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Caros, </div>
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Gostaria de convidá-los para o seminário desta semana promovido pela FGV/EMAp.</div>
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Atenciosamente</div>
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Duda</div>
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<b><span style="font-size:11.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#2F5597"> </span></b><b><span style="font-size:11.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#1F497D">
</span></b><b><span style="font-size:11.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#2F5597">LINEAR REPRESENTATION OF PERSISTENCE DIAGRAMS IN TOPOLOGICAL DATA ANALYSIS WITH APPLICATION TO GRAPH CLASSIFICATION
</span></b><b><span style="font-size:11.5pt; line-height:105%; font-family:"Arial",sans-serif; color:#203864"></span></b></p>
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<span style="font-family:"Arial",sans-serif; color:#3B3838"> </span><span style="font-family:"Arial",sans-serif"></span></p>
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<p style="margin-top: 0px; margin-bottom: 0px;margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; text-align:justify; line-height:115%; background:white">
<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Persistent homology plays a central role in TDA by providing a powerful framework to infer relevant topological features from complex data. However, these features
usually come as multisets of points in the plane, the so-called persistence diagrams, and cannot be directly processed as standard features in classical Machine Learning pipelines. Various methods have been proposed among the literature to “vectorize” persistence
diagrams, but they mostly appear as ad-hoc heuristics and remain difficult to use for non-expert. In this talk, after a brief general introduction to persistent homology, we will present a few mathematical properties of the commonly used representations of
persistence diagrams and show how they can be learnt from data. As an illustration, we will focus on a specific graph classification problem and show how persistence can easily lead to state-of-the-art results.</span></p>
<p class="x_x_MsoNormal" style="margin-top: 0px; margin-bottom: 0px;margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; text-align:justify; line-height:115%; background:white">
<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">This is a joint work with Mathieu Carrière (Columbia University), Théo Lacombe and Martin Royer (Inria) and, Yiuchi Ike and Yuhei Umeda (Fujitsu Labs) [AISTATS 2020</span></p>
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<span style="font-family:"Arial",sans-serif; color:#222222"> </span><span style="font-size:8.0pt; line-height:115%; font-family:"Arial",sans-serif; color:#222222"></span></p>
<p class="x_x_xxmsonormal" align="right" style="margin-top: 0px; margin-bottom: 0px;margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; text-align:right">
<b><sup><span style="font-family:"Arial",sans-serif; color:#3B3838"> </span></sup></b><b><sup><span style="font-family:"Arial",sans-serif; color:#1F497D">
</span></sup></b><b><sup><span style="font-family:"Arial",sans-serif; color:#3B3838"> </span></sup></b><sup><span style="font-size:9.0pt; font-family:"Arial",sans-serif; color:#595959">Texto informado pelo autor</span></sup><b><sup><span style="font-size:9.0pt; font-family:"Arial",sans-serif; color:#3B3838">.</span></sup></b><span style="font-family:"Arial",sans-serif"></span></p>
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<b><span style="font-family:"Arial",sans-serif; color:#1F4E79">Quando:</span></b><span style="font-family:"Arial",sans-serif"></span></p>
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">03 de Setembro de 2020, às
<b>16h</b></span><span style="font-family:"Arial",sans-serif; color:#595959"></span></p>
<p class="x_x_xxmsonormal" style="margin-top: 0px; margin-bottom: 0px;margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; line-height:115%">
<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Via Zoom</span><span style="font-family:"Arial",sans-serif; color:#595959"></span></p>
<p class="x_x_MsoNormal" style="margin-top: 0px; margin-bottom: 0px;margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; line-height:115%">
<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Registre-se com antecedência para este seminário:
<br>
</span><span style="font-size:10.0pt; line-height:115%; font-family:"Arial",sans-serif; color:black"><a href="https://ide-fgv-br.zoom.us/meeting/register/tJYrcemrqjsuHd0HAETl9Bd5PGa0cj26gqQ9">https://ide-fgv-br.zoom.us/meeting/register/tJYrcemrqjsuHd0HAETl9Bd5PGa0cj26gqQ9</a>
</span></p>
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<span style="font-size:8.0pt; font-family:"Arial",sans-serif; color:black"> </span></p>
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<span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Após o registro, você receberá um e-mail de confirmação contendo informações sobre conexão no seminário.</span><span style="font-family:"Arial",sans-serif"></span></p>
<p class="x_x_xxmsonormal" style="margin-top: 0px; margin-bottom: 0px;margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; text-align:justify; line-height:115%">
<b><span style="font-family:"Arial",sans-serif; color:#3B3838"> </span></b><span style="font-family:"Arial",sans-serif"></span></p>
<p class="x_x_xxmsonormal" style="margin-top: 0px; margin-bottom: 0px;margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; line-height:115%">
<b><span style="font-size:12.0pt; line-height:115%; font-family:"Arial",sans-serif; color:#1F4E79">Palestrante:</span></b><b><span style="font-size:12.0pt; line-height:115%; font-family:"Arial",sans-serif"></span></b></p>
<p class="x_x_MsoNormal" style="margin-top: 0px; margin-bottom: 0px;margin:0cm 0cm 0.0001pt; font-size:11pt; font-family:Calibri,sans-serif; margin-right:0cm; margin-left:0cm; font-size:12pt; font-family:"Times New Roman",serif; text-align:justify; line-height:115%">
<b><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">Frédéric Chazal
</span></b><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif; color:#595959">is a Directeur de Recherche (senior researcher) at INRIA Saclay Ile-de-France since 2007. After a PhD in pure mathematics, he oriented his research to
computational geometry and topology. He is now leading the DataShape team at INRIA, a group working on Topological Data Analysis (TDA), a recent fast growing field at the crossing of mathematics, statistics, machine learning and computer science. Frederic's
contributions to the field go from fundamental mathematical aspects to algorithmic and applied problems. He published more than 80 papers in major computer sciences conferences and mathematics journals, he co-authored 2 reference books and 3 patents. He is
also an associate editor of 4 international journals. During the last few years Frederic has been heading several national and international research projects on geometric and topological methods in statistics, machine learning and AI. He is also the scientific
head of joint industrial research projects between Inria and several companies such as Fujitsu or the French SME Sysnav.</span><b><span style="font-size:10.5pt; line-height:115%; font-family:"Arial",sans-serif"></span></b></p>
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