September 21, 2020 Python Leave a comment

Python GUI Development with Tkinter: Part 3

This is the third installment of our multi-part series on developing GUIs in Python using Tkinter. Check out the links below for the other parts to this series: Introduction Tkinter is the de facto standard package for building GUIs in Python. In StackAbuse’s first and second part of the Tkinter tutorial, we learned how to use the basic GUI building blocks to create simple interfaces. In the last part of our tutorial, we’ll take a look at a couple of […]

September 21, 2020 Python Leave a comment

Time Series Analysis with LSTM using Python’s Keras Library

Introduction Time series analysis refers to the analysis of change in the trend of the data over a period of time. Time series analysis has a variety of applications. One such application is the prediction of the future value of an item based on its past values. Future stock price prediction is probably the best example of such an application. In this article, we will see how we can perform time series analysis with the help of a recurrent neural […]

September 21, 2020 Python Leave a comment

Lists vs Tuples in Python

Introduction Lists and tuples are two of the most commonly used data structures in Python, with dictionary being the third. Lists and tuples have many similarities. Some of them have been enlisted below: They are both sequence data types that store a collection of items They can store items of any data type And any item is accessible via its index. So the question we’re trying to answer here is, how are they different? And if there is no difference […]

September 21, 2020 Artificial intelligence Leave a comment

Dialogue as Dataflow: A new approach to conversational AI

By the Semantic Machines research team “Easier said than done.” These four words reflect the promise of conversational AI. It takes just seconds to ask When are Megan and I both free? but much longer to find out manually from a calendar. Indeed, almost everything we do with technology can feel like a long path to a short goal. At Microsoft Semantic Machines, we’re working to bridge this gap—to build conversational AI experiences where you can focus on saying what […]

September 21, 2020 3D SHAPE REPRESENTATION, Machine Learning Leave a comment

Novel View Synthesis from Single Images via Point Cloud Transformation

In this paper the argument is made that for true novel view synthesis of objects, where the object can be synthesized from any viewpoint, an explicit 3D shape representation isdesired. Our method estimates point clouds to capture the geometry of the object, which can be freely rotated into the desired view and then projected into a new image… This image, however, is sparse by nature and hence this coarse view is used as the input of an image completion network […]

September 21, 2020 Machine Learning Leave a comment

ShapeAssembly: Learning to Generate Programs for 3D Shape Structure Synthesis

Manually authoring 3D shapes is difficult and time consuming; generative models of 3D shapes offer compelling alternatives. Procedural representations are one such possibility: they offer high-quality and editable results but are difficult to author and often produce outputs with limited diversity… On the other extreme are deep generative models: given enough data, they can learn to generate any class of shape but their outputs have artifacts and the representation is not editable. In this paper, we take a step towards […]

September 21, 2020 CAUSAL INFERENCE, Machine Learning Leave a comment

Understanding Effects of Editing Tweets for News Sharing by Media Accounts through a Causal Inference Framework

To reach a broader audience and optimize traffic toward news articles, media outlets commonly run social media accounts and share their content with a short text summary. Despite its importance of writing a compelling message in sharing articles, research community does not own a sufficient level of understanding of what kinds of editing strategies are effective in promoting audience engagement… In this study, we aim to fill the gap by analyzing the current practices of media outlets using a data-driven […]

September 21, 2020 IMAGE CAPTIONING, Machine Learning Leave a comment

Image Captioning with Attention for Smart Local Tourism using EfficientNet

Smart systems have been massively developed to help humans in various tasks. Deep Learning technologies push even further in creating accurate assistant systems due to the explosion of data lakes… One of the smart system tasks is to disseminate users needed information. This is crucial in the tourism sector to promote local tourism destinations. In this research, we design a model of local tourism specific image captioning, which later will support the development of AI-powered systems that assist various users. […]

September 20, 2020 Machine Learning, METRIC LEARNING Leave a comment

S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning

Deep Metric Learning (DML) provides a crucial tool for visual similarity and zero-shot retrieval applications by learning generalizing embedding spaces, although recent work in DML has shown strong performance saturation across training objectives. However, generalization capacity is known to scale with the embedding space dimensionality… Unfortunately, high dimensional embeddings also create higher retrieval cost for downstream applications. To remedy this, we propose S2SD – Simultaneous Similarity-based Self-distillation. S2SD extends DML with knowledge distillation from auxiliary, high-dimensional embedding and feature spaces […]

September 20, 2020 ADVERSARIAL TRAINING, Machine Learning Leave a comment

Large Norms of CNN Layers Do Not Hurt Adversarial Robustness

Since the Lipschitz properties of convolutional neural network (CNN) are widely considered to be related to adversarial robustness, we theoretically characterize the $ell_1$ norm and $ell_infty$ norm of 2D multi-channel convolutional layers and provide efficient methods to compute the exact $ell_1$ norm and $ell_infty$ norm. Based on our theorem, we propose a novel regularization method termed norm decay, which can effectively reduce the norms of CNN layers… Experiments show that norm-regularization methods, including norm decay, weight decay, and singular value […]

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