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Raffle Chat Product Launch
RaffleChat Product Launch event in Copenhagen, signalling the official unveiling of our exciting new product to our valued customers.
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Articles
Article
September 19, 2021
Here is the future of conversational AI
Unearth the future of conversational AI and how Raffle places itself in AI history.
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Article
September 2, 2021
Open source makes it easier to use deep learning
Read more on how open source increases the ease of using deep learning and how Raffle has built on this technology.
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Article
March 8, 2021
The future of conversational AI
Find out what the future of conversational AI has to offer and how Raffle plans to be a part of technological innovation.
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Article
January 8, 2021
How language modelling has changed NLP
This is the first of a series of four posts about the technology behind Raffle products, the future of AI, and NLP.
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Article
January 8, 2021
How we use open source deep learning models
Discover Raffle's deep learning framework and models to create our unique AI algorithms that are the core of our products.
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Article
December 18, 2018
End-to-end information extraction from documents
The Attend, Copy, Parse architecture is a deep neural network model trained on end-to-end data, that bypasses the need for word-level labels
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Article
November 21, 2017
Recurrent Relational Networks for Complex Relational Reasoning
Learn more about recurrent relational networks to train AI to mimic human behaviors.
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Article
April 3, 2017
Semi-supervised generation with cluster-aware generative models
Deep generative models trained with large amounts of unlabelled data have proven to be powerful within the domain of unsupervised learning.
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Article
January 1, 2017
Hash embeddings for efficient word representations
Learn more about hash embeddings, an efficient method for representing words in a continuous vector form.
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Article
January 1, 2017
A disentangled recognition and nonlinear dynamics model for Unsupervised Learning
Dive into the conversation around unsupervised learning and its impact within Raffle to understand our unique structure.
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Article
October 20, 2016
Neural machine translation with characters and hierarchical encoding
We propose a Neural Machine Translation model with a hierarchical char2word encoder that takes individual characters as input and output.
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Article
February 17, 2016
Auxiliary deep generative models
Deep generative models have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning.
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Article
December 31, 2015
Autoencoding beyond pixels using a learned similarity metric
We present an autoencoder that leverages learned representations to better measure similarities in data space.
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