A Comprehensive Guide to Attention Mechanism in Deep Learning for Everyone

 Overview The attention mechanism has changed the way we work with deep learning algorithms Fields like Natural Language Processing (NLP) and even Computer Vision have been revolutionized by the attention mechanism We will learn how this attention mechanism works in deep learning, and even implement it in Python   Introduction “Every once in a while, a revolutionary product comes along that changes everything.” – Steve Jobs What does one of the most famous quotes of the 21st century have to do with […]

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An Essential Guide to Pretrained Word Embeddings for NLP Practitioners

Overview Understand the importance of pretrained word embeddings Learn about the two popular types of pretrained word embeddings – Word2Vec and GloVe Compare the performance of pretrained word embeddings and learning embeddings from scratch   Introduction How do we make machines understand text data? We know that machines are supremely adept at dealing and working with numerical data but they become sputtering instruments if we feed raw text data to them. The idea is to create a representation of words […]

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How Part-of-Speech Tag, Dependency and Constituency Parsing Aid In Understanding Text Data?

Overview Learn about Part-of-Speech (POS) Tagging, Understand Dependency Parsing and Constituency Parsing   Introduction Knowledge of languages is the doorway to wisdom.                                                               – Roger Bacon I was amazed that Roger Bacon gave the above quote in the 13th century, and it still holds, Isn’t it? I am sure that […]

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Simple Text Multi Classification Task Using Keras BERT

This article was published as a part of the Data Science Blogathon. Introduction BERT is a really powerful language representation model that has been a big milestone in the field of NLP. It has greatly increased our capacity to do transfer learning in NLP. It comes with great promise to solve a wide variety of NLP tasks. Definitely you will gain great knowledge by the end of this article, keep reading. I am sure you will get good hands-on experience […]

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Suppressing Mislabeled Data via Grouping and Self-Attention

Deep networks achieve excellent results on large-scale clean data but degrade significantly when learning from noisy labels. To suppressing the impact of mislabeled data, this paper proposes a conceptually simple yet efficient training block, termed as Attentive Feature Mixup (AFM), which allows paying more attention to clean samples and less to mislabeled ones via sample interactions in small groups… Specifically, this plug-and-play AFM first leverages a textit{group-to-attend} module to construct groups and assign attention weights for group-wise samples, and then […]

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Low-Variance Policy Gradient Estimation with World Models

In this paper, we propose World Model Policy Gradient (WMPG), an approach to reduce the variance of policy gradient estimates using learned world models (WM’s). In WMPG, a WM is trained online and used to imagine trajectories… The imagined trajectories are used in two ways. Firstly, to calculate a without-replacement estimator of the policy gradient. Secondly, the return of the imagined trajectories is used as an informed baseline. We compare the proposed approach with AC and MAC on a set […]

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Teaching a GAN What Not to Learn

Generative adversarial networks (GANs) were originally envisioned as unsupervised generative models that learn to follow a target distribution. Variants such as conditional GANs, auxiliary-classifier GANs (ACGANs) project GANs on to supervised and semi-supervised learning frameworks by providing labelled data and using multi-class discriminators… In this paper, we approach the supervised GAN problem from a different perspective, one that is motivated by the philosophy of the famous Persian poet Rumi who said, “The art of knowing is knowing what to ignore.” […]

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A Framework for Learning Predator-prey Agents from Simulation to Real World

In this paper, we propose an evolutionary predatorprey robot system which can be generally implemented from simulation to the real world. We design the closed-loop robot system with camera and infrared sensors as inputs of controller… Both the predators and prey are co-evolved by NeuroEvolution of Augmenting Topologies (NEAT) to learn the expected behaviours. We design a framework that integrate Gym of OpenAI, Robot Operating System (ROS), Gazebo. In such a framework, users only need to focus on algorithms without […]

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Ray-marching Thurston geometries

We describe algorithms that produce accurate real-time interactive in-space views of the eight Thurston geometries using ray-marching. We give a theoretical framework for our algorithms, independent of the geometry involved… In addition to scenes within a geometry $X$, we also consider scenes within quotient manifolds and orbifolds $X / Gamma$. We adapt the Phong lighting model to non-euclidean geometries. The most difficult part of this is the calculation of light intensity, which relates to the area density of geodesic spheres. […]

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Passport-aware Normalization for Deep Model Protection

Despite tremendous success in many application scenarios, deep learning faces serious intellectual property (IP) infringement threats. Considering the cost of designing and training a good model, infringements will significantly infringe the interests of the original model owner… Recently, many impressive works have emerged for deep model IP protection. However, they either are vulnerable to ambiguity attacks, or require changes in the target network structure by replacing its original normalization layers and hence cause significant performance drops. To this end, we […]

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