Graph distance metric learning serves as the foundation for many graph learning problems, e.g., graph clustering, graph classification and graph matching. Existing research works on graph distance metric (or graph kernels) learning fail to maintain the basic properties of such metrics, e.g., non-negative, identity of indiscernibles, symmetry and triangle inequality, respectively. In this paper, we will introduce a new graph neural network based distance metric learning approaches, namely GB-DISTANCE (GRAPH-BERT based Neural Distance). Solely based on the attention mechanism, GB-DISTANCE can learn graph instance representations effectively based on a pre-trained GRAPH-BERT model. Different from the existing supervised/unsupervised metrics, GB-DISTANCE can be learned effectively in a semi-supervised manner. In addition, GB-DISTANCE can also maintain the distance metric basic properties mentioned above. Extensive experiments have been done on several benchmark graph datasets, and the results demonstrate that GB-DISTANCE can out-perform the existing baseline methods, especially the recent graph neural network model based graph metrics, with a significant gap in computing the graph distance.

Supervised machine learning algorithms have seen spectacular advances and surpassed human level performance in a wide range of specific applications. However, using complex ensemble or deep learning algorithms typically results in black box models, where the path leading to individual predictions cannot be followed in detail. In order to address this issue, we propose the novel "Cyclic Boosting" machine learning algorithm, which allows to efficiently perform accurate regression and classification tasks while at the same time allowing a detailed understanding of how each individual prediction was made.

Tackling binary analysis problems has traditionally implied manually defining rules and heuristics. As an alternative, we are suggesting using machine learning models for learning distributed representations of binaries that can be applicable for a number of downstream tasks. We construct a computational graph from the binary executable and use it with a graph convolutional neural network to learn a high dimensional representation of the program. We show the versatility of this approach by using our representations to solve two semantically different binary analysis tasks -- algorithm classification and vulnerability discovery. We compare the proposed approach to our own strong baseline as well as published results and demonstrate improvement on the state of the art methods for both tasks.

Local robustness verification can verify that a neural network is robust wrt. any perturbation to a specific input within a certain distance. We call this distance Robustness Radius. We observe that the robustness radii of correctly classified inputs are much larger than that of misclassified inputs which include adversarial examples, especially those from strong adversarial attacks. Another observation is that the robustness radii of correctly classified inputs often follow a normal distribution. Based on these two observations, we propose to validate inputs for neural networks via runtime local robustness verification. Experiments show that our approach can protect neural networks from adversarial examples and improve their accuracies.

Recent research has shown that it is challenging to detect out-of-distribution (OOD) data in deep generative models including flow-based models and variational autoencoders (VAEs). In this paper, we prove a theorem that, for a well-trained flow-based model, the distance between the distribution of representations of an OOD dataset and prior can be large enough, as long as the distance between the distributions of the training dataset and the OOD dataset is large enough. Furthermore, our observation shows that, for flow-based model and VAE with factorized prior, the representations of OOD datasets are more correlated than that of the training dataset. Based on our theorem and observation, we propose detecting OOD data according to the total correlation of representations in flow-based model and VAE. Experimental results show that our method can achieve nearly 100\% AUROC for all the widely used benchmarks and has robustness against data manipulation. While the state-of-the-art method performs not better than random guessing for challenging problems and can be fooled by data manipulation in almost all cases.

Knowledge distillation is a model-agnostic technique to improve model quality while having a fixed capacity budget. It is a commonly used technique for model compression, where a higher capacity teacher model with better quality is used to train a more compact student model with better inference efficiency. Through distillation, one hopes to benefit from student's compactness, without sacrificing too much on model quality. Despite the large success of knowledge distillation, better understanding of how it benefits student model's training dynamics remains under-explored. In this paper, we dissect the effects of knowledge distillation into three main factors: (1) benefits inherited from label smoothing, (2) example re-weighting based on teacher's confidence on ground-truth, and (3) prior knowledge of optimal output (logit) layer geometry. Using extensive systematic analyses and empirical studies on synthetic and real-world datasets, we confirm that the aforementioned three factors play a major role in knowledge distillation. Furthermore, based on our findings, we propose a simple, yet effective technique to improve knowledge distillation empirically.

Recent research efforts aiming to bridge the Neural-Symbolic gap for RDFS reasoning proved empirically that deep learning techniques can be used to learn RDFS inference rules. However, one of their main deficiencies compared to rule-based reasoners is the lack of derivations for the inferred triples (i.e. explainability in AI terms). In this paper, we build on these approaches to provide not only the inferred graph but also explain how these triples were inferred. In the graph words approach, RDF graphs are represented as a sequence of graph words where inference can be achieved through neural machine translation. To achieve explainability in RDFS reasoning, we revisit this approach and introduce a new neural network model that gets the input graph--as a sequence of graph words-- as well as the encoding of the inferred triple and outputs the derivation for the inferred triple. We evaluated our justification model on two datasets: a synthetic dataset-- LUBM benchmark-- and a real-world dataset --ScholarlyData about conferences-- where the lowest validation accuracy approached 96%.

Deep Learning (DL) systems are key enablers for engineering intelligent applications due to their ability to solve complex tasks such as image recognition and machine translation. Nevertheless, using DL systems in safety- and security-critical applications requires to provide testing evidence for their dependable operation. Recent research in this direction focuses on adapting testing criteria from traditional software engineering as a means of increasing confidence for their correct behaviour. However, they are inadequate in capturing the intrinsic properties exhibited by these systems. We bridge this gap by introducing DeepImportance, a systematic testing methodology accompanied by an Importance-Driven (IDC) test adequacy criterion for DL systems. Applying IDC enables to establish a layer-wise functional understanding of the importance of DL system components and use this information to assess the semantic diversity of a test set. Our empirical evaluation on several DL systems, across multiple DL datasets and with state-of-the-art adversarial generation techniques demonstrates the usefulness and effectiveness of DeepImportance and its ability to support the engineering of more robust DL systems.

Time series data have always been of major interest to financial services, and now with the rise of real-time applications, other areas such as retail and programmatic advertising are turning their attention to time-series data driven applications. In the last couple of years, several key players in cloud services, such as Apache Kafka and Apache Spark, have released new products for processing time series data. It is therefore of great interest to understand the role and potentials of Machine Learning (ML) in this rising field. In this article, I discuss the (very) recent discoveries on Time Series Classification (TSC) with Deep Learning, by following a series of publications from the authors of [2]. TSC is the area of ML interested in learning how to assign labels to time series.

Researchers from Google and Janelia Research Campus made a complex map of the brain of a fruit fly. Tiny brain, big science: Researchers from Google and the Howard Hughes Medical Institute's Janelia Research Campus have created an in-depth 3D map of a fruit fly's brain, which they say could teach us more about how the organ controls learning, memory and other behaviors. It's the largest brain map of this type -- called a connectome -- ever created, for any animal, according to the research project website and a paper describing the work. The map covers a large portion of the fly's brain, including the circuits used for learning, navigation, visual processing and possibly sleep, says the paper. It includes about 25,000 neurons, along with 20 million chemical synapses found between them, and it required complex imaging technology and deep learning algorithms, according to an article from the research campus.