Welcome to our biweekly selection of Free and Open machine learning news. Created using our own opinionated selection and summary algorithm. FOSS machine learning is crucial for everyone. Machine Learning is a complex technology. So keep it simple.
1 Duality — A New Approach to Reinforcement Learning
Many modern successful RL algorithms, such as Q-learning and actor- critic, propose to reduce the RL problem to a constraint- satisfaction problem, where a constraint exists for every possible “state” of the environment. Applying duality to this mathematical problem yields a different formulation of the same problem. Despite how ubiquitous the constraint-satisfaction approach is in practice, this strategy is often difficult to reconcile with the complexity of real- world settings. The choice of the regularizer is crucial to the final step, in which we apply duality once again to yield another formulation of an equivalent problem. This approach is based on convex duality , which is a well-studied mathematical tool used to transform problems expressed in one form into equivalent problems in distinct forms that may be more computationally friendly.
2 Fan Uses AI to Lipread TV Series Before Chinese Authorities Censored Them
China carries out censorship on a massive scale. What may be more surprising is that its censorship extends to even the most innocuous aspects of life. By analyzing the lip movements, it is possible to predict the sounds of a Chinese syllable.
3 The United States of Artificial Intelligence
Funding to artificial intelligence startups has been on the rise. Fifteen startups on the map have raised over $100M in disclosed equity funding.
4 TayPO, a Unifying Framework for Reinforcement Learning
Policy optimization is a major framework in model-free reinforcement learning (RL), providing insights that can drive significant algorithmic performance gains. Two of the most prominent such algorithmic improvements are trust-region policy search and off-policy corrections.
5 Causal Reinforcement Learning
This page provides information and materials about “Causal Reinforcement Learning ” (CRL), following the initial tutorial presentation at ICML 2020. Including the 147 slide presentation for those who are deep involved in RL.
6 List of computer vision pretrained models
A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application. But this is a great list to simplify reuse.
7 List of NLP pre-trained models
A pre-trained model is a model created by some one else to solve a similar problem. Instead of building a model from scratch to solve a similar problem, we can use the model trained on other problem as a starting point. A pre-trained model may not be 100% accurate in your application
8 What Went Wrong With Clearview AI?
Spoiler alert: Everything. Nice read.
9 Kafka-ML: connecting the data stream with ML/AI frameworks
Kafka is not a simple technology. I think only usable in a strict number of use-cases. But this article is nice if you are working with Kafka or considering using it.
Machine Learning (ML) and Artificial Intelligence (AI) have a dependency on data sources to train, improve and make predictions through their algorithms. With the digital revolution and current paradigms like the Internet of Things, this information is turning from static data into continuous data streams. However, most of the ML/AI frameworks used nowadays are not fully prepared for this revolution. In this paper, we proposed Kafka-ML, an open-source framework that enables the management of TensorFlow ML/AI pipelines through data streams (Apache Kafka). Kafka-ML provides an accessible and user-friendly Web User Interface where users can easily define ML models, to then train, evaluate and deploy them for inference.
10 A Survey of Privacy Attacks in Machine Learning
As machine learning becomes more widely used, the need to study its implications in security and privacy becomes more urgent. Research on the security aspects of machine learning, such as adversarial attacks, has received a lot of focus and publicity, but privacy related attacks have received less attention from the research community. To contribute into this research line we analyzed more than 40 papers related to privacy attacks against machine learning that have been published during the past seven years.
The FOSS Machine Learning News Blog is a brief overview of open machine learning news from all over the world. Free and Open machine learning means that everyone must be able to develop, test and play and deploy machine learning solutions. Read and share the FOSS ML Guide! And remember:You are invited to join the Free and Open Machine Learning open collaboration project.