MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
Hochreiter, The vanishing gradient problem during learning recurrent neural networks, Int
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Effective depth, an operational count of sequential transformations, predicts CNN trainability better than nominal layer count because shortcuts and branches decouple the two.
Empirical comparison of transfer learning performance across eleven pre-trained models on five image datasets using accuracy, time, and size metrics.
citing papers explorer
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What If We Let Forecasting Forget? A Sparse Bottleneck for Cross-Variable Dependencies
MS-FLOW uses a capacity-limited sparse routing mechanism to model only critical inter-variable dependencies in time series data, achieving state-of-the-art accuracy on 12 benchmarks with fewer but more reliable connections.
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The Effective Depth Paradox: Evaluating the Relationship between Architectural Topology and Trainability in Deep CNNs
Effective depth, an operational count of sequential transformations, predicts CNN trainability better than nominal layer count because shortcuts and branches decouple the two.
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A Transfer Learning Evaluation of Deep Neural Networks for Image Classification
Empirical comparison of transfer learning performance across eleven pre-trained models on five image datasets using accuracy, time, and size metrics.