How powerful are Graph Neural Networks? - Oxford Geometric Deep Learning
Federico Barbero
How powerful are Graph Neural Networks? - Oxford Geometric Deep Learning
9:27
Transformers are Graph Attention Networks !? - Oxford Geometric Deep Learning
Federico Barbero
Transformers are Graph Attention Networks !? - Oxford Geometric Deep Learning
8:42
Graph Attention Networks - Oxford Geometric Deep Learning
Federico Barbero
Graph Attention Networks - Oxford Geometric Deep Learning
9:09
Graph Convolutional Networks - Oxford Geometric Deep Learning
Federico Barbero
Graph Convolutional Networks - Oxford Geometric Deep Learning
13:56
Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift
Federico Barbero
Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift
19:39
Deconstructing the Python Walrus Operator
Federico Barbero
Deconstructing the Python Walrus Operator
12:01
Generators in Python: What are they and their advantages!
Federico Barbero
Generators in Python: What are they and their advantages!
10:20
Why Python list comprehensions are faster and more elegant
Federico Barbero
Why Python list comprehensions are faster and more elegant
8:39
Improving your Python by understanding Python bytecode!
Federico Barbero
Improving your Python by understanding Python bytecode!
15:41
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
Federico Barbero
Fundamental Tradeoffs between Invariance and Sensitivity to Adversarial Perturbations
19:04
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
Federico Barbero
Tempered Sigmoid Activations for Deep Learning with Differential Privacy
14:10
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
Federico Barbero
Regularisation of Neural Networks by Enforcing Lipschitz Continuity
15:12
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
Federico Barbero
Spectral Norm Regularization for Improving the Generalizability of Deep Learning
19:04
Developing a Deep Learning Library - LeCun's MNIST classifier - Part 3
Federico Barbero
Developing a Deep Learning Library - LeCun's MNIST classifier - Part 3
9:58
Developing a Deep Learning Library - Adam, RELU and Scikit-learn API - Part 2
Federico Barbero
Developing a Deep Learning Library - Adam, RELU and Scikit-learn API - Part 2
11:10
Developing a Deep Learning Library - JoelNet Library and Neural Networks - Part 1
Federico Barbero
Developing a Deep Learning Library - JoelNet Library and Neural Networks - Part 1
22:09
Tutorial on the Fast Gradient Sign Method for Adversarial Samples
Federico Barbero
Tutorial on the Fast Gradient Sign Method for Adversarial Samples
7:11
Lipschitz Regularization of Neural Networks - Intriguing Properties of Neural Networks
Federico Barbero
Lipschitz Regularization of Neural Networks - Intriguing Properties of Neural Networks
19:10
What are Adversarial Samples in Machine Learning? - Explaining and Harnessing Adversarial Samples
Federico Barbero
What are Adversarial Samples in Machine Learning? - Explaining and Harnessing Adversarial Samples
20:54