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Displaying 20 speakers out of 20
A. ANSUINI (SISSA)
Hands-on tutorial: Overfitting and Regularization
A. CELANI (ICTP)
Welcome Address
A. PEZZOTTA (ICTP)
Hands-on tutorial: An Introduction to Neural Networks
B. BIGGIO (Cagliari U.)
Adversarial Machine Learning (Part I)
Adversarial Machine Learning (Part II)
B. KAPPEN (Radboud U., Nijmegen)
Perceptrons and Gradient Descent Learning Rules
Control Theory and Path Integral Methods - 1
Control Theory and Path Integral Methods - 2
C. MATHYS (SISSA)
Tutorial - Learning from Noisy Sequential Observations
D. BRAUN (U. of Ulm)
Computational Models of Sensorimotor Learning and Decision-making - 1
Computational Models of Sensorimotor Learning and Decision-making - 2
Computational Models of Sensorimotor Learning and Decision-making - 3
D. HOFMANN (Emory U.)
Hands-on tutorial: Reinforcement Learning: theory
D. ZOCCOLAN (SISSA)
Tutorial - Introduction to the Visual System
E. ANNAVINI (SISSA)
Hands-on tutorial: Convolutional Neural Networks: Practicals
G. MATTEUCCI (SISSA)
Hands-on tutorial: Convolutional Neural Networks: Theory
J. DICARLO (MIT)
Reverse Engineering Human Vision 1: Where are the Neural Computations?
Reverse Engineering Human Vision 2: How Can We Model those Computations?
Reverse Engineering Human Vision 3: What Is Still Missing?
J. PETERS (Technische U. Darmstadt)
Robot Learning - 1
Robot Learning - 2
Robot Learning - 3
L. ROSASCO (Genoa U. and MIT)
Regularization in Learning Theory
Implicit Regularization
Regularization with (Random) Projections
M. ADORISIO (ICTP)
Hands-on tutorial: Reinforcement Learning: Practicals
M. PELILLO (U. Ca' Foscari, Venice)
Through the Philosopher’s Glass
N. TISHBY (The Hebrew University of Jerusalem)
Information Theory of Deep Learning - Rethinking Statistical Learning theory for Large-scale Learning
Information Theory of Deep Learning - The Special Role of Stochastic Gradient Descent and the Information Bottleneck Limit
Information Theory of Deep Learning - What Do the Layers of Deep Neural Networks Represent?
P. DAYAN (Max Planck Institute for Biological Cybernetics, Tuebingen)
Neural Reinforcement Learning 1: Prediction
Neural Reinforcement Learning 2: Choice
Neural Reinforcement Learning 3: The Self and the Other
R. ZECCHINA (U. Bocconi, Milan)
Typical Minima in Single-Layer and Multilayer Networks
The Central Role of Wide Flat Minima: Rarity, Geometrical Features and Accessibility
Loss Functions for Deep Learning
T. LATTIMORE (DeepMind, London)
Bandit Algorithms - 1
Bandit Algorithms - 2
Bandit Algorithms - 3

Organizers

Chris Mathys (SISSA), Davide Zoccolan (SISSA), Local Organiser: Antonio Celani

Co-sponsors