The problem of lossy compression is about constructing succinct representations of high-dimensional random vectors that retain the features of the data that are relevant for some subsequent task, such as reconstruction subject to a fidelity criterion or statistical inference. When the compressed representation is digital, with constraints imposed by the limitations on the speed of digital transmission oron the available storage space, the corresponding problem of lossy compression falls within the purview of rate-distortion theory[6] and the theory of vector quantization[15]. On the other hand, given recent advances in machine learning using deep neural nets[17], it is of interest to consider'analog' schemes for lossy compression that map the original high-dimensional data to a continuous representation of lower dimensionality[5], and where the reconstruction operations that send the compressed representation back to the original high-dimensional space are implemented bynonlinear maps with a given structure.

Visual relation reasoning is a central component in recent cross-modal analysis tasks, which aims at reasoning about the visual relationships between objects and their properties. These relationships convey rich semantics and help to enhance the visual representation for improving cross-modal analysis. Previous works have succeeded in designing strategies for modeling latent relations or rigid-categorized relations and achieving the lift of performance. However, this kind of methods leave out the ambiguity inherent in the relations because of the diverse relational semantics of different visual appearances. In this work, we explore to model relations by contextual-sensitive embeddings based on human priori knowledge. We novelly propose a plug-and-play relation reasoning module injected with the relation embeddings to enhance image encoder. Specifically, we design upgraded Graph Convolutional Networks (GCN) to utilize the information of relation embeddings and relation directionality between objects for generating relation-aware image representations. We demonstrate the effectiveness of the relation reasoning module by applying it to both Visual Question Answering (VQA) and Cross-Modal Information Retrieval (CMIR) tasks. Extensive experiments are conducted on VQA 2.0 and CMPlaces datasets and superior performance is reported when comparing with state-of-the-art work.

Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substantial training and experience, and even then the rating agreement between two radiologists is not perfect. We have developed a model we call AVRA (Automatic Visual Ratings of Atrophy) based on machine learning methods and trained on 2350 visual ratings made by an experienced neuroradiologist. It provides fast and automatic ratings for Scheltens' scale of medial temporal atrophy (MTA), the frontal subscale of Pasquier's Global Cortical Atrophy (GCA-F) scale, and Koedam's scale of Posterior Atrophy (PA). We demonstrate substantial inter-rater agreement between AVRA's and a neuroradiologist ratings with Cohen's weighted kappa values of $\kappa_w$ = 0.74/0.72 (MTA left/right), $\kappa_w$ = 0.62 (GCA-F) and $\kappa_w$ = 0.74 (PA), with an inherent intra-rater agreement of $\kappa_w$ = 1. We conclude that automatic visual ratings of atrophy can potentially have great clinical and scientific value, and aim to present AVRA as a freely available toolbox.

Learning when to communicate and doing that effectively is essential in multi-agent tasks. Recent works show that continuous communication allows efficient training with back-propagation in multi-agent scenarios, but have been restricted to fully-cooperative tasks. In this paper, we present Individualized Controlled Continuous Communication Model (IC3Net) which has better training efficiency than simple continuous communication model, and can be applied to semi-cooperative and competitive settings along with the cooperative settings. IC3Net controls continuous communication with a gating mechanism and uses individualized rewards foreach agent to gain better performance and scalability while fixing credit assignment issues. Using variety of tasks including StarCraft BroodWars explore and combat scenarios, we show that our network yields improved performance and convergence rates than the baselines as the scale increases. Our results convey that IC3Net agents learn when to communicate based on the scenario and profitability.

Discrete choice models generally assume that model specification is known a priori. In practice, determiningthe utility specification for a particular application remains a difficult task and model misspecification may lead to biased parameter estimates. In this paper, we propose anew mathematical framework for estimating choice models in which the systematic part of the utility specification is divided into an interpretable part and a learning representation partthat aims at automatically discovering a good utility specification from available data. We show the effectiveness of our framework by augmenting the utility specification of the Multinomial Logit Model (MNL) with a new nonlinear representation arising from a Neural Network (NN). This leads to a new choice model referred to as the Learning Multinomial Logit(L-MNL) model. Our experiments show that our L-MNL model outperformed the traditional MNL models and existing hybrid neural network models both in terms of predictive performance and accuracy in parameter estimation. Keywords: Discrete choice models, Neural networks, Utility specification 1. Introduction Discrete Choice Models (DCM) have emerged as a powerful theoretical framework for analyzing individual travel behavior. The goal of these models is to predict the choice among a given set of discrete alternatives (e.g., choice of walking as transportation mode rather than taking the car or the bus), while understanding the behavioral process that led to the specific choice. For many years, the Multinomial Logit Model (MNL) based on a linear utility specification has provided the foundation for the analysis of discrete choice. Despite its oversimplified assumptions regarding the actual decision-making, this model is still commonly used in practice because it enables a high level of interpretability. Interpretability is critical for researchers and practitioners to get insights into the complex human decision-making process. For instance, linear specifications allow for straightforward derivation of the value-of-time (VOT), i.e., the marginal rate of substitution between time and cost, that constitutes a highly relevant measure in a wide range of public transport policy.

Machine Learning (ML) applications on healthcare can have a great impact on people's lives helping deliver better and timely treatment to those in need. At the same time, medical data is usually big and sparse requiring important computational resources. Although it might not be a problem for wide-adoption of ML tools in developed nations, availability of computational resource can very well be limited in third-world nations. This can prevent the less favored people from benefiting of the advancement in ML applications for healthcare. In this project we explored methods to increase computational efficiency of ML algorithms, in particular Artificial Neural Nets (NN), while not compromising the accuracy of the predicted results. We used in-hospital mortality prediction as our case analysis based on the MIMIC III publicly available dataset. We explored three methods on two different NN architectures. We reduced the size of recurrent neural net (RNN) and dense neural net (DNN) by applying pruning of "unused" neurons. Additionally, we modified the RNN structure by adding a hidden-layer to the LSTM cell allowing to use less recurrent layers for the model. Finally, we implemented quantization on DNN forcing the weights to be 8-bits instead of 32-bits. We found that all our methods increased computational efficiency without compromising accuracy and some of them even achieved higher accuracy than the pre-condensed baseline models.

Character-based and word-based methods are two different solutions for Chinese word segmentation, the former exploiting sequence labeling models over characters and the latter using word-level features. Neural models have been exploited for character-based Chinese word segmentation, giving high accuracies by making use of external character embeddings, yet requiring less feature engineering. In this paper, we study a neural model for word-based Chinese word segmentation, by replacing the manually-designed discrete features with neural features in a transition-based word segmentation framework. Experimental results demonstrate that word features lead to comparable performance to the best systems in the literature, and a further combination of discrete and neural features obtains top accuracies on several benchmarks.

After selling the first artificial intelligence generated artwork earlier this year, deep learning algorithms are now tackling portraits -- of made-up people that don't exist. In a research paper, Nvidia recently shared the results of a generative adversarial network (GAN) trained to generate images of people while unsupervised. The concept is based in part on style transfer technology, which transfers the style of one image onto another in order to do anything from building a photo app like Prisma to creating realistic deep fakes. Nvidia researchers redesigned the GAN to use a style transfer, while adjusting that style on each layer of code. The change, the researchers say, allows the software to generate high-level attributes like subtle differences in poses as well as random variation in features.

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Big changes are underway in the world of Natural Language Processing (NLP). The long reign of word vectors as NLP's core representation technique has seen an exciting new line of challengers emerge: ELMo, ULMFiT, and the OpenAI transformer. These works made headlines by demonstrating that pretrained language models can be used to achieve state-of-the-art results on a wide range of NLP tasks. Such methods herald a watershed moment: they may have the same wide-ranging impact on NLP as pretrained ImageNet models had on computer vision. Pretrained word vectors have brought NLP a long way.