January 20, 2020 Abstract Graph structured data has wide applicability in various domains such as physics, chemistry, biology, computer vision, and social networks, to name a few. Recently, graph neural networks (GNN) were shown to be successful in effectively representing graph structured data because of their good performance and generalization ability. GNN is a deep learning based method that learns a node representation by combining specific nodes and the structural/topological information of a graph. However, like other deep models, explaining the effectiveness of GNN models is a challenging task because of the complex nonlinear transformations made over the iterations. In this paper, we propose GraphLIME, a local interpretable model explanation for graphs using the Hilbert-Schmidt Independence Criterion (HSIC) Lasso, which is a nonlinear feature selection method. GraphLIME is a generic GNN-model explanation framework that learns a nonlinear interpretable model locally in the subgraph of the node being explained. More specifically, to explain a node, we generate a nonlinear interpretable model from its N -hop neighborhood and then compute the K most representative features as the explanations of its prediction using HSIC Lasso. Through experiments on two real-world datasets, the explanations of GraphLIME are found to be of extraordinary degree and more descriptive in comparison to the existing explanation methods. 1 Introduction Deep Neural Network (DNN) is essentially a new machine learning algorithm based on discriminant model.

January 20, 2020 A BSTRACT A robust theoretical framework that can describe and predict the generalization ability of deep neural networks (DNNs) in general circumstances remains elusive. Classical attempts have produced complexity metrics that rely heavily on global measures of compactness and capacity with little investigation into the effects of sub-component collaboration. We demonstrate intriguing regularities in the activation patterns of the hidden nodes within fully-connected feedforward networks. By tracing the origin of these patterns, we show how such networks can be viewed as the combination of two information processing systems: one continuous and one discrete. We describe how these two systems arise naturally from the gradient-based optimization process, and demonstrate the classification ability of the two systems, individually and in collaboration. This perspective on DNN classification offers a novel way to think about generalization, in which different subsets of the training data are used to train distinct classifiers; those classifiers are then combined to perform the classification task, and their consistency is crucial for accurate classification. 1 Introduction One of the central tenets of computational learning theory (CL T) is that the ability of a machine-learning system to generalize to unseen data results from its compactness. That is, if the system employs a number of parameters that is small relative to the number of training samples that it processes appropriately, we can be confident that the system will generalize well to unseen samples drawn from the same distribution as the training data.

Domain adaptation performance of a learning algorithm on a target domain is a function of its source domain error and a divergence measure between the data distribution of these two domains. We present a study of various distance-based measures in the context of NLP tasks, that characterize the dissimilarity between domains based on sample estimates. We first conduct analysis experiments to show which of these distance measures can best differentiate samples from same versus different domains, and are correlated with empirical results. Next, we develop a DistanceNet model which uses these distance measures, or a mixture of these distance measures, as an additional loss function to be minimized jointly with the task's loss function, so as to achieve better unsupervised domain adaptation. Finally, we extend this model to a novel DistanceNet-Bandit model, which employs a multi-armed bandit controller to dynamically switch between multiple source domains and allow the model to learn an optimal trajectory and mixture of domains for transfer to the low-resource target domain. We conduct experiments on popular sentiment analysis datasets with several diverse domains and show that our DistanceNet model, as well as its dynamic bandit variant, can outperform competitive baselines in the context of unsupervised domain adaptation. 1 Introduction In situations where large-scale annotated datasets are available, supervised learning algorithms have achieved remarkable progress in various NLP challenges (LeCun, Bengio, and Hinton 2015). Most supervised learning algorithms rely on the assumption that data distribution during training is the same as that during test. However, in many real-life scenarios, the data distribution of interest at test-time might be different from that during training. The process of collecting new datasets that reflect the new distribution is usually not scalable due to monetary as well as time constraints.

Graph Neural Networks (GNNs) have received a lot of interest in the recent times. From the early spectral architectures that could only operate on undirected graphs per a transductive learning paradigm to the current state of the art spatial ones that can apply inductively to arbitrary graphs, GNNs have seen significant contributions from the research community. In this paper, we discuss some theoretical tools to better visualize the operations performed by state of the art spatial GNNs. We analyze the inner workings of these architectures and introduce a simple concept, node masking, that allows them to generalize and scale better. To empirically validate the theory, we perform several experiments on two widely used benchmark datasets for node classification in both transductive and inductive settings.

As the field of Spoken Dialogue Systems and Conversational AI grows, so does the need for tools and environments that abstract away implementation details in order to expedite the development process, lower the barrier of entry to the field, and offer a common test-bed for new ideas. In this paper, we present Plato, a flexible Conversational AI platform written in Python that supports any kind of conversational agent architecture, from standard architectures to architectures with jointly-trained components, single- or multi-party interactions, and offline or online training of any conversational agent component. Plato has been designed to be easy to understand and debug and is agnostic to the underlying learning frameworks that train each component.

As shown in Example 11.20, we compile our dense sentiment classifier with a line of code that should already be familiar from recent chapters, except that--because we have a single output neuron within a binary classifier--we use binary_crossentropy cost in place of the categorical_crossentropy cost we used for our multiclass MNIST classifiers.

First, let's import the necessary modules. PyTorch modules are quite straight forward. You can build a fully functional neural network using Tensor computation alone, but this is not what this article is about. We'll make use of the more powerful and convenient torch.nn, For those of you interested in knowing how to do this from'scratch scratch', visit this fantastic PyTorch official tutorial by Jeremy Howard.

Find here a listing of the latest industry news in genomics, genetics, precision medicine, and beyond. Updates are provided on a monthly basis. Sign-Up for our newsletter and never miss out on the latest news and updates. As 2019 came to an end, Veritas Genetics struggled to get funding due to concerns it had previously taken money from China. It was forced to cease US operations and is in talks with potential buyers. The GenomeAsia 100K Project announced its pilot phase with hopes to tackle the underrepresentation of non-Europeans in human genetic studies and enable genetic discoveries across Asia. Veritas Genetics, the start-up that can sequence a human genome for less than $600, ceases US operations and is in talks with potential buyers Veritas Genetics ceases US operations but will continue Veritas Europe and Latin America. It had trouble raising funding due to previous China investments and is looking to be acquired. Illumina loses DNA sequencing patents The European Patent ...

With just their input cables, human neurons can perform difficult logic calculations previously only seen in entire neural networks. To restate: human neurons are far more powerful devices than originally thought. And if deep learning algorithms--the AI method loosely based on the brain that's taken our world by storm--take note, they can be too. Those are unconventional, fighting words. For 70 years, neurons were considered the basic computational unit of the brain.

Researchers at Google-owned DeepMind discovered that a recent development in computer science regarding reinforcement learning could be applied to how the brain's dopamine system works. The research, published in the scientific journal Nature, has implications for better understanding mental health, as well as for learning and motivation disorders. It found evidence that something referred to as "distributional reinforcement learning" used in AI algorithms actually mimics the dopamine reward system within the brain. The technique allows the brain to use distribute the probability of future rewards rather than focussing on actions that result in immediate rewards. "We found that dopamine neurons in the brain were each tuned to different levels of pessimism or optimism," the researchers explained in a blog post describing their discovery.