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ICML 2016 Papers

International Conference on Machine Learning

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322papers

Counted from the official paper list; may differ slightly from the number the conference announced.

  • Poster
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A Comparative Analysis and Study of Multiview CNN Models for Joint Object Categorization and Pose Estimation

Mohamed Elhoseiny, Tarek El-Gaaly, Amr Bakry, Ahmed Elgammal

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In the Object Recognition task, there exists a dichotomy between the categorization of objects and estimating object pose, where the former necessitates a view-invariant representation, while the latter requires a representation capable of capturing pose information over different categories of objects. With the rise of deep architectures, the prime focus has been on object category recognition. Deep learning methods have achieved wide success in this task. In contrast, object pose estimation using these approaches has received relatively less attention. In this work, we study how Convolutional Neural Networks (CNN) architectures can be adapted to the task of simultaneous object recognition and pose estimation. We investigate and analyze the layers of various CNN models and extensively compare between them with the goal of discovering how the layers of distributed representations within CNNs represent object pose information and how this contradicts with object category representations. We extensively experiment on two recent large and challenging multi-view datasets and we achieve better than the state-of-the-art.

Poster
A Box-Constrained Approach for Hard Permutation Problems

Cong Han Lim, Steve Wright

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We describe the use of sorting networks to form relaxations of problems involving permutations of n objects. This approach is an alternative to relaxations based on the Birkhoff polytope (the set of n \times n doubly stochastic matrices), providing a more compact formulation in which the only constraints are box constraints. Using this approach, we form a variant of the relaxation of the quadratic assignment problem recently studied in Vogelstein et al. (2015), and show that the continuation method applied to this formulation can be quite effective. We develop a coordinate descent algorithm that achieves a per-cycle complexity of O(n^2 \log^2 n). We compare this method with Fast Approximate QAP (FAQ) algorithm introduced in Vogelstein et al. (2015), which uses a conditional-gradient method whose per-iteration complexity is O(n^3). We demonstrate that for most problems in QAPLIB and for a class of synthetic QAP problems, the sorting-network formulation returns solutions that are competitive with the FAQ algorithm, often in significantly less computing time.

Poster