Beginner, Data Science, NLP, Python, Text, Word Embeddings

Part 8: Step by Step Guide to Master NLP – Useful Natural Language Processing Tasks

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). Up to part-7 of this series, we completed the most useful concepts in NLP. While going away in this series, let’s first discuss some of the useful tasks of NLP so that you have much clarity about what you can do by learning the NLP. After this part, we will start our discussion on […]

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Advanced, NLP

Part 11: Step by Step Guide to Master NLP – Syntactic Analysis

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, we discussed an entity extraction technique named i.e, Named Entity Recognition. There is also another entity extraction technique which is also a popular technique named Topic Modeling, which we will discuss in the subsequent articles of our blog series. So, In this article, we will deep dive into Syntactic Analysis, […]

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Advanced, Data Engineering, Docker, Machine Learning, Model Deployment, NLP, Profile Building

Deploying Machine learning Application on AWS Fargate

Amazon Web Services(AWS) offers reliable, scalable, and cost-effective cloud computing services. It provides Infrastructure as a service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS) also a new model known as Function as a Service(FaaS) eg. AWS Lambda which is a serverless entity. Before going further, if you don’t have an AWS account please create one to follow along with the hands-on. Amazon Elastic Container Service(ECS) Amazon ECS is a container orchestration platform developed by Amazon, it is similar to […]

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Facebook NLP Research
NLP

Not All Memories are Created Equal: Learning to Forget by Expiring

Abstract Attention mechanisms have shown promising results in sequence modeling tasks that require long term memory. Recent work investigated mechanisms to reduce the computational cost of preserving and storing memories (Rae et al., 2020). However, not all content in the past is equally important to remember. We propose Expire-Span, a method that learns to retain the most important information and expire the irrelevant information. This forgetting of memories enables Transformers to scale to attend over tens of thousands of previous […]

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Facebook NLP Research
NLP

Improving Speech Translation by Understanding and Learning from the Auxiliary Text Translation Task

Abstract Pretraining and multitask learning are widely used to improve the speech to text translation performance. In this study, we are interested in training a speech to text translation model along with an auxiliary text to text translation task. We conduct a detailed analysis to understand the impact of the auxiliary task on the primary task within the multitask learning framework. Our analysis confirms that multitask learning tends to generate similar decoder representations from different modalities and preserve more information […]

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Facebook NLP Research
NLP

Adaptive Multi-Channel Signal Enhancement Based on Multi-Source Contribution Estimation

August 23, 2021 By: Jacob Donley, Vladimir Tourbabin, Boaz Rafaely, Ravish Mehra Abstract Automated solutions to multi-channel signal enhancement for improving speech communication in noisy environments has become a popular goal among the research community. Many proposed approaches focus on adapting to speech signals based on their temporal characteristics but these methods are primarily limited to specific types of desired and undesired sound sources. This paper outlines a new method to adapt to desired and undesired signals using their spatial […]

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Maps

Riemannian Convex Potential Maps with python

rcpm This repository is by Brandon Amos, Samuel Cohen and Yaron Lipman and contains the JAX source code to reproduce the experiments in our ICML 2021 paper on Riemannian Convex Potential Maps. Modeling distributions on Riemannian manifolds is a crucial component in understanding non-Euclidean data that arises, e.g., in physics and geology. The budding approaches in this space are limited by representational and computational tradeoffs. We propose and study a class of flows that uses convex potentials from Riemannian optimal […]

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Maps

Real-time Instance Segmentation with Discriminative Orientation Maps

OrienMask This repository implements the framework OrienMask for real-time instance segmentation. It achieves 34.8 mask AP on COCO test-dev at the speed of 42.7 FPS evaluated with a single RTX 2080Ti. (log) Paper: Real-time Instance Segmentation with Discriminative Orientation Maps Installation Please see INSTALL.md to prepare the environment and dataset. Usage Place the pre-trained backbone (link) and trained model (link) as follows for convenience (otherwise update the corresponding path in configurations): ├── checkpoints │ ├── pretrained │ │ ├──pretrained_darknet53.pth │ […]

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Logging

Summarize LSF job properties by parsing log files

lsf_stats Summarize LSF job properties by parsing log files of workflows executed by Snakemake. Installation $ pip install lsf_stats Usage $ lsf_stats –help Usage: lsf_stats [OPTIONS] COMMAND [ARGS]… Summarize LSF job properties by parsing log files. Options: –version Show the version and exit. –help Show this message and exit. Commands: gather Aggregate information from log files in single dataframe. summarize Summarize and visualize aggregated information. Example Assume that you executed your Snakemake workflow using the lsf-profile and all generated log […]

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