In this paper we describe the implementation of a convolutional neural network (CNN) used to assess online review helpfulness. To our knowledge, this is the first use of this architecture to address this problem. We explore the impact of two related factors impacting CNN performance: different word embedding initializations and different input review lengths. We also propose an approach to combining rating star information with review text to further improve prediction accuracy. We demonstrate that this can improve the overall accuracy by 2%. Finally, we evaluate the method on a benchmark dataset and show an improvement in accuracy relative to published results for traditional methods of 2.5% for a model trained using only review text and 4.24% for a model trained on a combination of rating star information and review text.

Artificial Neural Networks (ANNs) are computational models inspired by the central nervous system (especially the brain) of animals and are used to estimate or generate unknown approximation functions relied on large amounts of inputs. Capsule Neural Network (Sabour S, et al.[2017]) is a novel structure of Convolutional Neural Networks which simulates the visual processing system of human brain. In this paper, we introduce psychological theories which called Cognitive Consistency to optimize the routing algorithm of Capsnet to make it more close to the work pattern of human brain. It has been shown in the experiment that a progress had been made compared with the baseline.

Recent machine learning strategies for segmentation tasks have shown great ability when trained on large pixel-wise annotated image datasets. It remains a major challenge however to aggregate such datasets, as the time and monetary cost associated with collecting extensive annotations is extremely high. This is particularly the case for generating precise pixel-wise annotations in video and volumetric image data. To this end, this work presents a novel framework to produce pixel-wise segmentations using minimal supervision. Our method relies on 2D point supervision, whereby a single 2D location within an object of interest is provided on each image of the data. Our method then estimates the object appearance in a semi-supervised fashion by learning object-image-specific features and by using these in a semi-supervised learning framework. Our object model is then used in a graph-based optimization problem that takes into account all provided locations and the image data in order to infer the complete pixel-wise segmentation. In practice, we solve this optimally as a tracking problem using a K-shortest path approach. Both the object model and segmentation are then refined iteratively to further improve the final segmentation. We show that by collecting 2D locations using a gaze tracker, our approach can provide state-of-the-art segmentations on a range of objects and image modalities (video and 3D volumes), and that these can then be used to train supervised machine learning classifiers.

The paper considers the problem of performing a task defined on a model parameter that is only observed indirectly through noisy data in an ill-posed inverse problem. A key aspect is to formalize the steps of reconstruction and task as appropriate estimators (non-randomized decision rules) in statistical estimation problems. The implementation makes use of (deep) neural networks to provide a differentiable parametrization of the family of estimators for both steps. These networks are combined and jointly trained against suitable supervised training data in order to minimize a joint differentiable loss function, resulting in an end-to-end task adapted reconstruction method. The suggested framework is generic, yet adaptable, with a plug-and-play structure for adjusting both the inverse problem and the task at hand. More precisely, the data model (forward operator and statistical model of the noise) associated with the inverse problem is exchangeable, e.g., by using neural network architecture given by a learned iterative method. Furthermore, any task that is encodable as a trainable neural network can be used. The approach is demonstrated on joint tomographic image reconstruction, classification and joint tomographic image reconstruction segmentation.

Many real-world objects are designed by smooth curves, especially in the domain of aerospace and ship, where aerodynamic shapes (e.g., airfoils) and hydrodynamic shapes (e.g., hulls) are designed. To facilitate the design process of those objects, we propose a deep learning based generative model that can synthesize smooth curves. The model maps a low-dimensional latent representation to a sequence of discrete points sampled from a rational Bézier curve. We demonstrate the performance of our method in completing both synthetic and real-world generative tasks. Results show that our method can generate diverse and realistic curves, while preserving consistent shape variation in the latent space, which is favorable for latent space design optimization or design space exploration. Keywords: Shape synthesis, aerodynamic design, hydrodynamic design, generative adversarial networks 1. Introduction Smooth curves are widely used in the geometric design of products, ranging from daily supplies like bottles and drinking glasses to engineering structures such as aerodynamic or hydrodynamic shapes.

From self driving cars to speech recognition, machine learning has really created an impact in Artificial Intelligence in predicting results. Using apps which can predict how you will look like in the next 20yrs, how your skin will get some deformation, this functions are all base on machine learning. But, what really is machine learning, how is it implemented and what measures are taken in training a machine to function without human intervention? Machine learning is a computer science research area that deals with methods to identify and implement systems and algorithms by which a computer can learn based on the examples of a given input. Note that machine learning is not AI (Artificial Intelligence) but just one method of achieving AI.

For the past month, we ranked nearly 250 Machine Learning Open Source Projects to pick the Top 10. We compared projects with new or major release during this period. Mybridge AI ranks projects based on a variety of factors to measure its quality for professionals. Open source projects can be useful for programmers. Hope you find an interesting project that inspires you.

Update: Dota 2 does provide a scripting interface, enabling bots to be written in Lua. However, this limited interface does not enable bots to communicate with remote processes and save data about games played. OpenAI Five is a huge step forward for AI, but it's also really intimidating for AI researchers. Never before has there been so many open tools for building AI systems, but it also feels like the barrier to entry for academics has actually increased over recent years. I posted an open call for any interested parties to build the best StarCraft AI possible back in 2009, and it was open to anyone interested in AI. Now, it seems like you need to have access to closed APIs, massive compute power, and historic training data to make advances in AI.

Web developers are beginning to build artificial intelligence into their apps. More of them are experimenting with new frameworks that use JavaScript to compose machine learning (ML), deep learning (DL), and other AI functions. The most significant JavaScript toolkit for building Web-embedded AI is TensorFlow.js, which Google announced at its developer conference in late March. It enables interactive modeling, training, execution, and visualization of ML, DL, and other AI models. It encompasses an ecosystem of JavaScript tools for ML, including TensorFlow.js

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