Recent posts
[Cambridge Internship] Final Report
My ARPE internship report in pdf.
[Sydney Internship] Final Report
My M2 internship report in pdf.
Preuves et programmes
TD: La catégorie $PCoh$
1. Le produit monoïdal $\otimes$ de $PCoh$
Cours 20: Probabilistic Coherence Spaces and Linear Logic
In Probabilistic Coherence Spaces, $𝒫(𝒜)$ is closed under $\sup$
Homotopy Type Theory
Lecture 8: Cubical type theory
Globular Sets
Lecture 8: Categories in 2-level type theory, Fibrations and co-Fibrations
Category in 2-level type theory
Preuve formelle assistée par ordinateur
[Final Coq Project] Coherence of Heyting’s arithmetic
Coherence of Heyting’s arithmetic in Coq
TD10: Damier (checker)
NB: Boards are finite. Number of configurations:
Neuromodeling
[Project] Coherent Patterns of Activity from Chaotic Neural Networks
Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.
Lecture 6: Hodgkin-Huxley model
Cell membrane = semi-permeable
Machine Learning applied to Neuroscience
[Project] Memory Evolutive Neural Systems (MENS)
Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.
Lab: Efficient balanced networks
Lecturer: Lyudmila Kushnir
Neuro-robotique
Tutorial 4: Intent Recognition
Lab 4: Intent Recognition Younesse Kaddar, Alexandre Olech and Kexin Ren (Lecturer: Mohamed Chetouani)
[Final Project] Inferring Space from Sensorimotor Dependencies
Documentation Slides iPython notebook
Computational neuroscience
Exercise Sheet 6: integrate-and-Fire Neuron
Exercise Sheet 6: Integrate-and-Fire Neuron
Lecture 6: Biophysics of neurons
Teacher: Grégory Dumont
Théorie des modèles
Espace des types
Proposition: On se fixe $ℳ, \; A ⊆ M, \; \overline{x}, \; p = p(\overline{x})$ un ensemble de $ℒ_A$-formules. Si $𝒩 \succcurlyeq ℳ$, alors ...
Elimination des quantificateurs pour ACF, Types
Teacher: Tomas Ibarlucia
Topologie algébrique
Cours 2: Revêtements
Ch 4. Revêtements
Complexity
Exercises 12: Review
NP, coNP
Lecture 9: The PCP theorem
cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)
Statistical Learning
TP 11: PCA et $K$-Means
Practical session 11: PCA and K-Means
TD 12: Révisions
Practical session 12: Summary
Computer Vision
Final Summary: Dimensionality Reduction and Visualization of Representations
Final Summary for the final presentation
Optional TP: Calibration
Practical session: Calibration