Search Results for author:
Found 20 papers, 10 papers with code
DeepTimeAnomalyViz: A Tool for Visualizing and Post-processing Deep Learning Anomaly Detection Results for Industrial Time-Series
Industrial processes are monitored by a large number of various sensors that produce time-series data.
Representation Learning for Efficient and Effective Similarity Search and Recommendation
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.
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving
Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand.
Automatic Fake News Detection: Are Models Learning to Reason?
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.
Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering
While this is highly efficient, each bit dimension is equally weighted, which means that potentially discriminative information of the data is lost.
Unsupervised Multi-Index Semantic Hashing
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.
AURSAD: Universal Robot Screwdriving Anomaly Detection Dataset
In order to do so, first a dataset that fully describes the operation of an industrial robot performing automated screwdriving must be available.
Multi-Head Self-Attention with Role-Guided Masks
The state of the art in learning meaningful semantic representations of words is the Transformer model and its attention mechanisms.
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
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.
Unsupervised Semantic Hashing with Pairwise Reconstruction
Inspired by this, we present Semantic Hashing with Pairwise Reconstruction (PairRec), which is a discrete variational autoencoder based hashing model.
Factuality Checking in News Headlines with Eye Tracking
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.
Content-aware Neural Hashing for Cold-start Recommendation
NeuHash-CF is modelled as an autoencoder architecture, consisting of two joint hashing components for generating user and item hash codes.
Variational Hashing-based Collaborative Filtering with Self-Masking
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.
MultiFC: A Real-World Multi-Domain Dataset for Evidence-Based Fact Checking of Claims
We contribute the largest publicly available dataset of naturally occurring factual claims for the purpose of automatic claim verification.
Unsupervised Neural Generative Semantic Hashing
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.
Contextually Propagated Term Weights for Document Representation
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors.
Neural Speed Reading with Structural-Jump-LSTM
We present Structural-Jump-LSTM: the first neural speed reading model to both skip and jump text during inference.
Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking
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.
Modelling Sequential Music Track Skips using a Multi-RNN Approach
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.
Predicting Distresses using Deep Learning of Text Segments in Annual Reports
We develop a model that employs the unstructured textual data in the reports as well, namely the auditors' reports and managements' statements.
