Tabular encoder choice reorders multimodal rankings, can erase apparent fusion gains, and requires non-vanilla extraction for in-context learning models to avoid train-test representation shift.
Gradient boosting neural networks: Grownet.arXiv2020
3 Pith papers cite this work, alongside 55 external citations. Polarity classification is still indexing.
3
Pith papers citing it
55
external citations · Pith
abstract
A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters.
representative citing papers
HiPreNets progressively refines neural networks via residual learning and adaptive techniques to reduce both RMSE and L^∞ errors, outperforming standard networks on Feynman benchmarks and enabling fast high-dimensional ODE surrogates.
A systematic survey of data balancing methods that organizes the field into six taxonomic groups and reports a two-dataset case study showing method performance depends on the dataset and classifier.
citing papers explorer
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The Importance of Encoder Choice:A Tabular-Image Study
Tabular encoder choice reorders multimodal rankings, can erase apparent fusion gains, and requires non-vanilla extraction for in-context learning models to avoid train-test representation shift.
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HiPreNets: High-Precision Neural Networks through Progressive Training
HiPreNets progressively refines neural networks via residual learning and adaptive techniques to reduce both RMSE and L^∞ errors, outperforming standard networks on Feynman benchmarks and enabling fast high-dimensional ODE surrogates.
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Data Balancing Strategies: A Systematic Survey of Resampling and Augmentation Methods
A systematic survey of data balancing methods that organizes the field into six taxonomic groups and reports a two-dataset case study showing method performance depends on the dataset and classifier.