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Prioritized Architecture Sampling with Monto-Carlo Tree Search

NAS-Bench-Macro This repository includes the benchmark and code for NAS-Bench-Macro in paper “Prioritized Architecture Sampling with Monto-Carlo Tree Search”, CVPR2021. NAS-Bench-Macro is a NAS benchmark on macro search space. The NAS-Bench-Macro consists of 6561 networks and their test accuracies, parameters, and FLOPs on CIFAR-10 dataset. Each architecture in NAS-Bench-Macro is trained from scratch isolatedly. Benchmark All the evaluated architectures are stored in file nas-bench-macro_cifar10.json with the following format: { arch1: { test_acc: [float, float, float], // the test accuracies of […]

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Images

Polygonal Building Segmentation by Frame Field Learning

Polygonization-by-Frame-Field-Learning This repository contains the code for our fast polygonal building extraction from overhead images pipeline. We add a frame field output to an image segmentation neural network to improve segmentation quality and provide structural information for the subsequent polygonization step. Figure 1: Close-up of our additional frame field output on a test image. Figure 2: Given an overhead image, the model outputs an edge mask, an interior mask,and a frame field for buildings. The total loss includes terms that […]

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Network

An unofficial PyTorch implemenation of EventProp

This is an unofficial PyTorch implemenation of EventProp, a method to compute exact gradients for Spiking Neural Networks. The repo currently contains code to train a 1-layer Spiking Neural Network with leaky integrate-and-fire (LIF) neurons for 10-way digit classification on MNIST. Implementation Details The implementation of EventProp itself is in models.py, in form of the forward and backward methods of the SpikingLinear module, which compute the forward passes of a spiking layer and its adjoint layer. In particular, the manual_forward […]

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Visualization

Leaderboard and Visualization for RLCard with python

This is the GUI support for the RLCard project and DouZero project. RLCard-Showdown provides evaluation and visualization tools to help understand the performance of the agents. It includes a replay module, where you can analyze the replays, and a PvE module, where you can play with the AI interactively. Currently, we only support Leduc Hold’em and Dou Dizhu. The frontend is developed with React. The backend is based on Django and Flask. Have fun! Cite this work Zha, Daochen, et […]

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Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 84: Order Agnostic Cross-Entropy

I tend to be a little biased against autoregressive models. The way they operate: say exactly one subword, think for a while, and then say again exactly one subword, just does not sound natural to me. Moreover, with current models, a subword can be anything from a single character to a word as long as “Ausgußreiniger”. Non-autoregressive models generate everything in a single step. That does seem to be really natural either, but at least they offer an interesting alternative. […]

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Python

Simplify Complex Numbers With Python

Most general-purpose programming languages have either no support or limited support for complex numbers. Your typical options are learning some specialized tool like MATLAB or finding a third-party library. Python is a rare exception because it comes with complex numbers built in. Despite the name, complex numbers aren’t complicated! They’re convenient in tackling practical problems that you’ll get a taste of in this tutorial. You’ll explore vector graphics and sound frequency analysis, but complex numbers can also help in drawing […]

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Advanced, NLP, Project, Python, Structured Data, Technique, Text

All You Need to know about BERT

This article was published as a part of the Data Science Blogathon Introduction Machines understand language through language representations. These language representations are in the form of vectors of real numbers. Proper language representation is necessary for a better understanding of the language by the machine. Language representations are of two types: (i) Context-free language representation such as Glove and Word2vec where embeddings for each token in the vocabulary are constant and it doesn’t depend on the context of the word. […]

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Advanced, NLP, Project, Python, Text, Unstructured Data

Analyzing customer feedbacks using Aspect Based Sentiment Analysis

This article was published as a part of the Data Science Blogathon Introduction With the advancement in technology, the growth of social media like Facebook, Twitter, Instagram has been a platform for the customers to give feedback to the businesses based on their satisfaction. The reviews posted by customers are the globally trusted source of genuine content for other users. Customer feedback serves as the third-party validation tool to build user trust in the brand. For understanding these customer feedbacks […]

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Beginner, Data Cleaning, NLP, Python, Text, Unstructured Data, Word Embeddings

Part- 6: Step by Step Guide to Master Natural Language Processing (NLP) in Python

This article was published as a part of the Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In the previous article of this series, we completed the statistical or frequency-based word embedding techniques, which are pre-word embedding era techniques. So, in this article, we will discuss the recent word-era embedding techniques. NOTE: In recent word-era embedding, there are many such techniques but in this article, we will discuss only the Word2Vec […]

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Advanced, NLP, Project, Python, Text

Part- 1: Step by Step Guide to Master Natural Language Processing (NLP) in Python

This article was published as a part of the Data Science Blogathon Introduction Computers and Machines are great while working with tabular data or Spreadsheets. However, human beings generally communicate in words and sentences, not in the form of tables or spreadsheets, and most of the information that humans speak or write is present in an unstructured manner. So it is not very understandable for computers to interpret these languages. Therefore, In natural language processing (NLP), our aim is to make […]

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