Writing

Google Scholar

Essays

  1. We Must Build AI at Full Pace & with Full Accountability →2026 · 7 min
  2. Wrapping up my journey scaling Software 2.0 development for AV ↗Clement Farabet, Medium, Nov 2022.

Papers

  1. Levels of AGI: Operationalizing Progress on the Path to AGI ↗Meredith Ringel Morris, Jascha Sohl-dickstein, Noah Fiedel, Tris Warkentin, Allan Dafoe, Aleksandra Faust, Clement Farabet, Shane Legg, ArXiv, Nov 2023.
  2. Active Learning for Deep Object Detection via Probabilistic Modeling ↗Jiwoong Choi, Ismail Elezi, Hyuk-Jae Lee, Clement Farabet, Jose M. Alvarez, ArXiv preprint, March 2021.
  3. Deep Active Learning for Object Detection with Mixture Density Networks ↗Ji-Woong Choi, Ismail Elezi, Hyuk-Jae Lee, Clement Farabet, Jose M. Alvarez, ICLR, 2021.
  4. Scalable Active Learning for Object Detection ↗Elmar Haussmann, Michele Fenzi, Kashyap Chitta, Jan Ivanecky, Hanson Xu, Donna Roy, Akshita Mittel, Nicolas Koumchatzky, Clement Farabet, Jose M. Alvarez, ArXiv preprint, Oct 2020.· Blog
  5. Training Data Distribution Search with Ensemble Active Learning ↗Kashyap Chitta, Jose M. Alvarez, Elmar Haussmann, Clement Farabet, ArXiv preprint, Sept 2019.
  6. Less is More: An Exploration of Data Redundancy with Active Dataset Subsampling ↗Kashyap Chitta, Jose M. Alvarez, Elmar Haussmann, Clement Farabet, ArXiv preprint, May 2019.
  7. Toward real-time indoor semantic segmentation using depth information ↗Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun, Journal of Machine Learning Research, 2014.
  8. Convolutional nets and watershed cuts for real-time semantic Labeling of RGBD videos ↗Camille Couprie, Clement Farabet, Laurent Najman, Yann LeCun, Journal of Machine Learning Research, 2014.
  9. Learning Hierarchical Features for Scene Labeling ↗Clement Farabet, Camille Couprie, Laurent Najman and Yann LeCun, IEEE Transactions on Pattern Analysis and Machine Intelligence, in press, 2013.
  10. Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers ↗C. Farabet, C. Couprie, L. Najman, Y. LeCun, in Proc. of the International Conference on Machine Learning (ICML'12), Edinburgh, Scotland, 2012.
  11. Comparison Between Frame-Constrained Fix-Pixel-Value and Frame-Free Spiking-Dynamic-Pixel ConvNets for Visual Processing ↗Clement Farabet, Rafael Paz, Jose Perez-Carrasco, Carlos Zamarreno, Alejandro Linares-Barranco, Yann LeCun, Eugenio Culurciello, T. Gotarredona and B. Linares-Barranco, Frontiers in Neuroscience, 2012.
  12. NeuFlow: Dataflow Vision Processing System-on-a-Chip ↗Phi-Hung Pham, Darko Jelaca, Clement Farabet, Berin Martini, Yann LeCun and Eugenio Culurciello, in IEEE International Midwest Symposium on Circuits and systems, IEEE MWSCAS, 2012, Boise, Idaho, USA.
  13. NeuFlow: A Runtime Reconfigurable Dataflow Processor for Vision ↗InvitedC. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello and Y. LeCun, in Proc. of the Fifth IEEE Workshop on Embedded Computer Vision (ECV'11 @ CVPR'11), IEEE, Colorado Springs, 2011. Invited Paper.
  14. Hardware Accelerated Convolutional Neural Networks for Synthetic Vision Systems ↗C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun and E. Culurciello, in International Symposium on Circuits and Systems (ISCAS'10), IEEE, Paris, 2010.
  15. Convolutional Networks and Applications in Vision ↗Y. LeCun, K. Kavukcuoglu and C. Farabet, in International Symposium on Circuits and Systems (ISCAS'10), IEEE, Paris, 2010.
  16. An FPGA-Based Stream Processor for Embedded Real-Time Vision with Convolutional Networks ↗C. Farabet, C. Poulet and Y. LeCun, in Proc. of the Fifth IEEE Workshop on Embedded Computer Vision (ECV'09 @ ICCV'09), IEEE, Kyoto, 2009.
  17. CNP: An FPGA-based Processor for Convolutional Networks ↗C. Farabet, C. Poulet, J. Y. Han, and Y. LeCun, in Proc. International Conference on Field Programmable Logic and Applications (FPL'09), IEEE, Prague, 2009.
  18. Visual Tracking and LIDAR Relative Positioning for Automated Launch and Recovery of an Unmanned Rotorcraft from Ships at Sea ↗M. Garratt, H. Pota, A. Lambert, S. E.-Maslin, and C. Farabet, Naval Engineers Journal, vol. 121, no. 2, pp. 99-110, June 2009.
Patents4 entries
  1. Training, testing, and verifying autonomous machines using simulated environments ↗Granted 2022Clement Farabet, John Zedlewski, Zachary Taylor, Greg Heinrich, Claire Delaunay, Daly Mark, Campbell Matthew, Curtis Beeson, Gary D. Hicok, Michael Brian Cox, Lebaredian Rev, Tamasi Tony, Auld David, applied 2019, granted 2022.
  2. Distributed Neural Network Training System ↗Applied 2022Shen; Yichun, Li Siyi, Wen Yuhong, Farabet Clement, applied 2022.
  3. Systems and methods for safe and reliable autonomous vehicles ↗Applied 2019Michael Alan Ditty, Gary D. Hicok, Jonathan Sweedler, Clement Farabet, Mohammed Abdulla Yousuf, Tai-Yuen Chan, Ram Ganapathi, Ashok Srinivasan, Michael Rod Truog, Karl Greb, John George Mathieson, Nister David, Kevin Flory, Daniel Stewart Perrin, Dan Hettena, applied 2019.
  4. Runtime reconfigurable dataflow processor with multi-port memory access module ↗Granted 2018Clement Farabet, Yann LeCun, applied 2012, granted 2018.

Book chapter

  1. Large-Scale FPGA-Based Convolutional Networks ↗C. Farabet, Y. LeCun, K. Kavukcuoglu, B. Martini, P. Akselrod, S. Talay, and E. Culurciello (2011). In R. Bekkerman, M. Bilenko, and J. Langford (Ed.), Scaling Up Machine Learning. Cambridge University Press, 2011.· Book

Theses

  1. Towards Real-Time Image Understanding with Convolutional Networks ↗C. Farabet, PhD (2013). PhD Thesis. Universite Paris-Est.
  2. Hardware implementation of a convolutional neural network – design of a neural processorC. Farabet, Master's thesis, INSA Lyon, France – New York University, New York, USA.
  3. Implementation of a tracking system within an fpgaC. Farabet, University of New South Wales at ADFA, Canberra, Australia, Technical Report, July 2007.
Workshops and talks12 entries
  1. NeuFlow: A Runtime Reconfigurable Dataflow Processor for VisionInvited talkC. Farabet, Y. LeCun, in Big Learning Workshop (@ NIPS'11), Sierra Nevada, Spain, 2011. Invited Talk.
  2. Torch7: A Matlab-like Environment for Machine Learning ↗R. Collobert, K. Kavukcuoglu, C. Farabet, in Big Learning Workshop (@ NIPS'11), Sierra Nevada, Spain, 2011.
  3. NeuFlow: A Runtime Reconfigurable Dataflow Processor for VisionC. Farabet, Y. LeCun, E. Culurciello, in Snowbird Learning Workshop, Fort Lauderdale FL, 2011.
  4. Hardware Accelerated Visual Attention AlgorithmP. Akselrod, F. Zhao, I. Derekli, C. Farabet, B. Martini, Y. LeCun and Eugenio Culurciello. In Proc. Conference on Information Sciences and Systems (CISS'11), IEEE, Baltimore, 2011.
  5. A Dataflow Processor for General Purpose VisionC. Farabet, P. Akselrod, B. Martini, K. Kavukcuoglu, B. Corda, S. Talay, E. Culurciello and Y. LeCun, presented at Neural Information Processing Systems (NIPS'10), Vancouver CA, December 2010.
  6. Building Heterogeneous Platforms for End-to-end Online Learning Based on Dataflow Computing DesignB. Corda, C. Farabet, M. Scoffier and Y. LeCun, in Workshop on Learning on Cores, Clusters and Clouds (LCCC @ NIPS'10), Whistler CA, December 2010.
  7. A Study of Parallel Computing for Machine Learning: Which Platform for Which ApplicationB. Corda, C. Farabet and Y. LeCun, presented at the 4th Annual Machine Learning Symposium at the New York Academy of Sciences, New York, October 2010.
  8. NeuFlow: a Vision Processor for Real-Time Object Categorization in Megapixel VideosC. Farabet, presented at AIPR Workshop, Washington DC, October 2010.
  9. NeuFlow: a Dataflow Computer for General Purpose VisionC. Farabet, presented at e-lab's Seminar Series, Yale University, October 2010.
  10. Bio-Inspired Processing for Ultra-Fast Object Categorization ↗C. Farabet, B. Martini, P. Akselrod, S. Talay, Y. LeCun and E. Culurciello, in High Performance Embedded Computing (HPEC'10), MIT Lincoln Laboratory, Lexington, 2010.
  11. An FPGA-based Processor for Convolutional NetworksC. Farabet, C. Poulet, J. Y. Han, and Y. LeCun, in Snowbird Learning Workshop, Clearwater FL, 2009.
  12. Visual Tracking and LIDAR Relative Positioning for Automated Launch and Recovery of an Unmanned Rotorcraft from Ships at SeaM. Garratt, H. Pota, A. Lambert, S. E.-Maslin, and C. Farabet. In ASNE Conf. on Launch and Recovery of Unmanned Vehicles From Surface Platforms 2008, Annapolis, May 2008. American Society of Naval Engineers.

Built entirely with Antigravity + Gemini Argon

Clément Farabet

  1. Home
  2. About
  3. Work
  4. Talks
  5. Writing
  6. After hours
  7. The essay7 min
Appearance