Deep Learning

State-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch

deep-table implements various state-of-the-art deep learning and self-supervised learning algorithms for tabular data using PyTorch. Design Architecture As shown below, each pretraining/fine-tuning model is decomposed into two modules: Encoder and Head. Encoder Encoder has Embedding and Backbone. Embedding makes continuous/categorical features tokenized or simply normalized. Backbone processes the tokenized features. Pretraining/Fine-tuning Head Pretraining/Fine-tuning Head uses Encoder module for training. Implemented Methods Available Modules Encoder – Embedding FeatureEmbedding TabTransformerEmbedding Encoder – Backbone MLPBackbone FTTransformerBackbone SAINTBackbone Model – Head Model – Pretraining […]

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Facebook NLP Research
NLP

Speech Resynthesis from Discrete Disentangled Self-Supervised Representations

Abstract We propose using self-supervised discrete representations for the task of speech resynthesis. To generate disentangled representation, we separately extract low-bitrate representations for speech content, prosodic information, and speaker identity. This allows to synthesize speech in a controllable manner. We analyze various state-of-the-art, self-supervised representation learning methods and shed light on the advantages of each method while considering reconstruction quality and disentanglement properties. Specifically, we evaluate the F0 reconstruction, speaker identification performance (for both resynthesis and voice conversion), recordings’ intelligibility, […]

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Facebook NLP Research
NLP

Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications

November 7, 2021 By: Shuo Sun, Ahmed El-Kishky, Vishrav Chaudhary, James Cross, Francisco Guzmán, Lucia Specia Abstract Sentence-level Quality Estimation (QE) of machine translation is traditionally formulated as a regression task, and the performance of QE models is typically measured by Pearson correlation with human labels. Recent QE models have achieved previously-unseen levels of correlation with human judgments, but they rely on large multilingual contextualized language models that are computationally expensive and thus infeasible for many real-world applications. In this […]

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Facebook NLP Research
NLP

Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network

Abstract Inflectional morphology has since long been a useful testing ground for broader questions about generalization in language and the viability of neural network models as cognitive models of language. Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalization and rule-based models of this system. We perform analyses including behavior experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the […]

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Facebook NLP Research
NLP

Cross-Policy Compliance Detection via Question Answering

Abstract Policy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online platform conforms to community guidelines). This task has been previously instantiated as a form of textual entailment, which results in poor accuracy due to the complexity of the policies. In this paper we propose to address policy compliance detection via decomposing it into question answering, where questions check whether […]

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Package

The Python3 import playground

I have been confused about python modules and packages, this text tries to clear the topic up a bit. Sources: https://chrisyeh96.github.io/2017/08/08/definitive-guide-python-imports.html https://abarker.github.io/understanding_python_imports/ https://stackoverflow.com/questions/44834/can-someone-explain-all-in-python https://docs.python.org/3/reference/import.html https://stackoverflow.com/questions/43059267/how-to-do-from-module-import-using-importlib Modules Each file with extension .py can be used as a module. The module is in source file module_source.py, while it is imported as import module_source. In this example, both the module source file and the importing file are in the same directory. Lets import the module. (the module source includes a print statement print(“module_foo […]

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Tracker

Pytorch-Lightning implementation of the Box-Aware Tracker

Pytorch-Lightning implementation of the Box-Aware Tracker. Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds. ICCV 2021 Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li*, Shuguang Cui Citation @InProceedings{zheng2021box, title={Box-Aware Feature Enhancement for Single Object Tracking on Point Clouds}, author={Chaoda Zheng, Xu Yan, Jiaotao Gao, Weibing Zhao, Wei Zhang, Zhen Li, Shuguang Cui}, journal={ICCV}, year={2021} }   To finish reading, please visit source site

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Shortener URL

Shortens the website URL With Python

It shortens the website URL. How to run the script -Download the library using command pip install pyshorteners in command prompt. How it works It takes the website URL as input. It shortens the given URL and returns the shortened URL. Long URLs are not easy to handle so URLs should be short and sweet so that they are easy to share. pyshorteners is a Python lib to help you short and expand urls using URL Shorteners available. GitHub https://github.com/bindaldhara/URL-shortener […]

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Django

Simple application TodoList django with ReactJS

Django We basically follow the Django REST framework quickstart guide here. Create backend folder with a virtual Python environment: mkdir backend cd backend pipenv install; pipenv shell Install Django and Django REST framework: pipenv install django djangorestframework Create Django project structure: django-admin startproject backend . cd backend django-admin startapp todo cd .. Create Django super user: ./manage.py migrate ./manage.py createsuperuser –email    

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