Daniel Daza

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Daniel Daza

Daniel Daza

@danieldazac

Postdoc at Amsterdam UMC, working on machine learning and graphs.

Amsterdam, The Netherlands 加入时间 Şubat 2016
311 关注719 粉丝
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Daniel Daza
Daniel Daza@danieldazac·
It's been great to be part of this collaboration! In my opinion a neat result: graph embeddings trained for 1-hop link prediction generalize well to complex queries, with a tiny increase in parameters and the power of t-norms and t-conorms.
Erik Arakelyan@_kire_kara_

Excited to announce that we will be presenting our paper "Adapting Neural Link Predictors for Data-Efficient Complex Query Answering" at @NeurIPSConf Done with the most amazing researchers - @PMinervini @danieldazac @michaelcochez @IAugenstein paper: arxiv.org/abs/2301.12313

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Daniel Daza
Daniel Daza@danieldazac·
Congrats to all the UnRavL team for a Best Paper Honorable Mention at the 2024 Learning on Graphs Conference! UnRavL expands the types of queries that we can answer over incomplete knowledge graphs🧙 Read the paper here: openreview.net/pdf?id=183XrFq…
Learning on Graphs Conference 2025@LogConference

🏆 Best Paper Award & Honorable Mention 🏆 Congratulations to our outstanding authors for their exceptional contributions to LoG 2024! 🎉 📜 Your work inspires the entire graph and geometric ML community!

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Daniel Daza
Daniel Daza@danieldazac·
@elan_marko Thanks Elan! I really like the work you did with StATIK :)
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Elan Sopher Markowitz
Elan Sopher Markowitz@elan_marko·
@danieldazac Congratulations! Your Inductive Entity Representations from Text work was very helpful in developing some of my own research and furthering the field of inductive KG representation
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sirolf
sirolf@sirolfg·
🚀 Don't miss Tamara Cucumides’ talk at LOG 2024 at 19h10 CET about "UnRavL: A Neuro-Symbolic Framework for Answering Graph Pattern Queries in Knowledge Graphs". openreview.net/forum?id=183Xr… @LogConference
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Andrei Manolache
Andrei Manolache@andreimano2·
Ever wanted to rewire a graph but you couldn't decide on what heuristic to use👀? Fear not! Our #ICLR24 PR-MPNN method eliminates the need to ask ChatGPT for a list of interesting graph properties and learns how to automatically do rewiring in an end-to-end manner 🤯. 🧵 1/n
Andrei Manolache tweet media
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Vera Provatorova 🌻
Vera Provatorova 🌻@vera__pro·
Gave my final RiR presentation at @KBNLresearch today and got some nice gifts 😊 It was super fun, hartelijk dank! (Also achievement unlocked: answered all questions in Dutch 🧡)
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Daniel Daza
Daniel Daza@danieldazac·
What excites me about Sora: imagining what we could achieve if, instead of a bunch of layers that compute attention over everything, trained with astronomical amounts of data and compute; these models had stronger biases for representing objects and relations compositionally.
François Chollet@fchollet

With Sora, the evidence is piling up quickly. Now we're operating at scale. And the evidence will keep piling up with new models going forward -- even faster. And the evidence so far is resounding -- you really are looking at latent-space collages and interpolations. The inner physics model doesn't generalize to novel situations at all. It's not just that you can't use it as a reliable replacement for a fluid dynamics simulator to design a new aircraft or as a gravity simulator to design a new marble run course...

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Jakub Tomczak
Jakub Tomczak@jmtomczak·
It's going to happen! What? Generative Modeling Summer School 2024 (#GeMSS24)! When: June 24-28, 2024 Where: Eindhoven, the Netherlands (@TUeindhoven, @Brainport_int) Info: gemss.ai Application deadline: March 31, 2024 Don't wait, apply!
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Taraneh Younesian
Taraneh Younesian@TYounesian·
If you’re going to #NeurIPS2023, check out our poster GRAPES in GLFrontiers workshop (December 15th)! 🍇 GRAPES is a GFlowNet-based adaptive graph sampling method to scale GNNs to large graphs. Paper: arxiv.org/abs/2310.03399
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Intelligent Data Engineering Lab
Intelligent Data Engineering Lab@INDE_LAB_AMS·
In this exciting call, we also have an opening for an assistant professor in data engineering. Check it out.
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Daniel Daza
Daniel Daza@danieldazac·
In knowledge graphs, the triple representation (subject, predicate, object) tells only a part of the story. Join us at #CIKM2023 to learn about more expressive paradigms and the interesting prediction problems that we can solve with them!
Bo Xiong@BoXiongs

Thrilled to announce that we will be giving a tutorial with @danieldazac @michaelcochez and Mojtaba on "Reasoning beyond Triples: Recent Advances in Knowledge Graph Embeddings", at #cikm2023. Slides will be available on the tutorial website kg-beyond-triple.github.io. Stay tuned!

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发现

@vera__pro @elan_marko @_kire_kara_ @LogConference @SemanticsConf @andreimano2 @EmilevanKrieken @ChendiQian