no code implementations • 21 Sep 2021 • Błażej Leporowski, Casper Hansen, Alexandros Iosifidis
Industrial processes are monitored by a large number of various sensors that produce time-series data.
no code implementations • 4 Sep 2021 • Casper Hansen
This thesis addresses the above challenge and makes a number of contributions to representation learning that (i) improve effectiveness of hash codes through more expressive representations and a more effective similarity measure than the current state of the art, namely the Hamming distance, and (ii) improve efficiency of hash codes by learning representations that are especially suited to the choice of search method.
no code implementations • 5 Jul 2021 • Błażej Leporowski, Daniella Tola, Casper Hansen, Alexandros Iosifidis
Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand.
1 code implementation • ACL 2021 • Casper Hansen, Christian Hansen, Lucas Chaves Lima
Most fact checking models for automatic fake news detection are based on reasoning: given a claim with associated evidence, the models aim to estimate the claim veracity based on the supporting or refuting content within the evidence.
1 code implementation • 26 Mar 2021 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
In this work, we propose Multi-Index Semantic Hashing (MISH), an unsupervised hashing model that learns hash codes that are both effective and highly efficient by being optimized for multi-index hashing.
1 code implementation • 26 Mar 2021 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Christina Lioma
While this is highly efficient, each bit dimension is equally weighted, which means that potentially discriminative information of the data is lost.
no code implementations • 2 Feb 2021 • Błażej Leporowski, Daniella Tola, Casper Hansen, Alexandros Iosifidis
In order to do so, first a dataset that fully describes the operation of an industrial robot performing automated screwdriving must be available.
1 code implementation • 22 Dec 2020 • Dongsheng Wang, Casper Hansen, Lucas Chaves Lima, Christian Hansen, Maria Maistro, Jakob Grue Simonsen, Christina Lioma
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.
no code implementations • 25 Nov 2020 • Lucas Chaves Lima, Casper Hansen, Christian Hansen, Dongsheng Wang, Maria Maistro, Birger Larsen, Jakob Grue Simonsen, Christina Lioma
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the 2020 TREC-COVID Challenge) organised by the U. S. National Institute of Standards and Technology (NIST) and its Text Retrieval Conference (TREC) division.
1 code implementation • 1 Jul 2020 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.
1 code implementation • 17 Jun 2020 • Christian Hansen, Casper Hansen, Jakob Grue Simonsen, Birger Larsen, Stephen Alstrup, Christina Lioma
We study whether it is possible to infer if a news headline is true or false using only the movement of the human eyes when reading news headlines.
1 code implementation • 31 May 2020 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes.
no code implementations • 25 Sep 2019 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
To this end, we propose an end-to-end trainable variational hashing-based collaborative filtering approach that uses the novel concept of self-masking: the user hash code acts as a mask on the items (using the Boolean AND operation), such that it learns to encode which bits are important to the user, rather than the user's preference towards the underlying item property that the bits represent.
no code implementations • IJCNLP 2019 • Isabelle Augenstein, Christina Lioma, Dongsheng Wang, Lucas Chaves Lima, Casper Hansen, Christian Hansen, Jakob Grue Simonsen
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
1 code implementation • 3 Jun 2019 • Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors.
no code implementations • 3 Jun 2019 • Casper Hansen, Christian Hansen, Jakob Grue Simonsen, Stephen Alstrup, Christina Lioma
We present a novel unsupervised generative semantic hashing approach, \textit{Ranking based Semantic Hashing} (RBSH) that consists of both a variational and a ranking based component.
no code implementations • 20 Mar 2019 • Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sentences, and (iii) inferring the factuality of the sentences.
1 code implementation • 20 Mar 2019 • Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Modelling sequential music skips provides streaming companies the ability to better understand the needs of the user base, resulting in a better user experience by reducing the need to manually skip certain music tracks.
1 code implementation • ICLR 2019 • Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference.
no code implementations • 13 Nov 2018 • Rastin Matin, Casper Hansen, Christian Hansen, Pia Mølgaard
We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements.