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Found 13 papers, 9 papers with code
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.
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.
Tracking Behavioral Patterns among Students in an Online Educational System
Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn.
Detecting Ghostwriters in High Schools
Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product.
Investigating Writing Style Development in High School
Using this similarity measure, a student's newer essays are compared to their first essays, and a writing style development profile is constructed for the student.
Contextually Propagated Term Weights for Document Representation
Word embeddings predict a word from its neighbours by learning small, dense embedding vectors.
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.
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.
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.
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.
