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 2931–2940 of 4891 papers

TitleStatusHype
Data-Driven Model Discrimination of Switched Nonlinear Systems with Temporal Logic Inference—0
Geometric-Based Pruning Rules For Change Point Detection in Multiple Independent Time Series—0
A reinforcement learning strategy for p-adaptation in high order solvers—0
Batches Stabilize the Minimum Norm Risk in High Dimensional Overparameterized Linear Regression—0
Efficient Training of Physics-Informed Neural Networks with Direct Grid Refinement Algorithm—0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
Domain-Aware Few-Shot Learning for Optical Coherence Tomography Noise Reduction—0
Slicing Unbalanced Optimal TransportCode0
Efficiently Learning the Graph for Semi-supervised LearningCode0
Mean-Variance Efficient Collaborative Filtering for Stock Recommendation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ViTaLHamming Loss0.05—Unverified