## [Oxford Internship] Final Report

My M1 internship report in pdf.

## [Oxford Internship] The Category of Event Structures

Beginning of my M1 internship at Oxford University: itâ€™ll have to do with Glynn Winskelâ€™s event structures

## TD: La catĂ©gorie $PCoh$

1. Le produit monoĂŻdal $\otimes$ de $PCoh$

## Cours 20: Probabilistic Coherence Spaces and Linear Logic

In Probabilistic Coherence Spaces, $đť’«(đť’ś)$ is closed under $\sup$

Globular Sets

## Lecture 8: Categories in 2-level type theory, Fibrations and co-Fibrations

Category in 2-level type theory

## [Final Coq Project] Coherence of Heytingâ€™s arithmetic

Coherence of Heytingâ€™s arithmetic in Coq

## TD10: Damier (checker)

NB: Boards are finite. Number of configurations:

## [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

## [Project] Memory Evolutive Neural Systems (MENS)

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

## Lab: Efficient balanced networks

Lecturer: Lyudmila Kushnir

## 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 Â  Â  Â  Â  Â

## Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

## Espace des types

Proposition: On se fixe $â„ł, \; A âŠ† M, \; \overline{x}, \; p = p(\overline{x})$ un ensemble de $â„’_A$-formules. Si $đť’© \succcurlyeq â„ł$, alors ...

## Elimination des quantificateurs pour ACF, Types

Teacher: Tomas Ibarlucia

Feuille 1

## Cours 2: RevĂŞtements

Ch 4. RevĂŞtements

NP, coNP

## Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

### Statistical Learning

Practical session 12: Summary

## Lecture 10: Summary

Summary of the class

## Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

R-CNN