Preference Neural Network (PNN)
This paper proposes a preference neural network (PNN) to address the problem of indifference preferences orders with new activation function. PNN also solves the Multi-label ranking problem, where labels may have indifference preference orders or subgroups are equally ranked. PNN follows a multi-layer feedforward architecture with fully connected neurons. Each neuron contains a novel smooth stairstep activation function based on the number of preference orders. PNN inputs represent data features and output neurons represent label indexes. The proposed PNN is evaluated using new preference mining dataset that contains repeated label values which have not experimented before. PNN outperforms five previously proposed methods for strict label ranking in terms of accurate results with high computational efficiency. …

Guided Complement Entropy (GCE)
Model robustness has been an important issue, since adding small adversarial perturbations to images is sufficient to drive the model accuracy down to nearly zero. In this paper, we propose a new training objective ‘Guided Complement Entropy’ (GCE) that has dual desirable effects: (a) neutralizing the predicted probabilities of incorrect classes, and (b) maximizing the predicted probability of the ground-truth class, particularly when (a) is achieved. Training with GCE encourages models to learn latent representations where samples of different classes form distinct clusters, which we argue, improves the model robustness against adversarial perturbations. Furthermore, compared with the state-of-the-arts trained with cross-entropy, same models trained with GCE achieve significant improvements on the robustness against white-box adversarial attacks, both with and without adversarial training. When no attack is present, training with GCE also outperforms cross-entropy in terms of model accuracy. …

WaveGlow
In this paper we propose WaveGlow: a flow-based network capable of generating high quality speech from mel-spectrograms. WaveGlow combines insights from Glow and WaveNet in order to provide fast, efficient and high-quality audio synthesis, without the need for auto-regression. WaveGlow is implemented using only a single network, trained using only a single cost function: maximizing the likelihood of the training data, which makes the training procedure simple and stable. Our PyTorch implementation produces audio samples at a rate of more than 500 kHz on an NVIDIA V100 GPU. Mean Opinion Scores show that it delivers audio quality as good as the best publicly available WaveNet implementation. All code will be made publicly available online. …

Weakly Structured Information Processing and Exploration (WIPE)
WIPE is used for managing the graph traversal manipulation with BI-like data aggregation. WIPE stands for “Weakly-structured Information Processing and Exploration”. It is a data manipulation and query language built on top of the graph functionality in the SAP HANA Database. Like other domain specific languages provided by SAP HANA Database, WIPE is embedded in transactional context, which means that multiple WIPE statements can be executed concurrently, guaranteeing the atomicity, consistency, isolation and durability. With the help of this language, multiple graph operations such as inserting, updating or deleting a node and other query operations can be declared in one complex statement. It is the graph abstraction layer in the SAP HANA Database that provides interaction with the graph data stored in the database by exposing graph concepts directly to the application developer. The application developer can create or delete graphs, access the existing graphs, modify the vertices and edges of the graphs, or retrieve a set of vertices and edges based on their attributes. Besides retrieval and manipulation functions, a set of built-in graph operators are also provided by the SAP HANA Database. These operators, such as breadth-first or depth-first traversal algorithms, interact with the column store of the relational engine to execute efficiently and in a highly optimum manner. …