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At the end of the 1600s, Leibniz started thinking about building a machine that would answer legal questions. However, the first systems applying Artificial Intelligence (AI) to the law didn’t appear before the 1970s. One famous example of these systems is TAXMAN. The first law chatbot DoNotPay was launched in 2015. TAXMAN built a formal model of US tax law. DoNotPay is an online chatbot that was first created to help appeal for parking tickets. Since then, various approaches have been proposed to automate some legal tasks. In this talk, we will overview how recent advances in AI and Natural Language Processing (NLP) allow the analysis of large numbers of legal documents:

• Extraction of interesting entities/data from judgments to construct networks of lawyers and judgments. The network of lawyers is then used to compute metrics to rank lawyers based on their experience, wins/loss ratio, and their importance.
• Automatic compliance checking of privacy policies against the GDPR.

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Much work has been done recently to make neural networks more interpretable, and one approach is to arrange for the network to use only a subset of the available features. In linear models, Lasso (or L1-regularized) regression assigns zero weights to the most irrelevant or redundant features, and is widely used in data science. However the Lasso only applies to linear models. In this webinar, Mr. Lemhadri will introduce LassoNet, a neural network framework with global feature selection. The proposed approach enforces a hierarchy: specifically a feature can participate in a hidden unit only if its linear representative is active. Unlike other approaches to feature selection for neural nets, the method uses a modified objective function with constraints, and so integrates feature selection with the parameter learning directly. As a result, it delivers an entire regularization path of solutions with a range of feature sparsity. On systematic experiments, LassoNet significantly outperforms state-of-the-art methods for feature selection and regression. The LassoNet method uses projected proximal gradient descent, and generalizes directly to deep networks.

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Accurate image segmentation is crucial for medical imaging applications, which typically rely on high-quality manual annotations which is tedious and time-consuming for clinical experts. In this talk, Dr. Gridach will present his recent work on Densely Oriented Pooling Network (DOPNet) for capturing variation in feature size and preserving spatial interconnection in medical imaging segmentation. Dr. Gridach will start with a brief computer vision review before leading to his proposed work.

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The presence of pollutants in the air has a direct impact on our health and causes detrimental changes to our environment. Air quality monitoring is therefore of paramount importance. The high cost of the acquisition and maintenance of accurate air quality stations implies that only a small number of these stations can be deployed in a country. This presentation is about a low-cost approach to monitor air quality in urban areas. By combining Artificial Intelligence (AI) and Internet of Things (IoT), we can improve the spatial resolution of the air monitoring process, and successfully predict air quality based on readily available data.

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Attention mechanisms have improved the performance of NLP tasks while allowing models to remain explainable. Self-attention is currently widely used; however, interpretability is difficult due to the numerous attention distributions. Recent work has shown that model representations can benefit from label-specific information while facilitating interpretation of predictions. We introduce the Label Attention Layer, a new form of self-attention where attention heads represent labels. We test our novel approach by running constituency and dependency parsing experiments on the Penn Treebank (PTB) and the Chinese Treebank (CTB) datasets. The new proposed model achieves state-of-the-art results for both tasks. Moreover, our model requires fewer self-attention layers compared to existing models. Finally, we share our findings that Label Attention heads can learn relations between syntactic categories and show pathways to analyze errors.

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