Mariusz Bojarski

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We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with(More)
We consider the hashing mechanism for constructing binary embeddings, that involves pseudo-random projections followed by nonlin-ear (sign function) mappings. The pseudo-random projection is described by a matrix, where not all entries are independent random variables but instead a fixed " budget of random-ness " is distributed across the matrix. Such(More)
We consider supervised learning with random decision trees, where the tree construction is completely random. The method was used as a heuristic working well in practice despite the simplicity of the setting, but with almost no theoretical guarantees. The goal of this paper is to shed new light on the entire paradigm. We provide strong theoretical(More)
We analyze the performance of the top-down multiclass classification algorithm for decision tree learning called LOMtree, recently proposed in the literature Choromanska and Langford (2014) for solving efficiently classification problems with very large number of classes. The algorithm online optimizes the objective function which simultaneously controls(More)
—A new family of Class D resonant inverters is proposed in this paper. Multiple identical series resonant invert-ers are paralleled using intercell transformers to form phase-controlled multiphase resonant inverter with a common resonant circuit. Inverters can operate at constant frequency utilizing phase-shift control to regulate output. A frequency-domain(More)
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