**Parallel Augmented Maps (PAM)**

In this paper we introduce an interface for supporting ordered maps that are augmented to support quick ‘sums’ of values over ranges of the keys. We have implemented this interface as part of a C++ library called PAM (Parallel and Persistent Augmented Map library). This library supports a wide variety of functions on maps ranging from basic insertion and deletion to more interesting functions such as union, intersection, difference, filtering, extracting ranges, splitting, and range-sums. The functions in the library are parallel, persistent (meaning that functions do not affect their inputs), and work-efficient. The underlying data structure is the augmented balanced binary search tree, which is a binary search tree in which each node is augmented with a value keeping the ‘sum’ of its subtree with respect to some user supplied function. With this augmentation the library can be directly applied to many settings such as to 2D range trees, interval trees, word index searching, and segment trees. The interface greatly simplifies the implementation of such data structures while it achieves efficiency that is significantly better than previous libraries. We tested our library and its corresponding applications. Experiments show that our implementation of set functions can get up to 50+ speedup on 72 cores. As for our range tree implementation, the sequential running time is more efficient than existing libraries such as CGAL, and can get up to 42+ speedup on 72 cores. … **DeepBase**

Although deep learning models perform remarkably across a range of tasks such as language translation, parsing, and object recognition, it remains unclear whether, and to what extent, these models follow human-understandable logic or procedures when making predictions. Understanding this can lead to more interpretable models, better model design, and faster experimentation. Recent machine learning research has leveraged statistical methods to identify hidden units that behave (e.g., activate) similarly to human understandable logic such as detecting language features, however each analysis requires considerable manual effort. Our insight is that, from a query processing perspective, this high level logic is a query evaluated over a database of neural network hidden unit behaviors. This paper describes DeepBase, a system to inspect neural network behaviors through a query-based interface. We model high-level logic as hypothesis functions that transform an input dataset into time series signals. DeepBase lets users quickly identify individual or groups of units that have strong statistical dependencies with desired hypotheses. In fact, we show how many existing analyses are expressible as a single DeepBase query. We use DeepBase to analyze recurrent neural network models, and propose a set of simple and effective optimizations to speed up existing analysis approaches by up to 413x. We also group and analyze different portions of a real-world neural translation model and show that learns syntactic structure, which is consistent with prior NLP studies, but can be performed with only 3 DeepBase queries. … **Particle Swarm Optimization (PSO)**

In computer science, particle swarm optimization (PSO) is a computational method that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. It solves a problem by having a population of candidate solutions, here dubbed particles, and moving these particles around in the search-space according to simple mathematical formulae over the particle’s position and velocity. Each particle’s movement is influenced by its local best known position, but is also guided toward the best known positions in the search-space, which are updated as better positions are found by other particles. This is expected to move the swarm toward the best solutions. … **Neural Rendering Model (NRM)**

Unsupervised and semi-supervised learning are important problems that are especially challenging with complex data like natural images. Progress on these problems would accelerate if we had access to appropriate generative models under which to pose the associated inference tasks. Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Neural Rendering Model (NRM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture. The NRM uses the given CNN to design the prior distribution in the probabilistic model. Furthermore, the NRM generates images from coarse to finer scales. It introduces a small set of latent variables at each level, and enforces dependencies among all the latent variables via a conjugate prior distribution. This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN). We demonstrate that this regularizer improves generalization, both in theory and in practice. In addition, likelihood estimation in the NRM yields training losses for CNNs, and inspired by this, we design a new loss termed as the Max-Min cross entropy which outperforms the traditional cross-entropy loss for object classification. The Max-Min cross entropy suggests a new deep network architecture, namely the Max-Min network, which can learn from less labeled data while maintaining good prediction performance. Our experiments demonstrate that the NRM with the RPN and the Max-Min architecture exceeds or matches the-state-of-art on benchmarks including SVHN, CIFAR10, and CIFAR100 for semi-supervised and supervised learning tasks. …

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