Search Results for author: Peter Ebert Christensen

Found 7 papers, 4 papers with code

A Template Is All You Meme

1 code implementation11 Nov 2023 Luke Bates, Peter Ebert Christensen, Preslav Nakov, Iryna Gurevych

Here, to aid understanding of memes, we release a knowledge base of memes and information found on www. knowyourmeme. com, which we call the Know Your Meme Knowledge Base (KYMKB), composed of more than 54, 000 images.

Assessing Neural Network Robustness via Adversarial Pivotal Tuning

no code implementations17 Nov 2022 Peter Ebert Christensen, Vésteinn Snæbjarnarson, Andrea Dittadi, Serge Belongie, Sagie Benaim

We demonstrate that APT is capable of a wide range of class-preserving semantic image manipulations that fool a variety of pretrained classifiers.

Attribute

Searching for Structure in Unfalsifiable Claims

1 code implementation19 Aug 2022 Peter Ebert Christensen, Frederik Warburg, Menglin Jia, Serge Belongie

In this work, we aim to distill such posts into a small set of narratives that capture the essential claims related to a given topic.

Fact Checking Topic Models

Volumetric Disentanglement for 3D Scene Manipulation

no code implementations6 Jun 2022 Sagie Benaim, Frederik Warburg, Peter Ebert Christensen, Serge Belongie

To this end, we propose a volumetric framework for (i) disentangling or separating, the volumetric representation of a given foreground object from the background, and (ii) semantically manipulating the foreground object, as well as the background.

Disentanglement Object

Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis

1 code implementation NeurIPS 2020 Kavi Gupta, Peter Ebert Christensen, Xinyun Chen, Dawn Song

The use of deep learning techniques has achieved significant progress for program synthesis from input-output examples.

Program Synthesis

Autoencoding Undirected Molecular Graphs With Neural Networks

1 code implementation26 Nov 2019 Jeppe Johan Waarkjær Olsen, Peter Ebert Christensen, Martin Hangaard Hansen, Alexander Rosenberg Johansen

to fitting the QM9 dataset, which conforms to the octet rule, and to fitting the ZINC dataset, which contains hypervalent molecules and ions requiring the model to learn a more complex structure rule.

Language Modelling

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