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Although promising results have been achieved in the areas of object detection and classification, few works have provided an end-to-end solution to the perception problems in the autonomous driving field. In this paper, we make two contributions. Firstly, we fully enhanced our previously released TT100K benchmark and provide 16,817 elaborately labeled Tencent Street View panoramas.

Phase retrieval refers to recovery of a signal-of-interest given only the intensity measurement samples and has wide applicability including important areas of astronomy, computational biology, crystallography, digital communications, electron microscopy, neutron radiography and optical imaging. The classical problem formulation is to restore the time-domain signal from its power spectrum observations, although the Fourier transform can be generalized to any linear mappings.

Matrix completion aims to find the missing entries from incomplete observations using the low-rank property. Conventional convex optimization based techniques minimize the nuclear norm subject to a constraint on the Frobenius norm of the residual. However, they are not robust to outliers and have a high computational complexity. Different from the existing schemes based on solving a minimization problem, we formulate matrix completion as a feasibility problem.

Shape Boltzmann machine (a type of Deep Boltzmann machine) is a powerful tool for shape modelling; however, has some drawbacks in representation of local shape parts. Disjunctive Normal Shape Model (DNSM) is a strong shape model that can effectively represent local parts of objects. In this paper, we propose a new shape model based on Shape Boltzmann Machine and Disjunctive Normal Shape Model which we call Disjunctive Normal Shape Boltzmann Machine (DNSBM).

Despite the importance of distributed learning, few fully distributed support vector machines exist. In this paper, not only do we provide a fully distributed nonlinear SVM; we propose the first distributed constrained-form SVM. In the fully distributed context, a dataset is distributed among networked agents that cannot divulge their data, let alone centralize the data, and can only communicate with their neighbors in the network. Our strategy is based on two algorithms: the Douglas-Rachford algorithm and the projection-gradient method.

Non-negative matrix factorization (NMF) approximates a given matrix as a product of two non-negative matrix factors. Multiplicative algorithms deliver reliable results, but they show slow convergence for high-dimensional data and may be stuck away from local minima. Gradient descent methods have better behavior, but only apply to smooth losses. For non-smooth losses such as the Kullback-Leibler (KL) loss, surprisingly, these methods are lacking.

Ecologists can assess the health of wetlands by monitoring populations of animals such as Anurans (i.e., frogs and toads), which are sensitive to habitat changes. But, surveying anurans requires trained experts to identify species from the animals’ mating calls. This identification task can be streamlined by automation. To this end, we propose an automatic frog-call classification algorithm and a smartphone application that drastically simplify the monitoring of anuran populations.