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 3901–3925 of 4891 papers

TitleStatusHype
Prediction of 3D Cardiovascular hemodynamics before and after coronary artery bypass surgery via deep learning—0
Data-driven sparse polynomial chaos expansion for models with dependent inputs—0
ZeRO-Offload: Democratizing Billion-Scale Model TrainingCode0
Frequency-weighted H2-optimal model order reduction via oblique projection—0
CobBO: Coordinate Backoff Bayesian Optimization with Two-Stage KernelsCode1
Resolution-Based Distillation for Efficient Histology Image Classification—0
Disentangled Self-Attentive Neural Networks for Click-Through Rate PredictionCode0
A novel shape matching descriptor for real-time hand gesture recognition—0
End-to-end Prostate Cancer Detection in bpMRI via 3D CNNs: Effects of Attention Mechanisms, Clinical Priori and Decoupled False Positive ReductionCode1
A Robust and Efficient Framework for Sports-Field Registration—0
DCT-SNN: Using DCT To Distribute Spatial Information Over Time for Low-Latency Spiking Neural Networks—0
Statistically Consistent Saliency Estimation—0
RDI-Net: Relational Dynamic Inference NetworksCode0
Globally Optimal and Efficient Manhattan Frame Estimation by Delimiting Rotation Search Space—0
DTMNet: A Discrete Tchebichef Moments-Based Deep Neural Network for Multi-Focus Image Fusion—0
On Stochastic Variance Reduced Gradient Method for Semidefinite Optimization—0
DiffAutoML: Differentiable Joint Optimization for Efficient End-to-End Automated Machine Learning—0
Rethinking Convolution: Towards an Optimal Efficiency—0
Estimation of Number of Communities in Assortative Sparse Networks—0
Optimizing Over All Sequences of Orthogonal Polynomials—0
Self-Supervised Learning of Compressed Video Representations—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
A Simple Unified Information Regularization Framework for Multi-Source Domain Adaptation—0
Optimizing Quantized Neural Networks with Natural Gradient—0
DIET-SNN: A Low-Latency Spiking Neural Network with Direct Input Encoding & Leakage and Threshold Optimization—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified