Recent advances in machine learning have leveraged dramatic increases in computational power, a trend expected to continue in the future. This paper introduces the first Hyperscale Hardware Optimized Neural Architecture Search (H2O-NAS) to automatically design accurate and performant machine learning models tailored to the underlying hardware architecture. H2O-NAS consists of three key components: a new massively parallel “one-shot” search algorithm with intelligent weight sharing, which can scale to search spaces of O(10280) and handle large volumes of production traffic; hardware-optimized search spaces for diverse ML models on heterogeneous hardware; and a novel two-phase hybrid performance model and a multi-objective reward function optimized for large scale deployments.

Current face image retrieval methods achieve impressive results, but lack efficient ways to refine the search, particularly for geometric face attributes. Users cannot easily find faces with slightly more furrowed brows or specific leftward pose shifts, for example. To address this problem, we propose a new face search technique based on shape manipulation that is complementary to current search engines. Users drag one or a small number of contour points, like the bottom of the chin or the corner of an eyebrow, to search for faces similar in shape to the current face, but with updated geometric attributes specific to their edits. For example, the user can drag a mouth corner to find faces with wider smiles, or the tip of the nose to find faces with a specific pose. As part of our system, we propose (1) a novel confidence score for face alignment results that automatically constructs a contour-aligned face database with reasonable alignment accuracy, (2) a simple and straightforward extension of PCA with missing data to tensor analysis, and (3) a new regularized tensor model to compute shape feature vectors for each aligned face, all built upon previous work. To the best of our knowledge, our system demonstrates the first face retrieval approach based chiefly on shape manipulation. We show compelling results on a sizable database of over 10,000 face images captured in uncontrolled environments.

In this paper, we present a new framework for non-rigid structure from motion (NRSFM) that simultaneously addresses three significant challenges: severe occlusion, perspective camera projection, and large non-linear deformation. We introduce a concept called a model graph, which greatly reduces the computational cost of discovering groups of input images that depict consistent 3D shapes. A 3D model is constructed for each input image by traversing the model graph along multiple evolutionary paths. A compressive shape representation is constructed, which (1) consolidates the multiple 3D models for each image reconstructed during model evolution and (2) reduces the number of models needed to represent the input image set. Assuming feature correspondences are known, we demonstrate our algorithm on both real and synthetic data sets that exemplify all three aforementioned challenges.