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Ultimately, machine learning models are used to make decisions.

In Appendix A, we analyze the expressiveness of V ariational Structured Dropout (VSD) through the approximate posterior structure and the parameterization of prior hierarchy. In Appendix B, we provide proof for the KL condition in VSD. In Appendix C, we derive in details the variational objective of VSD with hierarchical prior. A essential question is how expressive the Dropout posterior in VSD is. MC Dropout objective is a lower bound on the scale mixture model's marginal MAP objective.

--Robotic dressing assistance has the potential to improve the quality of life for individuals with limited mobility. Existing solutions predominantly rely on rigid robotic manipulators, which have challenges in handling deformable garments and ensuring safe physical interaction with the human body. Prior robotic dressing methods require excessive operation times, complex control strategies, and constrained user postures, limiting their practicality and adaptability. This paper proposes a novel soft robotic dressing system, the Self-Wearing Adaptive Garment (SW AG), which uses an unfurling and growth mechanism to facilitate autonomous dressing. Unlike traditional approaches, the SW AG conforms to the human body through an unfurling-based deployment method, eliminating skin-garment friction and enabling a safer and more efficient dressing process. We present the working principles of the SW AG, introduce its design and fabrication, and demonstrate its performance in dressing assistance. The proposed system demonstrates effective garment application across various garment configurations, presenting a promising alternative to conventional robotic dressing assistance. RESSING is a fundamental activity of daily living that directly impacts independence and quality of life. For individuals with physical disabilities, the elderly, and those recovering from injuries, dressing can be a significant challenge [1]. The inability to dress independently often leads to a loss of autonomy, increased reliance on caregivers, and a diminished sense of dignity.

SW AG: Item Recommendations using Convolutions on Weighted Graphs Amit Pande, Kai Ni and V enkataramani Kini Data Sciences, Target Corporation Abstract --Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. In this work, we present a Graph Convolutional Network (GCN) algorithm SW AG (Sample Weight and AGgregate), which combines efficient random walks and graph convolutions on weighted graphs to generate embed-dings for nodes (items) that incorporate both graph structure as well as node feature information such as item-descriptions and item-images. The three important SWAG operations that enable us to efficiently generate node embeddings based on graph structures are (a) Sampling of graph to homogeneous structure, (b) W eighting the sampling, walks and convolution operations, and (c) using AGgregation functions for generating convolutions. The work is an adaptation of graphSAGE over weighted graphs. We deploy SW AG at T arget and train it on a graph of more than 500K products sold online with over 50M edges. Offline and online evaluations reveal the benefit of using a graph-based approach and the benefits of weighing to produce high quality embeddings and product recommendations. I NTRODUCTION Convolutional Neural Networks (CNNs) are used to establish state-of-the-art performance on many Computer Vision applications [2]. CNNs consist of a series of parameterized convolutional layers operating locally (around neighboring pixels of an image) to obtain hierarchy of features about an image. The first layer learns simple edge-oriented detectors. Higher layers build up on the learning of lower layers to learn more complex features and objects. The success of CNNs in Computer Vision has inspired efforts to extend the convolu-tional operation from regular grids (2D images), to graph-structured data [9]. Graphs, such as social networks, word co-occurrence networks, guest purchasing behavior, protein-protein interactions and communication networks, occur naturally in various real-world applications. Analyzing them yields insights into the structure of society, language, and different patterns of communication.

Stories generated with neural language models have shown promise in grammatical and stylistic consistency. However, the generated stories are still lacking in common sense reasoning, e.g., they often contain sentences deprived of world knowledge. W e propose a simple multi-task learning scheme to achieve quantitatively better common sense reasoning in language models by leveraging auxiliary training signals from datasets designed to provide common sense grounding. When combined with our two-stage fine-tuning pipeline, our method achieves improved common sense reasoning and state-of-the-art perplexity on the Writing-Prompts ( Fan et al., 2018) story generation dataset.