The human genome is now investigated through high-throughput functional assays, and through the generation of population genomic data. These advances support the identification of functional genetic variants and the prediction of traits (e.g. This review summarizes lessons learned from the large-scale analyses of genome and exome data sets, modeling of population data and machine-learning strategies to solve complex genomic sequence regions. The review also portrays the rapid adoption of artificial intelligence/deep neural networks in genomics; in particular, deep learning approaches are well suited to model the complex dependencies in the regulatory landscape of the genome, and to provide predictors for genetic variant calling and interpretation.

With a new era of cloud and big data, Database Management Systems (DBMSs) have become more crucial in numerous enterprise business applications in all the industries. Accordingly, the importance of their proactive and preventive maintenance has also increased. However, detecting problems by predefined rules or stochastic modeling has limitations, particularly when analyzing the data on high-dimensional Key Performance Indicators (KPIs) from a DBMS. In recent years, Deep Learning (DL) has opened new opportunities for this complex analysis. In this paper, we present two complementary DL approaches to detect anomalies in SAP HANA. A temporal learning approach is used to detect abnormal patterns based on unlabeled historical data, whereas a spatial learning approach is used to classify known anomalies based on labeled data. We implement a system in SAP HANA integrated with Google TensorFlow. The experimental results with real-world data confirm the effectiveness of the system and models.

The deployment of state-of-the-art neural networks containing millions of parameters to resource-constrained platforms may be prohibitive in terms of both time and space. In this work, we present an efficient coresets-based neural network compression algorithm that provably sparsifies the parameters of a trained feedforward neural network in a manner that approximately preserves the network's output. Our approach is based on an importance sampling scheme that judiciously defines a sampling distribution over the neural network parameters, and as a result, retains parameters of high importance while discarding redundant ones. Our method and analysis introduce an empirical notion of sensitivity and extend traditional coreset constructions to the application of compressing parameters. Our theoretical analysis establishes both instance-dependent and -independent bounds on the size of the resulting compressed neural network as a function of the user-specified tolerance and failure probability parameters. As a corollary to our practical compression algorithm, we obtain novel generalization bounds that may provide novel insights on the generalization properties of neural networks.

A major challenge for video captioning is to combine audio and visual cues. Existing multi-modal fusion methods have shown encouraging results in video understanding. However, the temporal structures of multiple modalities at different granularities are rarely explored, and how to selectively fuse the multi-modal representations at different levels of details remains uncharted. In this paper, we propose a novel hierarchically aligned cross-modal attention (HACA) framework to learn and selectively fuse both global and local temporal dynamics of different modalities. Furthermore, for the first time, we validate the superior performance of the deep audio features on the video captioning task. Finally, our HACA model significantly outperforms the previous best systems and achieves new state-of-the-art results on the widely used MSR-VTT dataset.

Online speech recognition is crucial for developing natural human-machine interfaces. This modality, however, is significantly more challenging than off-line ASR, since real-time/low-latency constraints inevitably hinder the use of future information, that is known to be very helpful to perform robust predictions. A popular solution to mitigate this issue consists of feeding neural acoustic models with context windows that gather some future frames. This introduces a latency which depends on the number of employed look-ahead features. This paper explores a different approach, based on estimating the future rather than waiting for it. Our technique encourages the hidden representations of a unidirectional recurrent network to embed some useful information about the future. Inspired by a recently proposed technique called Twin Networks, we add a regularization term that forces forward hidden states to be as close as possible to cotemporal backward ones, computed by a "twin" neural network running backwards in time. The experiments, conducted on a number of datasets, recurrent architectures, input features, and acoustic conditions, have shown the effectiveness of this approach. One important advantage is that our method does not introduce any additional computation at test time if compared to standard unidirectional recurrent networks.

Cellular-connected unmanned aerial vehicles (UAVs) will inevitably be integrated into future cellular networks as new aerial mobile users. Providing cellular connectivity to UAVs will enable a myriad of applications ranging from online video streaming to medical delivery. However, to enable a reliable wireless connectivity for the UAVs as well as a secure operation, various challenges need to be addressed such as interference management, mobility management and handover, cyber-physical attacks, and authentication. In this paper, the goal is to expose the wireless and security challenges that arise in the context of UAV-based delivery systems, UAV-based real-time multimedia streaming, and UAV-enabled intelligent transportation systems. To address such challenges, artificial neural network (ANN) based solution schemes are introduced. The introduced approaches enable the UAVs to adaptively exploit the wireless system resources while guaranteeing a secure operation, in real-time. Preliminary simulation results show the benefits of the introduced solutions for each of the aforementioned cellular-connected UAV application use case.

In my last tutorial, you learned how to create a facial recognition pipeline in Tensorflow with convolutional neural networks. In this tutorial, you'll learn how a convolutional neural network (CNN) and Long Short Term Memory (LSTM) can be combined to create an image caption generator and generate captions for your own images. In 2014, researchers from Google released a paper, Show And Tell: A Neural Image Caption Generator. At the time, this architecture was state-of-the-art on the MSCOCO dataset. It utilized a CNN LSTM to take an image as input and output a caption.

In the previous post, Coding Neural Network - Forward Propagation and Backpropagation, we implemented both forward propagation and backpropagation in numpy. However, implementing backpropagation from scratch is usually more prune to bugs/errors. Therefore, it's necessary before running the neural network on training data to check if our implementation of backpropagation is correct. Before we start, let's revisit what back-propagation is: We loop over the nodes in reverse topological order starting at the final node to compute the derivative of the cost with respect to each edge's node tail. In other words, we compute the derivative of cost function with respect to all parameters, i.e $\frac{\partial J}{\partial \theta}$ where $\theta$ represents the parameters of the model.

Panasonic is introducing its FacePRO deep learning facial recognition system, with a core engine the company says provides the world's highest performance, at ISC West, April 11 to 13. The company's facial recognition engine received a high score in NIST IJB-A testing last year. FacePRO features the capabilities announced by Panasonic as part of its Face Recognition Server Software and Expansion Kit in February. Those capabilities include recognition of faces captured at 45-degree side-angles or 30-degree vertical angles, as well as those partially hidden, such as by sunglasses. They also include "iA" or intelligent auto mode, which adjusts camera settings to optimize for facial recognition.

In this episode, Audrow Nash speaks with Michael Laskey, PhD student at UC Berkeley, about a method for robust imitation learning, called DART. Laskey discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines. To learn more, see this post on Robohub from the Berkeley Artificial Intelligence Research (BAIR) Lab. Michael Laskey is a Ph.D. Candidate in EECS at UC Berkeley, advised by Prof. Ken Goldberg in the AUTOLAB (Automation Sciences). Michael's Ph.D. develops new algorithms for Deep Learning of robust robot control policies and examines how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments.