Would you say deep learning is mature enough to be taught in high schools? Some time ago, I received an email from a product manager at a very large company. I love sharing personal emails publicly, so I'll post it here: I sat on this email for days. I couldn't come up with a constructive answer. If anything, I wanted to reply that maybe her engineers should be scared.

This paper presents the first deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-life complexity in heterogeneous disease progression and treatment choices, with the goal of providing doctor and patients the data-driven personalized decision recommendations. The proposed DRL framework comprises (i) a supervised learning step to predict the most possible expert actions, and (ii) a deep reinforcement learning step to estimate the long-term value function of Dynamic Treatment Regimes. Both steps depend on deep neural networks. As a key motivational example, we have implemented the proposed framework on a data set from the Center for International Bone Marrow Transplant Research (CIBMTR) registry database, focusing on the sequence of prevention and treatments for acute and chronic graft versus host disease after transplantation. In the experimental results, we have demonstrated promising accuracy in predicting human experts' decisions, as well as the high expected reward function in the DRL-based dynamic treatment regimes.

In this paper, we present our approach to solve a physics-based reinforcement learning challenge "Learning to Run" with objective to train physiologically-based human model to navigate a complex obstacle course as quickly as possible. The environment is computationally expensive, has a high-dimensional continuous action space and is stochastic. We benchmark state of the art policy-gradient methods and test several improvements, such as layer normalization, parameter noise, action and state reflecting, to stabilize training and improve its sample-efficiency. We found that the Deep Deterministic Policy Gradient method is the most efficient method for this environment and the improvements we have introduced help to stabilize training. Learned models are able to generalize to new physical scenarios, e.g. different obstacle courses.

Learning meaningful representations that maintain the content necessary for a particular task while filtering away detrimental variations is a problem of great interest in machine learning. In this paper, we tackle the problem of learning representations invariant to a specific factor or trait of data. The representation learning process is formulated as an adversarial minimax game. We analyze the optimal equilibrium of such a game and find that it amounts to maximizing the uncertainty of inferring the detrimental factor given the representation while maximizing the certainty of making task-specific predictions. On three benchmark tasks, namely fair and bias-free classification, language-independent generation, and lighting-independent image classification, we show that the proposed framework induces an invariant representation, and leads to better generalization evidenced by the improved performance.

Note: All of this is free open-source. I explain all the gritty technical details in Part 2 of this post which you can find here. Feel free to contact me at anytime if you have questions. Note 2: If you're an LCS Team hire me! I'll help you beat our Korean Overlords.

This could a little late given that we have already embarked upon a new year. But it could be worthwhile looking back for a moment... If that's a little far fetched, considering the wide use of drones, advances in VR/AR and blockchain, that's because of the'bias' (read enthusiasm) in my neurons. I haven't been for long in this field but after Deepmind's paper a few years back, this year was among the first to show commercial viability of AI and showed how well poised it is for a few established problems. I have tried here to distil some major events that happened earlier in 2016 but, you know, like all networks, my brain might have missed out on some signals.

Back when I did my masters thesis I spent a lot of time processing large amounts of lidar data. One of these steps was to remove all point measurements that belonged to static objects in the scene (buildings, fences and so on). Each static object in the scene was modeled as a rectangular object, which essentially meant that I had to check if each lidar measurement fell inside any of the rectangles. The lidar that was used in my thesis operated at 10Hz, and each scan contained around 100,000 to 150,000 measurements, which meant that one second of lidar data corresponded to 1-1.5 million lidar points that had to be processed. At that time I did not know about Python or broadcasting, so my implementation of this processing step was not that fast or efficient.

Scientists at the National Center for Supercomputing Applications (NCSA), located at the University of Illinois at Urbana-Champaign, have pioneered the use of GPU-accelerated deep learning for rapid detection and characterization of gravitational waves. This new approach will enable astronomers to study gravitational waves using minimal computational resources, reducing time to discovery and increasing the scientific reach of gravitational wave astrophysics. This innovative research was recently published in Physics Letters B.

I have an upcoming interview that involves applying Deep Learning to Computer Vision problems. Though I have experience with deep learning I'm currently weak on the pure Computer Vision side of things. What are the topics that I should revise? What questions might be asked?

Google's artificial intelligence unit DeepMind engaged in "highly questionable" practices when it struck a 2015 deal to access years' worth of UK hospital patient records held by the National Health Service, says a paper published March 16 in the journal "Health and Technology." The paper, written by Cambridge University law academic Julia Powles and Economist journalist Hal Hodson, is the first piece of scholarship to analyze the terms by which 1.6 million patient records from three London hospitals that are part of the NHS Royal Free London trust were shared with DeepMind. That agreement was replaced by a 2016 deal that the authors will analyze in future. The earlier agreement is currently being investigated by two UK regulatory bodies. One of those investigations, by the Information Commissioner's Office (ICO), is "close to conclusion," the ICO says. The paper argues that both DeepMind and the hospital administrations, in their eagerness to take advantage of national data-sets, were too lax in the way the data was shared.