Anomaly detection is an active research topic in many different fields such as intrusion detection, network monitoring, system health monitoring, IoT healthcare, etc. However, many existing anomaly detection approaches require either human intervention or domain knowledge and may suffer from high computation complexity, consequently hindering their applicability in real-world scenarios. Therefore, a lightweight and ready-to-go approach that is able to detect anomalies in real-time is highly sought-after. Such an approach could be easily and immediately applied to perform time series anomaly detection on any commodity machine. The approach could provide timely anomaly alerts and by that enable appropriate countermeasures to be undertaken as early as possible. With these goals in mind, this paper introduces ReRe, which is a Real-time Ready-to-go proactive Anomaly Detection algorithm for streaming time series. ReRe employs two lightweight Long Short-Term Memory (LSTM) models to predict and jointly determine whether or not an upcoming data point is anomalous based on short-term historical data points and two long-term self-adaptive thresholds. Experiments based on real-world time-series datasets demonstrate the good performance of ReRe in real-time anomaly detection without requiring human intervention or domain knowledge.

Graph-structured data arise in many scenarios. A fundamental problem is to quantify the similarities of graphs for tasks such as classification. Graph kernels are positive-semidefinite functions that decompose graphs into substructures and compare them. One problem in the effective implementation of this idea is that the substructures are not independent, which leads to high-dimensional feature space. In addition, graph kernels cannot capture the high-order complex interactions between vertices. To mitigate these two problems, we propose a framework called DeepMap to learn deep representations for graph feature maps. The learnt deep representation for a graph is a dense and low-dimensional vector that captures complex high-order interactions in a vertex neighborhood. DeepMap extends Convolutional Neural Networks (CNNs) to arbitrary graphs by aligning vertices across graphs and building the receptive field for each vertex. We empirically validate DeepMap on various graph classification benchmarks and demonstrate that it achieves state-of-the-art performance.

Generating music with emotion similar to that of an input video is a very relevant issue nowadays. Video content creators and automatic movie directors benefit from maintaining their viewers engaged, which can be facilitated by producing novel material eliciting stronger emotions in them. Moreover, there's currently a demand for more empathetic computers to aid humans in applications such as augmenting the perception ability of visually and/or hearing impaired people. Current approaches overlook the video's emotional characteristics in the music generation step, only consider static images instead of videos, are unable to generate novel music, and require a high level of human effort and skills. In this study, we propose a novel hybrid deep neural network that uses an Adaptive Neuro-Fuzzy Inference System to predict a video's emotion from its visual features and a deep Long Short-Term Memory Recurrent Neural Network to generate its corresponding audio signals with similar emotional inkling. The former is able to appropriately model emotions due to its fuzzy properties, and the latter is able to model data with dynamic time properties well due to the availability of the previous hidden state information. The novelty of our proposed method lies in the extraction of visual emotional features in order to transform them into audio signals with corresponding emotional aspects for users. Quantitative experiments show low mean absolute errors of 0.217 and 0.255 in the Lindsey and DEAP datasets respectively, and similar global features in the spectrograms. This indicates that our model is able to appropriately perform domain transformation between visual and audio features. Based on experimental results, our model can effectively generate audio that matches the scene eliciting a similar emotion from the viewer in both datasets, and music generated by our model is also chosen more often.

Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence alignment. In particular, we use the deep bidirectional LSTM to learn features of LSH. The obtained LSH is then can be utilized to perform sequence alignment. We demonstrate the feasibility of the modeling sequences using the proposed LSTM-based model by aligning the short read queries over the reference genome. We use the human reference genome as our training dataset, in addition to a set of short reads generated using Illumina sequencing technology. The ultimate goal is to align query sequences into a reference genome. We first decompose the reference genome into multiple sequences. These sequences are then fed into the bidirectional LSTM model and then mapped into fixed-length vectors. These vectors are what we call the trained LSH, which can then be used for sequence alignment. The case study shows that using the introduced LSTM-based model, we achieve higher accuracy with the number of epochs.

The MIT/IEEE/Amazon GraphChallenge.org encourages community approaches to developing new solutions for analyzing graphs and sparse data. Sparse AI analytics present unique scalability difficulties. The Sparse Deep Neural Network (DNN) Challenge draws upon prior challenges from machine learning, high performance computing, and visual analytics to create a challenge that is reflective of emerging sparse AI systems. The sparse DNN challenge is based on a mathematically well-defined DNN inference computation and can be implemented in any programming environment. In 2019 several sparse DNN challenge submissions were received from a wide range of authors and organizations. This paper presents a performance analysis of the best performers of these submissions. These submissions show that their state-of-the-art sparse DNN execution time, $T_{\rm DNN}$, is a strong function of the number of DNN operations performed, $N_{\rm op}$. The sparse DNN challenge provides a clear picture of current sparse DNN systems and underscores the need for new innovations to achieve high performance on very large sparse DNNs.

Recent results in the theoretical study of deep learning have shown that the optimization dynamics of wide neural networks exhibit a surprisingly simple behaviour. In this work, we study the optimization dynamics of hypernetworks, which are architectures in which a learned meta-network produces the weights of a task-specific primary network. Hypernetworks have been demonstrated repeatedly to obtain state of the art results. However, their theoretical understanding is still lacking. As can be expected, the optimization process of multiplicative models is much more complicated than optimizing standard ReLU networks. It is shown that for an infinitely wide neural network with a gating layer the cost function cannot be accurately approximated by it first order Taylor approximation. Specifically, for a fixed sized primary network of depth H, the first H terms of the Taylor approximation of the cost function are nonzero, even when the meta-network is infinitely wide. However, for an infinitely wide meta and primary networks, the learning dynamics is determined by a linear model obtained from the first-order Taylor expansion of the network around its initial parameters and the kernel of this process is given by the Hadamard product of the kernels induced by the meta and primary networks. As part of our study, we partially solve an open problem suggested by Dyer & Gur-Ari (2020) and show that the convergence rate of the r'th order term of the Taylor expansion of the cost function along the optimization trajectories of SGD isO (1/n r 1), wheren is the width of the learned neural network, improving upon the O(1/n) bound suggested by the conjecture of Dyer & Gur-Ari (2020), while matching their empirical observations.

Deep learning techniques have been successfully applied to predict unplanned readmissions of patients in medical centers. The training data for these models is usually based on historical medical records that contain a significant amount of free-text from admission reports, referrals, exam notes, etc. Most of the models proposed so far are tailored to English text data and assume that electronic medical records follow standards common in developed countries. These two characteristics make them difficult to apply in developing countries that do not necessarily follow international standards for registering patient information, or that store text information in languages other than English. In this paper we propose a deep learning architecture for predicting unplanned readmissions that consumes data that is significantly less structured compared with previous models in the literature. We use it to present the first results for this task in a large clinical dataset that mainly contains Spanish text data. The dataset is composed of almost 10 years of records in a Chilean medical center. On this dataset, our model achieves results that are comparable to some of the most recent results obtained in US medical centers for the same task (0.76 AUROC).

Detecting anomalous inputs is critical for safely deploying deep learning models in the real world. Existing approaches for detecting out-of-distribution (OOD) examples work well when evaluated on natural samples drawn from a sufficiently different distribution than the training data distribution. However, in this paper, we show that existing detection mechanisms can be extremely brittle when evaluating on inputs with minimal adversarial perturbations which don't change their semantics. Formally, we introduce a novel and challenging problem, Robust Out-of-Distribution Detection, and propose an algorithm that can fool existing OOD detectors by adding small perturbations to the inputs while preserving their semantics and thus the distributional membership. We take a first step to solve this challenge, and propose an effective algorithm called ALOE, which performs robust training by exposing the model to both adversarially crafted inlier and outlier examples. Our method can be flexibly combined with, and render existing methods robust. On common benchmark datasets, we show that ALOE substantially improves the robustness of state-of-the-art OOD detection, with 58.4% AUROC improvement on CIFAR-10 and 46.59% improvement on CIFAR-100. Finally, we provide theoretical analysis for our method, underpinning the empirical results above.

The study of unsupervised learning can be generally divided into two categories: imitation learning and reinforcement learning. In imitation learning the machine learns by mimicking the behavior of an expert system whereas in reinforcement learning the machine learns via direct environment feedback. Traditional deep reinforcement learning takes a significant time before the machine starts to converge to an optimal policy. This paper proposes Augmented Q-Imitation-Learning, a method by which deep reinforcement learning convergence can be accelerated by applying Q-imitation-learning as the initial training process in traditional Deep Q-learning.

Supervised learning methods excel at capturing statistical properties of language when trained over large text corpora. Yet, these models often produce inconsistent outputs in goal-oriented language settings as they are not trained to complete the underlying task. Moreover, as soon as the agents are finetuned to maximize task completion, they suffer from the so-called language drift phenomenon: they slowly lose syntactic and semantic properties of language as they only focus on solving the task. In this paper, we propose a generic approach to counter language drift by using iterated learning. We iterate between fine-tuning agents with interactive training steps, and periodically replacing them with new agents that are seeded from last iteration and trained to imitate the latest finetuned models. Iterated learning does not require external syntactic constraint nor semantic knowledge, making it a valuable task-agnostic finetuning protocol. We first explore iterated learning in the Lewis Game. We then scale-up the approach in the translation game. In both settings, our results show that iterated learn-ing drastically counters language drift as well as it improves the task completion metric.