Implementation
Thesis
Sunday Morning Insight
Other in-depth insights
- Wasserstein GAN / Towards Principled Methods for Training Generative Adversarial Networks
- Learning the structure of learning
- Continuing To Learn the Structure of Learning
- Controlling Light Transmission Through Highly Scattering Media Using Semi-Definite Programming as a Phase Retrieval Computation Method
- Understanding deep learning requires rethinking generalization
- In Texas, we just call them Neural Networks.
- An NlogN Parallel Fast Direct Solver for Kernel Matrices
- NIPS 2016 Tutorial: Generative Adversarial Networks / Learning in Implicit Generative Models
- FINN: A Framework for Fast, Scalable Binarized Neural Network Inference / Scaling Binarized Neural Networks on Reconfigurable Logic
- A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers
- Randomized Clustered Nystrom for Large-Scale Kernel Machines
Book
Job:
- Job: Postdoc, Research Associate in Data Science, UCL, London
- Jobs: Data Science Postdoctoral Fellows, Harvard University
- Job: Summer 2017 IBM Social Good Fellowship (Undergraduates, Graduates, NGOs)
- CSjobs: two postdoctoral researchers, "C-SENSE: Exploiting low dimensional signal models for sensing, computation and processing" and "CS for Radar and Electronic Surveillance", Edinburgh, Scotland
- CSJob: Postdoctoral Position Computational Optics and Optical Computing, LKB, ENS, Paris, France
Paris Machine Learning meetup
Videos:
- The #NIPS2016 Videos are out
- Saturday Morning Videos: Foundations of Machine Learning Boot Camp, Simons Institute, Berkeley
- Saturday Morning Videos: Fairness, Accountability, and Transparency in Machine Learning @NYU School of Law
- Saturday Morning Videos: Deep Learning Symposium, #NIPS2016
- Saturday Morning Video: Stan Conference 2017 video streaming
- Saturday Morning Video: Head, Torso and Tail - Performance for modeling real data, Alex Smola
- Videos and Slides: 6th MMDS Workshop on Algorithms for Modern Massive Data Sets
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