X-TrainCaps
Convolutional Neural Networks (CNNs) are extensively in use due to their excellent results in various machine learning (ML) tasks like image classification and object detection. Recently, Capsule Networks (CapsNets) have shown improved performances compared to the traditional CNNs, by encoding and preserving spatial relationships between the detected features in a better way. This is achieved through the so-called Capsules (i.e., groups of neurons) that encode both the instantiation probability and the spatial information. However, one of the major hurdles in the wide adoption of CapsNets is its gigantic training time, which is primarily due to the relatively higher complexity of its constituting elements. In this paper, we illustrate how can we devise new optimizations in the training process to achieve fast training of CapsNets, and if such optimizations affect the network accuracy or not. Towards this, we propose a novel framework ‘X-TrainCaps’ that employs lightweight software-level optimizations, including a novel learning rate policy called WarmAdaBatch that jointly performs warm restarts and adaptive batch size, as well as weight sharing for capsule layers to reduce the hardware requirements of CapsNets by removing unused/redundant connections and capsules, while keeping high accuracy through tests of different learning rate policies and batch sizes. We demonstrate that one of the solutions generated by X-TrainCaps framework can achieve 58.6% training time reduction while preserving the accuracy (even 0.9% accuracy improvement), compared to the CapsNet in the original paper by Sabour et al. (2017), while other Pareto-optimal solutions can be leveraged to realize trade-offs between training time and achieved accuracy. …

Multimodal Style Transfer (MST)
An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alternative approaches have represented styles by decomposing them into local pixel or neural patches. Despite the recent progress, most existing methods treat the semantic patterns of style image uniformly, resulting unpleasing results on complex styles. In this paper, we introduce a more flexible and general universal style transfer technique: multimodal style transfer (MST). MST explicitly considers the matching of semantic patterns in content and style images. Specifically, the style image features are clustered into sub-style components, which are matched with local content features under a graph cut formulation. A reconstruction network is trained to transfer each sub-style and render the final stylized result. Extensive experiments demonstrate the superior effectiveness, robustness and flexibility of MST. …

Combination of Peaks of Energy (COPE)
Sound analysis research has mainly been focused on speech and music processing. The deployed methodologies are not suitable for analysis of sounds with varying background noise, in many cases with very low signal-to-noise ratio (SNR). In this paper, we present a method for the detection of patterns of interest in audio signals. We propose novel trainable feature extractors, which we call COPE (Combination of Peaks of Energy). The structure of a COPE feature extractor is determined using a single prototype sound pattern in an automatic configuration process, which is a type of representation learning. We construct a set of COPE feature extractors, configured on a number of training patterns. Then we take their responses to build feature vectors that we use in combination with a classifier to detect and classify patterns of interest in audio signals. We carried out experiments on four public data sets: MIVIA audio events, MIVIA road events, ESC-10 and TU Dortmund data sets. The results that we achieved (recognition rate equal to 91.71% on the MIVIA audio events, 94% on the MIVIA road events, 81.25% on the ESC-10 and 94.27% on the TU Dortmund) demonstrate the effectiveness of the proposed method and are higher than the ones obtained by other existing approaches. The COPE feature extractors have high robustness to variations of SNR. Real-time performance is achieved even when the value of a large number of features is computed. …

Act2Vec
We introduce Act2Vec, a general framework for learning context-based action representation for Reinforcement Learning. Representing actions in a vector space help reinforcement learning algorithms achieve better performance by grouping similar actions and utilizing relations between different actions. We show how prior knowledge of an environment can be extracted from demonstrations and injected into action vector representations that encode natural compatible behavior. We then use these for augmenting state representations as well as improving function approximation of Q-values. We visualize and test action embeddings in three domains including a drawing task, a high dimensional navigation task, and the large action space domain of StarCraft II. …