R.I.P.
π»
Ghosted
Understanding Deep Convolutional Networks
January 19, 2016 Β· Declared Dead Β· π Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
StΓ©phane Mallat
arXiv ID
1601.04920
Category
stat.ML: Machine Learning (Stat)
Cross-listed
cs.CV,
cs.LG
Citations
669
Venue
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Last Checked
1 month ago
Abstract
Deep convolutional networks provide state of the art classifications and regressions results over many high-dimensional problems. We review their architecture, which scatters data with a cascade of linear filter weights and non-linearities. A mathematical framework is introduced to analyze their properties. Computations of invariants involve multiscale contractions, the linearization of hierarchical symmetries, and sparse separations. Applications are discussed.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Machine Learning (Stat)
R.I.P.
π»
Ghosted
Distilling the Knowledge in a Neural Network
R.I.P.
π»
Ghosted
Layer Normalization
R.I.P.
π»
Ghosted
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
R.I.P.
π»
Ghosted
Domain-Adversarial Training of Neural Networks
R.I.P.
π»
Ghosted
Deep Learning with Differential Privacy
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Language Models are Few-Shot Learners
R.I.P.
π»
Ghosted
PyTorch: An Imperative Style, High-Performance Deep Learning Library
R.I.P.
π»
Ghosted
XGBoost: A Scalable Tree Boosting System
R.I.P.
π»
Ghosted