Multi-layered distributed representation is believed to be the key ingredient of deep neural networks especially in cognitive tasks like computer vision. While non-differentiable models such as gradient boosting decision trees (GBDTs) are still the dominant methods for modeling discrete or tabular data, they are hard to incorporate with such representation learning ability. In this work, we propose the multi-layered GBDT forest (mGBDTs), with an explicit emphasis on exploring the ability to learn hierarchical distributed representations by stacking several layers of regression GBDTs as its building block. The model can be jointly trained by a variant of target propagation across layers, without the need to derive backpropagation nor differentiability. Experiments confirmed the effectiveness of the model in terms of performance and representation learning ability.

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Tokyo Electric Power Company Holdings (Tepco) said Saturday that an alert system did not work during a test operation held the day prior as part of the restart of the No. 6 reactor at its Kashiwazaki-Kariwa nuclear plant in Niigata Prefecture. The company is working to identify the cause of the problem, but failure to resolve it soon may affect its plan to restart the reactor on Tuesday. According to Tepco, the problem was confirmed at 12:36 p.m., and it stopped the test operation. The alert system is designed to activate when a control rod is being pulled out of the reactor while another rod is already out. The Kashiwazaki-Kariwa reactor would be the first of Tepco's nuclear reactors to be restarted since the March 2011 accident at its tsunami-crippled Fukushima No. 1 nuclear plant.

The automation of the medical evidence acquisition and diagnosis process has recently attracted increasing attention in order to reduce the workload of doctors and democratize access to medical care. However, most works proposed in the machine learning literature focus solely on improving the prediction accuracy of a patient's pathology. We argue that this objective is insufficient to ensure doctors' acceptability of such systems. In their initial interaction with patients, doctors do not only focus on identifying the pathology a patient is suffering from; they instead generate a differential diagnosis (in the form of a short list of plausible diseases) because the medical evidence collected from patients is often insufficient to establish a final diagnosis. Moreover, doctors explicitly explore severe pathologies before potentially ruling them out from the differential, especially in acute care settings. Finally, for doctors to trust a system's recommendations, they need to understand how the gathered evidences led to the predicted diseases.