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Category Archives: news
Talk by prof. Byeng Dong YOUN
In developing our collaborations with experts and scholars around the globe, we were happy to welcome Dr. Byeng Dong YOUN, Professor of Mechanical and Aerospace Engineering at Seoul National University (SNU), the founder and CEO of OnePredict Inc. (onepredict.com), and the President of the Korean Society of Prognostics and Health Management (PHM). Prof. Youn presented his talk on Industry AI based Machine Intelligences for Industrial Digitalization and we discussed the challenges related to applying machine learning techniques in industrial monitoring.
Variational saccading
Ever tried to train a deep neural network over high resolution images taken by modern smartphone cameras or smart devices? The memory and inferential costs when working with inputs of such large dimensions (e.g. 4000x3000) increase rapidly and often prohibitively. Jason Ramapuram proposes a solution in his new paper "Variational Saccading: Efficient Inference for Large Resolution Images". He will present his idea at the BMVC conference in September this year but you don't have to wait, check out the preprint!
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Tagged BMVC conference, conference, Jason, Jason Ramapuram, paper, Variational saccading
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ML research on the move
Though ML labs may seem to outsiders as rather nerdy isolated groups, it is not our case! We are on the move, visiting and welcoming ML researchers, sometimes near and sometimes further away. Discussing, collaborating, making friends, having fun!
Lionel Blonde in AISTATS
AISTATS 2019 is well under way and Lionel Blonde is there to present his work on Sample-Efficient Imitation Learning via Generative Adversarial Nets. You can check his poster Th79 at the poster session tomorrow (Thursday, April 18, 13h30-16h30). He will be happy to explain how he improves upon GAIL by reducing the sample complexity by orders of magnitude. Go and speak to him!
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New machine learning courses
The HEG (HES-SO Geneva) opened a new Data Science branch in its Master's programe in Information Studies. Our group is responsible for the two major modules on machine learning spanning over two semesters (16 credits in total). Building a new machine learning course is a challenge we are happy to embrace. Continue reading
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NIPS 2018
Meet us at the NIPS workshops were we will be presenting the following works:
- Sample-Efficient Imitation Learning via Generative Adversarial Nets (Lionel Blondé, Alexandros Kalousis), in the Deep RL and in the Imitation Learning and its Challenges in Robotics workshops.
- Continual Classification Learning Using Generative Models (Frantzeska Lavda, Jason Ramapuram, Magda Gregorova, Alexandros Kalousis), in the Continual Learning workshop.
- Variational Saccading: Efficient Inference for Large Resolution Images (Jason Ramapuram, Maurits Diephuis, Russ Webb, Alexandros Kalousis), in the Bayesian Deep Learning workshop.
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Tagged Bayesian Deep Learning nips workshop, Continual Learning nips workshop, Deep RL nips workshop, Imitation Learning and its Challenges in Robotics nips workshop, NIPS, NIPS conference, NIPS2018
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The blog is live!
Read the blog to know about the latest endeavours of the DMML group!
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Welcome to Ye!
Warm welcomes to our new colleague, Ye Yuan, who started his PhD with us in September 2018 to work on our gait project. Ye has got a master's degree in Computer Science from the Institute of Computing Technology of the University of Chinese Academy of Science and has got some experience with NLP and stock market predictions. We hope Ye will be a good fit for our team and that he will enjoy his PhDing in Geneva, Switzerland. Continue reading
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Busy Monday, 12 November 2018
On Monday 12/November we will host a talk of Dr. Julien Mairal, Inria, Grenoble on: "Invariance and Stability to Deformations of Deep Convolutional Representations".
Later the same day, Magda will defend her thesis on "Sparse Learning for variable selection with structures and nonlinearities". Continue reading
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Lionel to speak at SMLD2018
Lionel Blondé will present his work on "Sample-Efficient Imitation Learning via Generative Adversarial Nets" at the 2018 edition of the Swiss Machine Learning Day. Continue reading
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