Introduction to Artificial Intelligence
Fall 2019, Prof. Gilles Louppe : https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec-all.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Fall 2019, Prof. Gilles Louppe : https://glouppe.github.io/info8006-introduction-to-ai/pdf/lec-all.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning
Professor Karl Friston - Frontiers publications.
https://loop.frontiersin.org/people/20407/overview
https://loop.frontiersin.org/people/20407/overview
Loop
Karl Friston
Karl Friston is a neuroscientist and authority on brain imaging. He invented statistical parametric mapping: SPM is an international standard for analysing imaging data and rests on the general linear model and random field theory (developed with Keith Worsley).…
Multiple PhD positions on machine learning with simulation and physics modeling of the world
Frantzeska Lavda [[email protected]]
Sent: 19 December 2019 14:11
To:
lhc-machinelearning-wg (Discussion group for machine learning at the LHC)
We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations.
The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (https://dmml.ch) as a research and teaching assistant at HES-SO, Geneva. The positions shall be filled in as soon as possible.
The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models.
In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.
Related literature:
- Battaglia, Peter, et al. "Interaction networks for learning about objects, relations and physics." Advances in neural information processing systems. 2016.
- Lionel Blondé, Alexandros Kalousis "Sample-Efficient Imitation Learning via Generative Adversarial Nets." AISTATS 2019: 3138-3148
- Narayanaswamy, Siddharth, et al. "Learning disentangled representations with semi-supervised deep generative models." Advances in Neural Information Processing Systems. 2017.
We seek strongly motivated candidates prepared to dedicate to high quality research in the above domains for a number of years (the expected time to PhD graduation is 4-5 years). The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with strong background in as many as possible (but at least some) of these: machine learning, probability and statistical modeling, mathematical optimization, programming and software development (preferably Pytorch and/or Tensorflow).
If interested, please send the following to [email protected]
- academic CV (max 2 pages)
- academic transcript of the study results
- one page motivation letter explaining why the candidate is suitable for the position
- 500 word research proposal on one of the topics described above
- contact details of three referees (do not send reference letters)
The applications will be processed as they come as of now until the positions are filled. The status of the openings will be update here: https://dmml.ch/recruitment/
In case of any further questions, please contact [email protected].
Frantzeska Lavda [[email protected]]
Sent: 19 December 2019 14:11
To:
lhc-machinelearning-wg (Discussion group for machine learning at the LHC)
We have several PhD openings in machine learning research for exploring methods to combine learning with process-driven modeling and simulations.
The successful candidate will enroll as a PhD student in the Computer Science department of the University of Geneva (under the co-direction of myself and Prof. Stephane Marchand-Maillet) and, at the same time, will become a member of the Data Mining and Machine Learning group (https://dmml.ch) as a research and teaching assistant at HES-SO, Geneva. The positions shall be filled in as soon as possible.
The interaction and cooperation between a simulator and a machine learning model can be exploited in a number of areas where data are expensive or difficult to obtain, and/or where domain knowledge within the process-driven models can back the inductive biases factored into the machine learning models.
In the medical domain, machine learning methods can be combined with neuromechanical simulators to develop models of human locomotion that shall support critical medical decisions related to surgical interventions treating pathological gait patterns. In industrial manufacturing, simulations and physical modeling of realistic or extreme operational conditions can support the learning of rare faulty behaviours in order to trigger early alerts. In chemoinformatics, an external system (e.g. RDKit) can provide relevant constraints for generating valid new molecules with specific required characteristics.
Related literature:
- Battaglia, Peter, et al. "Interaction networks for learning about objects, relations and physics." Advances in neural information processing systems. 2016.
- Lionel Blondé, Alexandros Kalousis "Sample-Efficient Imitation Learning via Generative Adversarial Nets." AISTATS 2019: 3138-3148
- Narayanaswamy, Siddharth, et al. "Learning disentangled representations with semi-supervised deep generative models." Advances in Neural Information Processing Systems. 2017.
We seek strongly motivated candidates prepared to dedicate to high quality research in the above domains for a number of years (the expected time to PhD graduation is 4-5 years). The candidate should have (or be close to obtaining) a Master's degree or equivalent in computer science, statistics, applied mathematics, electrical engineering or other related field with strong background in as many as possible (but at least some) of these: machine learning, probability and statistical modeling, mathematical optimization, programming and software development (preferably Pytorch and/or Tensorflow).
If interested, please send the following to [email protected]
- academic CV (max 2 pages)
- academic transcript of the study results
- one page motivation letter explaining why the candidate is suitable for the position
- 500 word research proposal on one of the topics described above
- contact details of three referees (do not send reference letters)
The applications will be processed as they come as of now until the positions are filled. The status of the openings will be update here: https://dmml.ch/recruitment/
In case of any further questions, please contact [email protected].
Data mining and machine learning group, Geneva
About - Data mining and machine learning group, Geneva
We are a machine learning reserach lab based in Geneva. In our research, we focus on various modern ML problems including deep and reinforcement learning.
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data
https://eng.uber.com/generative-teaching-networks/
https://eng.uber.com/generative-teaching-networks/
Uber Blog
Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data | Uber Blog
Generative Teaching Networks (GANs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.
SynSin: End-to-end View Synthesis from a Single Image
Wiles et al.: https://arxiv.org/abs/1912.08804
#ArtificialIntelligence #DeepLearning #MachineLearning
Wiles et al.: https://arxiv.org/abs/1912.08804
#ArtificialIntelligence #DeepLearning #MachineLearning
arXiv.org
SynSin: End-to-end View Synthesis from a Single Image
Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D scene from a...
2019 has been a crazy year with all the AI hype but lets take a look at what all happened this year.
👉 AI Research went crazy. Between 1998 and 2018, there’s been a 300% increase in the publication of peer-reviewed papers on AI.
👉 Attendance at conferences went crazy too, for eg. NeurIPS, got some 13,500 attendees this year, up 800% from 2012.
👉 Education too bumped up, a lot of folks took up MSc / PhD with something in Machine Learning
👉 USA still leads in AI, no matter what other countries say
👉 AI algorithms are becoming cheaper and mainstream
👉 self driving vehicles market is coming of age and raking in a lot of investments
Download Full Stanford Report: https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf
👉 AI Research went crazy. Between 1998 and 2018, there’s been a 300% increase in the publication of peer-reviewed papers on AI.
👉 Attendance at conferences went crazy too, for eg. NeurIPS, got some 13,500 attendees this year, up 800% from 2012.
👉 Education too bumped up, a lot of folks took up MSc / PhD with something in Machine Learning
👉 USA still leads in AI, no matter what other countries say
👉 AI algorithms are becoming cheaper and mainstream
👉 self driving vehicles market is coming of age and raking in a lot of investments
Download Full Stanford Report: https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf
Report on an IPAM long program on ML for Physics and Physics for ML (which I co-organized), written by some of the participants.
https://www.ipam.ucla.edu/news/white-paper-machine-learning-for-physics-and-the-physics-of-learning/
https://www.ipam.ucla.edu/news/white-paper-machine-learning-for-physics-and-the-physics-of-learning/
IPAM
White Paper: Machine Learning for Physics and the Physics of Learning - IPAM
This white paper is an outcome of IPAM’s fall 2019 long program, Machine Learning for Physics and the Physics of Learning. During the last couple of decades advances in artificial intelligence and machine learning (ML) have revolutionized many application…
Yoshua Bengio
@ArtificialIntelligenceArticles
I am currently recruiting a postdoc for the visualizing climate change project at Mila (https://mila.quebec/en/ai-society/visualizing-climate-change/). The ideal candidate would have a strong background in ML/DL, specifically in Computer Vision and generative models, with a clear and strong interest in the environment and climate change in particular. Please apply on the mila website, specifying your interest for this project in the application. Application form here: https://docs.google.com/forms/d/1pIfuSBORzKRLBEG8zIdJoEwwlsfvvKi2D2ENFPcbX5c/viewform?edit_requested=true#start=invite
Sasha Lu
@ArtificialIntelligenceArticles
@ArtificialIntelligenceArticles
I am currently recruiting a postdoc for the visualizing climate change project at Mila (https://mila.quebec/en/ai-society/visualizing-climate-change/). The ideal candidate would have a strong background in ML/DL, specifically in Computer Vision and generative models, with a clear and strong interest in the environment and climate change in particular. Please apply on the mila website, specifying your interest for this project in the application. Application form here: https://docs.google.com/forms/d/1pIfuSBORzKRLBEG8zIdJoEwwlsfvvKi2D2ENFPcbX5c/viewform?edit_requested=true#start=invite
Sasha Lu
@ArtificialIntelligenceArticles
Mila
This Climate Does Not Exist - Mila
How Far is Too Far? | The Age of A.I.
https://www.youtube.com/watch?v=UwsrzCVZAb8&feature=youtu.be
https://www.youtube.com/watch?v=UwsrzCVZAb8&feature=youtu.be
YouTube
How Far is Too Far? | The Age of A.I.
Can A.I. make music? Can it feel excitement and fear? Is it alive? Will.i.am and Mark Sagar push the limits of what a machine can do. How far is too far, and how much further can we go?
The Age of A.I. is a 8 part documentary series hosted by Robert Downey…
The Age of A.I. is a 8 part documentary series hosted by Robert Downey…
Look at Tackling Climate Change with ML 2 on SlidesLive! #NeurIPS2019 climate change workshop, including a panel with our very own Andrew Ng, Yoshua Bengio, Jeff Dean, Carla Gomes, and Lester Mackey
https://slideslive.com/38922107/tackling-climate-change-with-ml-2
https://slideslive.com/38922107/tackling-climate-change-with-ml-2
SlidesLive
Leveraging digitalization for urban solutions in the Anthropocene
Online Deep Reinforcement Learning for Autonomous UAV Navigation and Exploration of Outdo... https://arxiv.org/abs/1912.05684
VIBE: Video Inference for Human Body Pose and Shape Estimation. https://arxiv.org/abs/1912.05656
arXiv.org
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior. Despite progress on single-image 3D pose and shape estimation, existing video-based state-of-the-art methods fail to produce accurate and...
Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection. https://arxiv.org/abs/1912.05651
arXiv.org
Bayesian Variational Autoencoders for Unsupervised...
Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major...
Neural Voice Puppetry: Audio-driven Facial Reenactment. https://arxiv.org/abs/1912.05566
arXiv.org
Neural Voice Puppetry: Audio-driven Facial Reenactment
We present Neural Voice Puppetry, a novel approach for audio-driven facial video synthesis. Given an audio sequence of a source person or digital assistant, we generate a photo-realistic output...
XGBoost: An Intuitive Explanation
Ashutosh Nayak : https://towardsdatascience.com/xgboost-an-intuitive-explanation-88eb32a48eff
#MachineLearning #DataScience #RandomForest #Xgboost #DecisionTree
Ashutosh Nayak : https://towardsdatascience.com/xgboost-an-intuitive-explanation-88eb32a48eff
#MachineLearning #DataScience #RandomForest #Xgboost #DecisionTree
What can the brain teach us about building artificial intelligence?
Dileep George : https://arxiv.org/abs/1909.01561v1
#ArtificialIntelligence #Brain #Cognition
Dileep George : https://arxiv.org/abs/1909.01561v1
#ArtificialIntelligence #Brain #Cognition