convolutional Neural Network Training with Direct Feedback Alignment—0
Artificial neural networks condensation: A strategy to facilitate adaption of machine learning in medical settings by reducing computational burden—0
Deep autoregressive neural networks for high-dimensional inverse problems in groundwater contaminant source identificationCode0
Accurate Hand Keypoint Localization on Mobile Devices—0
Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber—0
Imbalanced biomedical data classification using self-adaptive multilayer ELM combined with dynamic GAN—0
Shortcut Matrix Product States and its applications—0
Surrogate-assisted Bayesian inversion for landscape and basin evolution modelsCode0
Learning Item-Interaction Embeddings for User Recommendations—0
Non-Intrusive Load Monitoring with Fully Convolutional Networks—0
Demystifying excessively volatile human learning: A Bayesian persistent prior and a neural approximation—0
Efficient Stochastic Gradient Hard Thresholding—0
Regret Bounds for Online Portfolio Selection with a Cardinality Constraint—0
PAC-Bayes Tree: weighted subtrees with guarantees—0
Deep Signal Recovery with One-Bit Quantization—0
Iterative Projection and Matching: Finding Structure-preserving Representatives and Its Application to Computer VisionCode0
CCNet: Criss-Cross Attention for Semantic SegmentationCode0
Compact and Efficient Encodings for Planning in Factored State and Action Spaces with Learned Binarized Neural Network Transition Models—0
Machine learning enables long time scale molecular photodynamics simulations—0
Explain to Fix: A Framework to Interpret and Correct DNN Object Detector PredictionsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified