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<title>Wenjia Xu | PhD Student @ University of Chinese Academy of Sciences</title>
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<h2>Wenjia Xu</h2>
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<div class="image rounded" style="margin-bottom: 1.5em;"><img src="images/photo.jpg" width="200" alt="" /></div>
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<p>PhD candidate</p>
<p>University of Chinese Academy of Sciences</p>
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<p>I am a fourth-year PhD student at the University of Chinese Academy of Sciences, advised by <a href="http://people.ucas.ac.cn/~0001014?language=en" target="_blank">Prof. Yirong Wu</a>. My main area of interest is explaining the behavior of neural networks.</p>
<p>Currently, I'm doing research on Explainable Artificail Inteligience under the supervision of <a href="https://eml-unitue.de/people/zeynep-akata" target="_blank">Prof. Zeynep Akata</a> at <a href="https://www.mpi-inf.mpg.de/home/" target="_blank">Max Plank Institute of Informatics</a>.</p>
<p>My formal background is in Electrical Engineering (B.Sc. hons. 2014 at <a href="http://english.bit.edu.cn/" target="_blank">Beijing Institute of Technology</a>). During Jan.2016 - Jul.2016, I finished my bachelor thesis on ''Evaluation of Privacy-Preserving Methods for Smart Grids'' under the supervision of <a href="https://www.kth.se/profile/oech" target="_blank">Prof. Tobias Oechtering</a> in <a href="https://www.kth.se/" target="_blank">kth</a>.</p>
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<h3 style="text-align: center">News</h3>
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<li>09/2018: I get to Max Plank Institute as a visiting researcher.</li>
<li>06/2019: I get to University of Amsterdam as a visiting researcher.</li>
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<h2 style="text-align: center">Selected Publications</h2>
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<div class="image rounded"><img src="images/compile.png" width="180" alt="" style="border:none;" /></div>
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<h4>Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration</h4>
<p></p>
</header>
<p><span style="text-decoration: underline;">W. Xu</span>, G Xu, Y Wang, X Sun, D Lin, Y Wu<span style="font-weight: bold">Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration</span>, (Remote Sensing), <span style="font-weight: bold; color: #DC143C">Long Oral</span> [<a href='https://arxiv.org/abs/1812.01483'>Link</a>, <a
href="https://www.mdpi.com/2072-4292/10/12/1893">PDF (arXiv)</a>].</p>
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<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/nri.png" width="180" alt="" style="border:none;" /></div>
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<header>
<h4>Neural Relational Inference for Interacting Systems</h4>
<p>Learning the latent interaction graph of a dynamical system</p>
</header>
<p><span style="text-decoration: underline;">T. Kipf*</span>, E. Fetaya*, K. Wang, M. Welling, R. Zemel, <span style="font-weight: bold">Neural Relational Inference for Interacting Systems</span>, (ICML 2018) [<a href='https://arxiv.org/abs/1802.04687'>Link</a>, <a
href="https://arxiv.org/pdf/1802.04687.pdf">PDF (arXiv)</a>, <a href="https://github.com/ethanfetaya/nri">code</a>], *equal contribution.</p>
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<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/molgan.png" width="180" alt="" style="border:none;" /></div>
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<header>
<h4>MolGAN: An implicit generative model for small molecular graphs</h4>
<p>Learning to generate molecular graphs using a combined GAN/RL-based objective</p>
</header>
<p>N. De Cao, <span style="text-decoration: underline;">T. Kipf</span>, <span style="font-weight: bold">MolGAN: An implicit generative model for small molecular graphs</span>, ICML Deep Generative Models Workshop (2018) [<a href='https://arxiv.org/abs/1805.11973'>Link</a>, <a
href="https://arxiv.org/pdf/1805.11973.pdf">PDF (arXiv)</a>, <a href="https://github.com/nicola-decao/MolGAN">code</a>].</p>
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<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/svae.png" width="180" alt="" style="border:none;" /></div>
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<header>
<h4>Hyperspherical Variational Auto-Encoders</h4>
<p>Learning hyperspherical latent spaces</p>
</header>
<p>T. R. Davidson*, L. Falorsi*, N. De Cao*, <span style="text-decoration: underline;">T. Kipf</span>, J. M. Tomczak, <span style="font-weight: bold">Hyperspherical Variational Auto-Encoders</span>, (UAI 2018), <span style="font-weight: bold; color: #DC143C">Plenary Talk</span> [<a href='https://arxiv.org/abs/1804.00891'>Link</a>, <a
href="https://arxiv.org/pdf/1804.00891.pdf">PDF (arXiv)</a>, <a href="http://github.com/nicola-decao/s-vae">code</a>, <a href="https://nicola-decao.github.io/s-vae">blog</a>], *equal contribution.</p>
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<div class="row 200%">
<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/airway.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Extraction of Airways using Graph Neural Networks</h4>
<p>Graph refinement with graph neural nets</p>
</header>
<p>R. Selvan, <span style="text-decoration: underline;">T. Kipf</span>, M. Welling, J. H. Pedersen, J. Petersen, M. de Bruijne, <span style="font-weight: bold">Extraction of Airways using Graph Neural Networks</span>, (MIDL 2018 - short paper) [<a href='https://arxiv.org/abs/1804.04436'>Link</a>, <a
href="https://arxiv.org/pdf/1804.04436.pdf">PDF (arXiv)</a>].</p>
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<div class="row 200%">
<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/rgcn.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Modeling Relational Data with Graph Convolutional Networks</h4>
<p>Link prediction and entity classification on knowledge graphs</p>
</header>
<p>M. Schlichtkrull*, <span style="text-decoration: underline;">T. N. Kipf*</span>, P. Bloem, R. vd Berg, I. Titov, M. Welling, <span style="font-weight: bold">Modeling Relational Data with Graph Convolutional Networks</span>, (ESWC 2018), <span style="font-weight: bold; color: #DC143C">Best Student Research Paper</span> [<a href='https://arxiv.org/abs/1703.06103'>Link</a>, <a
href="https://arxiv.org/pdf/1703.06103.pdf">PDF (arXiv)</a>, <a href="https://github.com/tkipf/relational-gcn">code</a>], *equal contribution.</p>
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<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/gcmc.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Graph Convolutional Matrix Completion</h4>
<p>Collaborative filtering with side information in bipartite graphs</p>
</header>
<p>R. vd Berg, <span style="text-decoration: underline;">T. N. Kipf</span>, M. Welling, <span style="font-weight: bold">Graph Convolutional Matrix Completion</span>, KDD Deep Learning Day (2018), <span style="font-weight: bold; color: #DC143C">Spotlight Talk</span> [<a href='https://arxiv.org/abs/1706.02263'>Link</a>, <a
href="https://arxiv.org/pdf/1706.02263.pdf">PDF (arXiv)</a>, <a href="https://github.com/riannevdberg/gc-mc">code</a>]</p>
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<hr />
<div class="row 200%">
<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/gcn.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Semi-Supervised Classification with Graph Convolutional Networks</h4>
<p>Neural networks for node classification on graphs</p>
</header>
<p><span style="text-decoration: underline;">T. N. Kipf</span>, M. Welling, <span style="font-weight: bold">Semi-Supervised Classification with Graph Convolutional Networks</span> (ICLR 2017) [<a href='http://arxiv.org/abs/1609.02907'>Link</a>, <a
href="https://arxiv.org/pdf/1609.02907.pdf">PDF (arXiv)</a>, <a href="https://github.com/tkipf/gcn">code</a>, <a href="http://tkipf.github.io/graph-convolutional-networks/">blog</a>]</p>
</div>
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<hr />
<div class="row 200%">
<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/gae.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Variational Graph Auto-Encoders</h4>
<p>A latent variable model for graph-structured data</p>
</header>
<p><span style="text-decoration: underline;">T. N. Kipf</span>, M. Welling, <span style="font-weight: bold">Variational Graph Auto-Encoders</span>, (NeurIPS Bayesian Deep Learning Workshop 2016) [<a href='https://arxiv.org/abs/1611.07308'>Link</a>, <a href="http://arxiv.org/pdf/1611.07308v1.pdf">PDF (arXiv)</a>, <a href="https://github.com/tkipf/gae">code</a>]</p>
</div>
</div>
<hr />
<div class="row 200%">
<div class="3u 4u(large) 12u$(medium)">
<div class="image rounded"><img src="images/msc.png" width="180" alt="" style="border:none;" /></div>
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<div class="9u$ 8u$(large) 12u$(medium)">
<header>
<h4>Recurrent Neural Networks for Graph-Based 3D Agglomeration</h4>
<p>M.Sc. thesis (2016), Department of Connectomics, MPI for Brain Research</p>
</header>
<p>Thesis copy available here: [<a href="http://tkipf.github.io/misc/MScThesis_ThomasKipf.pdf">PDF (9 MB)</a>]</p>
</div>
</div>
<hr />
<p style="text-align:center;">For a full list, have a look at my <a href="https://scholar.google.com/citations?user=83HL5FwAAAAJ">Google Scholar</a> page.</p>
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<h2>Curriculum vitae</h2>
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<li><a href="CV_ThomasKipf.pdf" class="button alt big">Download CV</a></li>
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<h2>Get in touch</h2>
<p><span style="font-weight: bold">THOMAS [DOT] KIPF [AT] GMAIL [DOT] COM</span><br /> Amsterdam Machine Learning Lab, University of Amsterdam<br />Science Park 904, 1098 XH Amsterdam, The Netherlands</p>
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