Postdoc position at Stanford
Professor Stefano Ermon is seeking an outstanding researcher for a postdoctoral position at Stanford (https://cs.stanford.edu/~ermon/website/). The postdoc will carry out Machine Learning research on a broad range of topics, including learning with limited supervision, generative models, and imitation learning. We welcome applications from candidates with diverse educational backgrounds.
Required qualifications:
A Ph.D. (completed by start of employment) in Computer Science, or a relevant area
Publication record in top Machine Learning conferences
Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
Duration: This is a one-year position with the expectation of renewal for additional years conditional on performance.
To apply: Applicants should send their C.V. and research statement to [email protected]. Review of applications will begin immediately after and will continue until the position is filled.
Stanford University is an Equal Opportunity, Affirmative Action Educational Institution and Employer, Title IX University. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by the law. Stanford University is an E-Verify Employer.
Professor Stefano Ermon is seeking an outstanding researcher for a postdoctoral position at Stanford (https://cs.stanford.edu/~ermon/website/). The postdoc will carry out Machine Learning research on a broad range of topics, including learning with limited supervision, generative models, and imitation learning. We welcome applications from candidates with diverse educational backgrounds.
Required qualifications:
A Ph.D. (completed by start of employment) in Computer Science, or a relevant area
Publication record in top Machine Learning conferences
Experience with deep learning frameworks (e.g., TensorFlow, PyTorch)
Duration: This is a one-year position with the expectation of renewal for additional years conditional on performance.
To apply: Applicants should send their C.V. and research statement to [email protected]. Review of applications will begin immediately after and will continue until the position is filled.
Stanford University is an Equal Opportunity, Affirmative Action Educational Institution and Employer, Title IX University. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any other characteristic protected by the law. Stanford University is an E-Verify Employer.
cs.stanford.edu
Ermon Group
website description
Improving Deep Neuroevolution via Deep Innovation Protection
Sebastian Risi and Kenneth O. Stanley : https://arxiv.org/abs/2001.01683
#ArtificialIntelligence #DeepLearning #Neuroevolution
Sebastian Risi and Kenneth O. Stanley : https://arxiv.org/abs/2001.01683
#ArtificialIntelligence #DeepLearning #Neuroevolution
New Deep Learning Baseline for Image Classification called FrequentNet just got released!
Paper: https://arxiv.org/pdf/2001.01034.pdf
The authors generalize the idea from the method called ”PCANet” (Chan et al., 2015) to achieve a new baseline deep learning model for image classification. Instead of using principal component vectors as the filter vector in ”PCANet”.
Paper: https://arxiv.org/pdf/2001.01034.pdf
The authors generalize the idea from the method called ”PCANet” (Chan et al., 2015) to achieve a new baseline deep learning model for image classification. Instead of using principal component vectors as the filter vector in ”PCANet”.
Artificial Intelligence for Social Good: A Survey
Zheyuan Ryan Shi, Claire Wang, Fei Fang : https://arxiv.org/abs/2001.01818
#AI4SG #ArtificialIntelligence #AIGovernance
Zheyuan Ryan Shi, Claire Wang, Fei Fang : https://arxiv.org/abs/2001.01818
#AI4SG #ArtificialIntelligence #AIGovernance
Data project checklist
By Jeremy Howard : https://www.fast.ai/2020/01/07/data-questionnaire/
#ArtificialIntelligence #DataScience #MachineLearning
By Jeremy Howard : https://www.fast.ai/2020/01/07/data-questionnaire/
#ArtificialIntelligence #DataScience #MachineLearning
Connections between Support Vector Machines, Wasserstein distance and gradient-penalty GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
Blog : https://ajolicoeur.wordpress.com/MaximumMarginGAN
Code : https://github.com/AlexiaJM/MaximumMarginGANs
#DeepLearning #SupportVectorMachines #GANs
Alexia Jolicoeur-Martineau and Ioannis Mitliagkas : https://arxiv.org/abs/1910.06922
Blog : https://ajolicoeur.wordpress.com/MaximumMarginGAN
Code : https://github.com/AlexiaJM/MaximumMarginGANs
#DeepLearning #SupportVectorMachines #GANs
Alexia Jolicoeur-Martineau
Connections between SVMs, Wasserstein distance and GANs
Check out my new paper entitled “Support Vector Machines, Wasserstein’s distance and gradient-penalty GANs are connected”! 😸 In this paper, we explain how one can derive SVMs and …
How neural networks find generalizable solutions: Self-tuned annealing in deep learning
Yu Feng and Yuhai Tu : https://arxiv.org/abs/2001.01678
#ArtificialIntelligence #MachineLearning #SelfOrganizingSystem
Yu Feng and Yuhai Tu : https://arxiv.org/abs/2001.01678
#ArtificialIntelligence #MachineLearning #SelfOrganizingSystem
Lucid
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab
A collection of infrastructure and tools for research in neural network interpretability : https://github.com/tensorflow/lucid
#Tensorflow #Interpretability #Visualization #MachineLearning #Colab
GitHub
GitHub - tensorflow/lucid: A collection of infrastructure and tools for research in neural network interpretability.
A collection of infrastructure and tools for research in neural network interpretability. - tensorflow/lucid
Cell-specific non-canonical amino acid labelling identifies changes in the de novo proteome during memory formation
https://elifesciences.org/articles/52990
https://elifesciences.org/articles/52990
eLife
Cell-specific non-canonical amino acid labelling identifies changes in the de novo proteome during memory formation
Quantitative de novo proteomics paired with in vivo cell-specific non-canonical amino acid labelling identified several spatial long-term memory-induced changes in protein synthesis in hippocampal neurons.
Multi-Graph Transformer for Free-Hand Sketch Recognition
Xu et al.: https://arxiv.org/abs/1912.11258
#ArtificialIntelligence #DeepLearning #Transformer
Xu et al.: https://arxiv.org/abs/1912.11258
#ArtificialIntelligence #DeepLearning #Transformer
Uber Open-Sourced ‘Manifold’: A Visual Debugging Tool for Machine Learning
Github: https://github.com/uber/manifold
Paper (2018): https://arxiv.org/pdf/1808.00196.pdf
Github: https://github.com/uber/manifold
Paper (2018): https://arxiv.org/pdf/1808.00196.pdf
Advanced Deep Learning Course, by DeepMind @ArtificialIntelligenceArticles
https://www.youtube.com/watch?v=eMIcjYhUdCY
https://www.youtube.com/watch?v=eMIcjYhUdCY
Decrappifying brain images with deep learning
https://www.eurekalert.org/pub_releases/2020-01/uota-dbi010820.php
https://www.eurekalert.org/pub_releases/2020-01/uota-dbi010820.php
EurekAlert!
Decrappifying brain images with deep learning
To understand brain functions, it is necessary to first map how different cells and cell parts interact in three-dimensions. Doing so with existing equipment and methods has been a challenge. Researchers from the Salk Institute developed an approach using…
The year in AI: 2019 ML/AI advances recap https://medium.com/@xamat/the-year-in-ai-2019-ml-ai-advances-recap-c6cc1d902d5
Some of the brightest minds in #AI express their hopes for 2020🔝
Good read https://blog.deeplearning.ai/blog/the-batch-happy-new-year-hopes-for-ai-in-2020-yann-lecun-kai-fu-lee-anima-anandkumar-richard-socher
Good read https://blog.deeplearning.ai/blog/the-batch-happy-new-year-hopes-for-ai-in-2020-yann-lecun-kai-fu-lee-anima-anandkumar-richard-socher
Lessons from Archives: Strategies for Collecting Sociocultural Data in Machine Learning
Eun Seo Jo and Timnit Gebru : https://arxiv.org/abs/1912.10389
#MachineLearning #ArtificialIntelligence #Society
Eun Seo Jo and Timnit Gebru : https://arxiv.org/abs/1912.10389
#MachineLearning #ArtificialIntelligence #Society
Did you know that now it is possible to search for datasets just like searching for images in Google? This makes easier than ever the searching of data to train our machine learning methods.
PS: Remember that as a good practice in data science you always have to clean and prepare any dataset before using it!
https://toolbox.google.com/datasetsearch
#datascience
#machinelearning
PS: Remember that as a good practice in data science you always have to clean and prepare any dataset before using it!
https://toolbox.google.com/datasetsearch
#datascience
#machinelearning
CIS professor and arXiv.org founder receives physics award
Paul Ginsparg, Ph.D., professor of physics and information science, founder of arXiv, has been named the recipient of the American Institute of Physics 2020 Karl Taylor Compton Medal for Leadership in Physics.
He deserved the recognition!
https://news.cornell.edu/stories/2020/01/cis-professor-and-arxiv-founder-receives-physics-award
Paul Ginsparg, Ph.D., professor of physics and information science, founder of arXiv, has been named the recipient of the American Institute of Physics 2020 Karl Taylor Compton Medal for Leadership in Physics.
He deserved the recognition!
https://news.cornell.edu/stories/2020/01/cis-professor-and-arxiv-founder-receives-physics-award
Cornell Chronicle
Ginsparg, Ph.D. ’81, arXiv founder, receives physics award | Cornell Chronicle
Paul Ginsparg, Ph.D. ’81, professor of physics and information science, is the recipient of the American Institute of Physics 2020 Karl Taylor Compton Medal for Leadership in Physics.