In this page, you will find all the previous reading group session.
2022 Q2: 30/05/22

Tiny ML : Machine Learning for embedded systems by Yanis Slides here

RPi cluster & Deep Learning by Fatou Kiné Slides here

Nvidia Jetson Nano by Nicolas Slides here
2022 Q2: 28/03/22

Sparse matrix multiplication with pytorch and Cuda by Nicolas and Yanis (14h)

Inference using tflite with larq by Fatou (14h30)
Slides here 
Social computing : NLP and community detection by Matthieu and Paul (15h) Slides here

Greedy decentralized optimization for deep learning by Simon (16h00)

Convergence of MH algorithm for deep learning by Jordy (16h30)
2022 Q1: 24/01/22
 Binarization of Neural Networks by Fatou Kiné Sow & Matthieu François
[1] « XNORNet: ImageNet Classification Using Binary Convolutional Neural Networks », M. Rastegari, V. Ordonez, J. Redmon, A. Farhadi. See the full article here.
[2] « XNORNet++: Improved binary neural networks » , A. Bulat, G. Tzimiropoulos. See the full article here.
[3] « BinaryConnect: Training Deep Neural Networks with binary weights during propagations », M. Courbariaux, Y. Bengio, JP. David. See the full article here.
[4] « Binarized Neural Networks: Training Neural Networks with Weights and Activations Constrained to +1 or −1 », M. Courbariaux, I. Hubara, D. Soudry, R. ElYaniv, Y. Bengio. See the full article here.
 Early Exit and Device to Cloud by Simon Lebeaud
For more details, see :
[1] « Multiscale dense networkds for resource efficient image classification » , G. Huang, D. Chen,T. Li, F. Wu L. van der Maaten, K. Weinberger. See the full article here.
[2] « SPINN : Synergistic Progressive Inference of Neural Networks over device and cloud », S. Laskaridis, S. I. Venieris, M. Almeida, I. Leontiadis, N. D. Lane. See the full article here.
 Pruning by Nicolas Tirel & Yanis Chaigneau
See the Jupyternotebook here.
[1] « Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment », M. C. Mozer et P. Smolensky, 1988. See the complete article.
[2] « SNIP: Singleshot Network Pruning based on Connection Sensitivity », N. Lee, T. Ajanthan, P. H. S. Torr. See the full article here.
[3] « Progressive Skeletonization: Trimming more fat from a network at initialization », P. de Jorge, A. Sanyal, H. S. Behl, P. H. S. Torr, G. Rogez, et P. K. Dokania, March 2021. See the full article here.
 Pruning with budget and Regularization : Jordy Palafox [1] « Deep Rewiring: Training very sparse deep networks », G. Bellec, D. Kappel, W. Maass, R. Legenstein. See the full article here.
[2] « Statistical guarantees for regularized neural networks », M. Taheri, F. Xie, J. Lederer. See the full article here.
2021 Q4: 29/10/2021
 Metrical Task System, Online Learning and Power Management by Matthieu François :
[1] « Online Learning and the Metrical Task System Problem », A. Blum, C. Burch. See the full article here.
[2] « Online Strategies for Dynamic Power Management in Systems with Multiple PowerSaving States », S. Irani, S. Shukla, R. Gupta. See the full article.
 Metrical Task System and Kserver problem by Jordy Palafox :
[1] « An Optimal OnLine Algorithm for Metrical Task System », A. Borodin, N. Linial, M.E. Saks. See the full article here.
[2] « Competitive Algorithms for Server Problems », M.S. Manasse, L.A. McGeoch, D.D. Sleator. Journal of algrithms 11, 208230 (1990). See the full article here.
Contact
You want to join the team ? Feel free to contact us if you want to contribute: contact Paul or Sébastien