activity
20142019
most citedNeural Speed Reading with Structural-Jump-LSTM

16 citations · 26 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL201916 cited

Neural Speed Reading with Structural-Jump-LSTM

Christian Hansen, Casper Hansen, Stephen Alstrup +2

Recurrent neural networks (RNNs) can model natural language by sequentially 'reading' input tokens and outputting a distributed representation of each token. Due to the sequential…

cs.IR20197 cited

Modelling Sequential Music Track Skips using a Multi-RNN Approach

Christian Hansen, Casper Hansen, Stephen Alstrup +2

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 nee…

cs.IR20191 cited

Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking

Casper Hansen, Christian Hansen, Stephen Alstrup +2

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 sente…

cs.DS20161 cited

Near-Optimal Induced Universal Graphs for Bounded Degree Graphs

Mikkel Abrahamsen, Stephen Alstrup, Jacob Holm +2

A graph is an induced universal graph for a family of graphs if every graph in is a vertex-induced subgraph of . For the family of all undirected graphs on verti…

cs.DS20141 cited

Adjacency labeling schemes and induced-universal graphs

Stephen Alstrup, Haim Kaplan, Mikkel Thorup +1

We describe a way of assigning labels to the vertices of any undirected graph on up to vertices, each composed of bits, such that given the labels of two vertices, a…