Neural Offset Min-Sum Decoding
L. Lugosch and W. J. Gross, "Neural offset min-sum decoding," 2017 IEEE International Symposium on Information Theory (ISIT), Aachen, 2017, pp. 1361-1365.
CV
Education
- B.Eng. in Computer Engineering + Linguistics, McGill University, 2015
- M.Eng. in Electrical Engineering, McGill University, 2018
- Ph.D in Version Control Theory, GitHub University, 2018 (expected)
Work experience
- Summer 2015: Research Assistant
- Github University
- Duties included: Tagging issues
- Supervisor: Professor Git
- Fall 2015: Research Assistant
- Github University
- Duties included: Merging pull requests
- Supervisor: Professor Hub
Skills
- Skill 1
- Skill 2
- Sub-skill 2.1
- Sub-skill 2.2
- Sub-skill 2.3
- Skill 3
Publications
Deep Learning Methods for Improved Decoding of Linear Codes
E. Nachmani, E. Marciano, L. Lugosch, W. J. Gross, D. Burshtein and Y. Be’ery, "Deep learning methods for improved decoding of linear codes," in IEEE Journal of Selected Topics in Signal Processing, special issue on "Machine Learning for Cognition in Radio Communications and Radar", vol. 12, no. 1, pp. 119-131, Feb. 2018.
Learning Algorithms for Error Correction
L. Lugosch, “Learning algorithms for error correction”, Masters thesis, McGill University, 2018.
Tone Recognition Using Lifters and CTC
L. Lugosch and V. S. Tomar, “Tone recognition using lifters and CTC”, Interspeech, Hyderabad, India, pp. 2305-2309, September 2018.
Learning from the Syndrome
L. Lugosch, W. J. Gross, "Learning from the syndrome," in Asilomar Conference on Signals, Systems, and Computers, special session on "Machine Learning for Wireless Systems", Oct. 2018.
DONUT: CTC-based Query-by-Example Keyword Spotting
L. Lugosch, S. Myer, and V. S. Tomar, “DONUT: CTC-based query-by-example keyword spotting”, NeurIPS Workshop on Interpretability and Robustness in Audio, Speech, and Language, Montreal, Canada, December 2018.
Speech Model Pre-training for End-to-End Spoken Language Understanding
L. Lugosch, M. Ravanelli, P. Ignoto, V. S. Tomar, Y. Bengio, “Speech Model Pre-training for End-to-End Spoken Language Understanding”, Interspeech, September 2019.
Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models
L. Lugosch, B. Meyer, D. Nowrouzezahrai, M. Ravanelli, “Using Speech Synthesis to Train End-to-End Spoken Language Understanding Models”, ICASSP 2020.
Surprisal-Triggered Conditional Computation with Neural Networks
L. Lugosch, D. Nowrouzezahrai, B. H. Meyer, “Surprisal-Triggered Conditional Computation with Neural Networks”, arXiv, 2020.
Talks
Teaching
Service and leadership
- Currently signed in to 43 different slack teams