Greate paper on controllable speach generation
https://google.github.io/tacotron/publications/semisupervised_generative_modeling_for_controllable_speech_synthesis/
https://google.github.io/tacotron/publications/semisupervised_generative_modeling_for_controllable_speech_synthesis/
Multiple AI/ML Postdoc and Research Scientist Positions
The Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University seeks applicants for multiple postdoc and research-scientist positions in the areas of machine learning, artificial intelligence, and related fields.
Applicants are particularly sought with interest and expertise in the following sub-areas:
Explainable AI - with emphasis on reinforcement learning and computer vision
Machine Common Sense - with emphasis on combining DNNs with symbolic methods
Robust AI - with emphasis on anomaly detection and robust reinforcement learning
The CoRIS institute at Oregon State University in Corvallis, Oregon contains a team of more than 25 faculty and 180 graduate students working across most areas of artificial intelligence and robotics. Our researchers regularly publish in top-tier venues and value high-quality collaborative research, both fundamental and applied. The positions have flexible starting dates, up to a 3 year duration, and competitive salaries and benefits.
Interested applicants should send a detailed CV with expression of interest to Dr. Alan Fern <[email protected]>, Dr. Thomas Dietterich <[email protected]>, and Dr. Fuxin Li <[email protected]> with the subject “Postdoc Application”. There is no fixed application deadlines and applications will be considered until the positions are filled.
Qualification:
A PhD in computer science, electrical engineering, statistics, mathematics, or related fields
A strong research record demonstrated by relevant publications in top conferences/journals
Strong communication skills and fluency in English
Highly-motivated, creative, and collaborative personality
The Collaborative Robotics and Intelligent Systems (CoRIS) Institute at Oregon State University seeks applicants for multiple postdoc and research-scientist positions in the areas of machine learning, artificial intelligence, and related fields.
Applicants are particularly sought with interest and expertise in the following sub-areas:
Explainable AI - with emphasis on reinforcement learning and computer vision
Machine Common Sense - with emphasis on combining DNNs with symbolic methods
Robust AI - with emphasis on anomaly detection and robust reinforcement learning
The CoRIS institute at Oregon State University in Corvallis, Oregon contains a team of more than 25 faculty and 180 graduate students working across most areas of artificial intelligence and robotics. Our researchers regularly publish in top-tier venues and value high-quality collaborative research, both fundamental and applied. The positions have flexible starting dates, up to a 3 year duration, and competitive salaries and benefits.
Interested applicants should send a detailed CV with expression of interest to Dr. Alan Fern <[email protected]>, Dr. Thomas Dietterich <[email protected]>, and Dr. Fuxin Li <[email protected]> with the subject “Postdoc Application”. There is no fixed application deadlines and applications will be considered until the positions are filled.
Qualification:
A PhD in computer science, electrical engineering, statistics, mathematics, or related fields
A strong research record demonstrated by relevant publications in top conferences/journals
Strong communication skills and fluency in English
Highly-motivated, creative, and collaborative personality
COMPSCI 282BR - Interpretability and Explainability in Machine Learning
Instructor : Hima Lakkaraju - https://interpretable-ml-class.github.io
#interpretability #artificialintelligence #machinelearning
Instructor : Hima Lakkaraju - https://interpretable-ml-class.github.io
#interpretability #artificialintelligence #machinelearning
A Guide for Ethical Data Science
A collaboration between the Royal Statistical Society (RSS) and the Institute and Faculty of Actuaries (IFoA) : https://www.statslife.org.uk/news/4292-rss-and-ifoa-publish-new-ethical-guidance-on-data-science
#datascience #ethics #society
A collaboration between the Royal Statistical Society (RSS) and the Institute and Faculty of Actuaries (IFoA) : https://www.statslife.org.uk/news/4292-rss-and-ifoa-publish-new-ethical-guidance-on-data-science
#datascience #ethics #society
https://www.wired.com/story/ai-pioneer-algorithms-understand-why/
https://t.iss.one/ArtificialIntelligenceArticles
https://t.iss.one/ArtificialIntelligenceArticles
WIRED
An AI Pioneer Wants His Algorithms to Understand the 'Why'
Deep learning is good at finding patterns in reams of data, but can't explain how they're connected. Turing Award winner Yoshua Bengio wants to change that.
How Does Huawei Rise to Core AI Challenges?
https://www.cio.com/article/3444197/how-does-huawei-rise-to-core-ai-challenges.html
https://www.cio.com/article/3444197/how-does-huawei-rise-to-core-ai-challenges.html
CIO
How Does Huawei Rise to Core AI Challenges?
According to an analysis released by OpenAI, the demand for computing power has increased by more than 300,000 times in the six years after 2012. It grows by about factor of 10 each year, far exceeding the pace set by Moore's Law.
"I'm sorry Dave, I'm afraid I can't do that" Deep Q-learning from forbidden action. https://arxiv.org/abs/1910.02078
Mapping (Dis-)Information Flow about the MH17 Plane Crash. https://arxiv.org/abs/1910.01363
Perturbations are not Enough: Generating Adversarial Examples with Spatial Distortions. https://arxiv.org/abs/1910.01329
arXiv.org
Perturbations are not Enough: Generating Adversarial Examples with...
Deep neural network image classifiers are reported to be susceptible to
adversarial evasion attacks, which use carefully crafted images created to
mislead a classifier. Recently, various kinds of...
adversarial evasion attacks, which use carefully crafted images created to
mislead a classifier. Recently, various kinds of...
"At least 40% of startups in Europe that claim to use AI are lying" - Verge
Read Article: https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report
Read this interesting 150 page report by MMC group: https://www.mmcventures.com/wp-content/uploads/2019/02/The-State-of-AI-2019-Divergence.pdf
Read Article: https://www.theverge.com/2019/3/5/18251326/ai-startups-europe-fake-40-percent-mmc-report
Read this interesting 150 page report by MMC group: https://www.mmcventures.com/wp-content/uploads/2019/02/The-State-of-AI-2019-Divergence.pdf
The Verge
Forty percent of ‘AI startups’ in Europe don’t actually use AI, claims report
Companies want to take advantage of the AI hype.
WHAT’S STATE OF THE ART IN AUTOML IN 2019?
https://www.topbots.com/automl-state-of-the-art-2019/
https://www.topbots.com/automl-state-of-the-art-2019/
TOPBOTS
What's State Of The Art In AutoML in 2019?
More and more industries and organizations are leveraging artificial intelligence to delight customers and cut through the competition. However, development and deployment of deep learning models is time-consuming and costly – often prohibitively costly.…
Accelerating Federated Learning via Momentum Gradient Descent
Liu et al.: https://arxiv.org/abs/1910.03197
#ArtificialIntelligence #FederatedLearning #MachineLearning
Liu et al.: https://arxiv.org/abs/1910.03197
#ArtificialIntelligence #FederatedLearning #MachineLearning
arXiv.org
Accelerating Federated Learning via Momentum Gradient Descent
Federated learning (FL) provides a communication-efficient approach to solve
machine learning problems concerning distributed data, without sending raw data
to a central server. However, existing...
machine learning problems concerning distributed data, without sending raw data
to a central server. However, existing...
TorchBeast: A PyTorch Platform for Distributed RL
Kuttler et al.: https://arxiv.org/abs/1910.03552
#DeepLearning #OpenAIGym #ReinforcementLearning
Kuttler et al.: https://arxiv.org/abs/1910.03552
#DeepLearning #OpenAIGym #ReinforcementLearning
Spectral Inference Networks: Unifying Deep and Spectral Learning
Pfau et al. : https://arxiv.org/abs/1806.02215
#MachineLearning #DeepLearning #ArtificialIntelligence
Pfau et al. : https://arxiv.org/abs/1806.02215
#MachineLearning #DeepLearning #ArtificialIntelligence
arXiv.org
Spectral Inference Networks: Unifying Deep and Spectral Learning
We present Spectral Inference Networks, a framework for learning eigenfunctions of linear operators by stochastic optimization. Spectral Inference Networks generalize Slow Feature Analysis to...
Image Deduplicator
Tanuj Jain, Christopher Lennan, Zubin John and Dat Tran : https://github.com/idealo/imagededup
#DeepLearning #MachineLearning #Tensorflow
Tanuj Jain, Christopher Lennan, Zubin John and Dat Tran : https://github.com/idealo/imagededup
#DeepLearning #MachineLearning #Tensorflow
GitHub
GitHub - idealo/imagededup: 😎 Finding duplicate images made easy!
😎 Finding duplicate images made easy! Contribute to idealo/imagededup development by creating an account on GitHub.
ArtificialIntelligenceArticles
New book @ArtificialIntelligenceArticles
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
@ArtificialIntelligenceArticles
@ArtificialIntelligenceArticles