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<!DOCTYPE HTML>
<html prefix="og: http://ogp.me/ns#" lang="en">
<head>
<title>Lisbon.ai</title>
<meta name="google-site-verification" content="quS7hlvIkpF0kzpctIX8A5dV-1dwp0o1EXKohAJ-_pM" />
<meta property="og:title" content="Lisbon.AI" />
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<meta name="description" content="Lisbon.ai is a conference about artificial inteligence held in Lisbon, Portugal — where bots and humans come together.">
<meta name="keywords" content="AI, artificial inteligence, lisbon, october, lisbon.ai, Portugal, conference, bots, humans, lisboa, 2017, inteligência artificial, schedule, sponsors, tickets">
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<a href='schedule.html'><h2 class='t-center'>SCHEDULE</h2></a>
<a href='program.html'><h2 class='t-center'>PROGRAM</h2></a>
<a href='team.html'><h2 class='t-center'>TEAM</h2></a>
<a href='faq.html'><h2 class='t-center'>FAQ</h2></a>
<a href='https://www.eventbrite.com/e/lisbonai-tickets-36106108342' target="_blank"><h2 class='button t-center'>GET TICKETS</h2></a>
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<main class='max-width mt-50 flex dir-column'>
<div class='flex dir-row wrap mt-50'>
<h3 class='fifth upper mr-40'>TALKS</h3>
<div class='four-fifths'>
<h4 id='introduction'><b>Introduction to Deep Learning</b></h4>
<p class='mb-10 o-70'>Wang Ling</p>
<p>In this talk, I will introduce the core concepts for the understanding of neural networks.
I will start from very basic concepts that everyone should be familiar with, such as numbers
and basic operators (addition, subtraction and multiplication), and systematically guide the
audience towards every essential concept for the understanding of how neural networks function.
Using a simple example based on a popular educational cartoon, we will start by developing a
simple linear model, in order to illustrate how to define a model and optimizer its parameters.
Then, we show how more complex models can be built (e.g. Multilayer Perceptrons), and describe the
key technologies that enable, such models to be built and trained (e.g. GPUs, Computational graphs).</p>
<h4 id='translation' class='mt-50'><b>Translation Quality Estimation</b></h4>
<p class='mb-10 o-70'>André Martins</p>
<p>
In this talk, I will present a tutorial on translation quality estimation,
a task of growing importance in NLP, due to its potential to reduce post-editing
human effort in disruptive ways. In particular, I will present Unbabel's
quality estimation system, where we achieve remarkable improvements by
exploiting synergies between the related tasks of word-level quality
estimation and automatic post-editing. First, we stack a new, carefully
engineered, neural model into a rich feature-based word-level quality
estimation system. Then, we use the output of an automatic post-editing
system as an extra feature, obtaining a new state of the art for word-level
and sentence-level quality estimation. I will end with some thoughts
about future work in this area.
</p>
<h4 id ='imitation' class='mt-50'><b>Imitation learning for structured prediction in
natural language processing</b></h4>
<p class='mb-10 o-70'>Andreas Vlachos</p>
<p>
In this talk we will see how to use imitation learning to improve
incremental structured prediction models with applications to natural
language understanding and generation.
</p>
</div>
</div>
<div class='flex dir-row wrap mt-50'>
<h3 class='fifth upper mr-40'>WORKSHOPS</h3>
<div class='four-fifths'>
<h4 id='introduction-ai'><b>Introduction to AI</b></h4>
<p class='mb-10 o-70'>Tiago Baptista</p>
<p class='mb-10'>In this workshop we will introduce you to basic concepts of Artificial
Intelligence, using a very hands-on approach. Being an introductory
workshop we will cover a broad range of subjects from different
sub-fields of AI, like reactive agents (or bots), search algorithms,
or evolutionary computation. Using games as an example environment, we
will implement some of the algorithms mentioned, providing a better
(and hopefully more enjoyable) way to learn about them.</p>
<b class="event-title o-97">Instructions</b>
<p class='mb-10'>In order to participate in Tiago Baptista's workshop you need Python 3.5 or Python 3.6 installed (as well as pip3).
You also need the following things: </p>
<p class='mb-10'><b class='mr-5 o-97'>1.</b>IDE (ideally PyCharm)</p>
<p class='mb-10'><b class='mr-5 o-97'>2.</b><a href='https://github.com/tbaptista/pyafai' target='_blank' class='underline'>pyafai</a> — which can be installed with "pip install pyafai".</p>
<p>Make sure you install pyafai with pip3 for Python 3 (you might need to run "pip3 install pyafai" on some systems).</p>
<h4 id='markov' class='mt-50'><b>Markov Chains for Text Generation Tutorial</b></h4>
<p class='mb-10 o-70'>Rodrigo Gomes</p>
<p class='mb-10'>In this tutorial we will go through the basics of
markov models and implement them to achieve a toy use case (generate text).</p>
<p class='mb-10'>We will learn what a markov model is, why they are useful to
model certain processes, and how to implement one and train it. With one
implemented we will train it on existing test data (we will bring a
sample of text to train the model on, but feel free to bring your own),
and generate text.
</p>
<b class="event-title o-97">Instructions</b>
<p class='mb-10'>In order to participate in Rodrigo Gomes's workshop, you need Python 3 (and pip3) installed.<br />
The list of packages you need for the workshop will be revealed later or during the workshop.</p>
<h4 id='deep' class='mt-50'><b>Deep Learning with Tensorflow</b></h4>
<p class='mb-10 o-70'>Miguel Jaques</p>
<p class='mb-10'>This workshop will be an introduction to deep learning. We will discuss
the advantages and disadvantages of deep learning (when to use, when not to use),
understand how neural networks are actually trained, and get our hands
dirty with Tensorflow in order to solve tasks like digit recognition.
</p>
<b class="event-title o-97">Instructions</b>
<p class='mb-10'>In order to take part in Miguel's workshop, you will need Python 3 (and pip 3) and follow the instructions here:</p>
<p class='mb-10'><a href="https://github.com/seuqaj114/lisbonai_workshop_student" target="_blank" class="underline">https://github.com/seuqaj114/lisbonai_workshop_student</a></p>
<h4 id='recommender' class='mt-50'><b>Recommender Systems</b></h4>
<p class='mb-10 o-70'>Ekaterina Stambolieva</p>
<p class='mb-10'>A gentle introduction to recommender systems — content-based systems v.s.
collaborative filtering, dimensionality reduction, similarity metrics.</p>
<b class="event-title o-97">Instructions</b>
<p class='mb-10'>The workshop is language-agnostic and the participants will develop recommender systems algorithms in their preferred programming language. <a href="https://docs.google.com/document/d/1Wzw23QrM12kDPuAxPPyxe9ezoffIhLgRvn6sBqP75tI/" target="_blank" class="underline">Here</a> is a paper about the workshop.</p>
<h4 id='decision' class='mt-50'><b>Visualizing your ML Models</b></h4>
<p class='mb-10 o-70'>Pedro Fonseca & Samuel Hopkin</p>
<p class='mb-10'>In this workshop you will learn some intuitions about how different
machine learning algorithms make decisions, by training different models and
observing the resulting decision boundaries. It will be a highly visual approach
into the different concepts of machine learning, and also of overfitting, and the
tradeoff between variance and bias.</p>
<b class="event-title o-97">Instructions</b>
<p class='mb-10'>The instructions for Pedro & Sam's workshop can be found and followed <a href='https://github.com/LDSSA/lisbon.ai-workshop-setup' target='_blank' class='underline'>here</a>.</p>
</div>
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