High-quality labeled data is essential to successfully train supervised machine learning models. Although a large amount of unlabeled data is present in the medical domain, labeling poses a major challenge: medical professionals who can expertly label the data are a scarce and expensive resource. Making matters worse, voxel-wise delineation of data (e.g. for segmentation tasks) is tedious and suffers from high inter-rater variance, thus dramatically limiting available training data. We propose a recursive training strategy to perform the task of semantic segmentation given only very few training samples with pixel-level annotations. We expand on this small training set having cheaper image-level annotations using a recursive training strategy. We apply this technique on the segmentation of intracranial hemorrhage (ICH) in CT (computed tomography) scans of the brain, where typically few annotated data is available.

Graph convolutional networks (GCNs) have shown promising results in processing graph data by extracting structure-aware features. This gave rise to extensive work in geometric deep learning, focusing on designing network architectures that ensure neuron activations conform to regularity patterns within the input graph. However, in most cases the graph structure is only accounted for by considering the similarity of activations between adjacent nodes, which in turn degrades the results. In this work, we augment GCN models by incorporating richer notions of regularity by leveraging cascades of band-pass filters, known as geometric scatterings. The produced graph features incorporate multiscale representations of local graph structures, while avoiding overly smooth activations forced by previous architectures. Moreover, inspired by skip connections used in residual networks, we introduce graph residual convolutions that reduce high-frequency noise caused by joining together information at multiple scales. Our hybrid architecture introduces a new model for semi-supervised learning on graph-structured data, and its potential is demonstrated for node classification tasks on multiple graph datasets, where it outperforms leading GCN models.

Many complex problems in various applications are solved by means of deep machine learning models, in particular deep neural networks, at the present time. One of the demonstrative examples is the disease diagnosis by the models on the basis of medical images or another medical information. At the same time, deep learning models often work as black-box models such that details of their functioning are often completely unknown. It is difficult to explain in this case how a certain result or decision is achieved. As a result, the machine learning models meet some difficulties in their incorporating into many important applications, for example, into medicine, where doctors have to have an explanation of a stated diagnosis in order to choose a corresponding treatment. The lack of the explanation elements in many machine learning models has motivated development of many methods which could interpret or explain the deep machine learning algorithm predictions and understand the decisionmaking process or the key factors involved in the decision [4, 18, 35, 36]. The methods explaining the black-box machine learning models can be divided into two main groups: local methods which derive explanation locally around a test example; global methods which try to explain the overall behavior of the model. A key component of explanations for models is the contribution of individual input features. It is assumed that a prediction is explained when every feature is assigned by some number quantified its impact on the prediction.

We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input information. The QNN uses quaternion-valued features, where each element is a quaternion. The QNN hides input information into a random phase of quaternion-valued features. Even if attackers have obtained network parameters and intermediate-layer features, they cannot extract input information without knowing the target phase. In this way, the QNN can effectively protect the input privacy. Besides, the output accuracy of QNNs only degrades mildly compared to traditional neural networks, and the computational cost is much less than other privacy-preserving methods.

Learning continuous representations of discrete objects such as text, users, and URLs lies at the heart of many applications including language and user modeling. When using discrete objects as input to neural networks, we often ignore the underlying structures (e.g. natural groupings and similarities) and embed the objects independently into individual vectors. As a result, existing methods do not scale to large vocabulary sizes. In this paper, we design a Bayesian nonparametric prior for embeddings that encourages sparsity and leverages natural groupings among objects. We derive an approximate inference algorithm based on Small Variance Asymptotics which yields a simple and natural algorithm for learning a small set of anchor embeddings and a sparse transformation matrix. We call our method Anchor & Transform (ANT) as the embeddings of discrete objects are a sparse linear combination of the anchors, weighted according to the transformation matrix. ANT is scalable, flexible, end-to-end trainable, and allows the user to incorporate domain knowledge about object relationships. On text classification and language modeling benchmarks, ANT demonstrates stronger performance with fewer parameters as compared to existing compression baselines.

We study image inverse problems with invertible generative priors, specifically normalizing flow models. Our formulation views the solution as the Maximum a Posteriori (MAP) estimate of the image given the measurements. Our general formulation allows for non-linear differentiable forward operators and noise distributions with long-range dependencies. We establish theoretical recovery guarantees for denoising and compressed sensing under our framework. We also empirically validate our method on various inverse problems including compressed sensing with quantized measurements and denoising with dependent noise patterns.

We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network. Stable neural flows provide an implicit guarantee on asymptotic stability of the depth-flows, leading to robustness against input perturbations and low computational burden for the numerical solver. The learning procedure is cast as an optimal control problem, and an approximate solution is proposed based on adjoint sensivity analysis. We further introduce novel regularizers designed to ease the optimization process and speed up convergence. The proposed model class is evaluated on non-linear classification and function approximation tasks.

This article deals with the IT security of connectionist artificial intelligence (AI) applications, focusing on threats to integrity, one of the three IT security goals. Such threats are for instance most relevant in prominent AI computer vision applications. In order to present a holistic view on the IT security goal integrity, many additional aspects such as interpretability, robustness and documentation are taken into account. A comprehensive list of threats and possible mitigations is presented by reviewing the state-of-the-art literature. AI-specific vulnerabilities such as adversarial attacks and poisoning attacks as well as their AI-specific root causes are discussed in detail. Additionally and in contrast to former reviews, the whole AI supply chain is analysed with respect to vulnerabilities, including the planning, data acquisition, training, evaluation and operation phases. The discussion of mitigations is likewise not restricted to the level of the AI system itself but rather advocates viewing AI systems in the context of their supply chains and their embeddings in larger IT infrastructures and hardware devices. Based on this and the observation that adaptive attackers may circumvent any single published AI-specific defence to date, the article concludes that single protective measures are not sufficient but rather multiple measures on different levels have to be combined to achieve a minimum level of IT security for AI applications.

Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints. In this paper, we start by motivating reinforcement learning as a solution to the placement problem. We then give an overview of what deep reinforcement learning is. We next formulate the placement problem as a reinforcement learning problem and show how this problem can be solved with policy gradient optimization. Finally, we describe lessons we have learned from training deep reinforcement learning policies across a variety of placement optimization problems.

In late December last year, Dr. Li Wenliang began warning officials about a novel coronavirus in Wuhan, China, but was silenced by the police before tragically succumbing to the disease two months later. Meanwhile, almost simultaneously, a computer server halfway across the world started issuing worrying alerts of a potential new outbreak. The server runs software by BlueDot, a company based in San Francisco that uses AI to monitor infectious disease outbreaks for signs of early trouble. Not enough people listened to either human expertise or AI. Then cases skyrocketed in Wuhan and spread across the world, and people had to take note.