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Blog Post number 4
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Blog Post number 1
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portfolio
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publications
Low-dimensional dynamics of encoding and learning in recurrent neural networks
Stefan Horoi, Victor Geadah, Guillaume Lajoie, Guy Wolf
Proceedings of the 33rd Canadian Conference on Artificial Intelligence (CAIAC), Springer’s LNCS vol. 12109, 2020
Visualizing High-Dimensional Trajectories on the Loss-Landscape of ANNs
Stefan Horoi*, Jessie Huang*, Guy Wolf†, Smita Krishnaswamy†
Poster presentation, DLIG workshop at NeurIPS, 2020
Top-down optimization recovers biological coding principles of single-neuron adaptation in RNNs
Victor Geadah, Stefan Horoi, Giancarlo Kerg, Guillaume Lajoie, Guy Wolf
Poster presentation, CoSyNe, 2022
Goal-driven optimization of single-neuron properties in artificial networks reveals regularization role of neural diversity and adaptation
Victor Geadah, Stefan Horoi, Giancarlo Kerg, Guillaume Lajoie, Guy Wolf
On the Inadequacy of CKA as a Measure of Similarity in Deep Learning
MohammadReza Davari*, Stefan Horoi*, Amine Natik, Guillaume Lajoie, Guy Wolf†, Eugene Belilovsky†
Exploring the Geometry and Topology of Neural Network Loss Landscapes
Stefan Horoi*, Jessie Huang*, Bastian Rieck, Guillaume Lajoie, Guy Wolf†, Smita Krishnaswamy†
Proceedings of the 20th Symposium on Intelligent Data Analysis (IDA), Springer’s LNCS vol. 13205, 2022
We presented a novel way of sampling the loss landscape of artificial neural networks and use manifold learning and computational homology to study the sampled data. We found that the extracted geometrical and topological features of the loss landscapes hold meaningful information about the model’s ability to generalize.
talks
Talk 1 on Relevant Topic in Your Field
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Tutorial 1 on Relevant Topic in Your Field
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Talk 2 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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