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Found 9 papers, 3 papers with code
Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing
We evaluate and report the performance of popular EL systems on the ShadowLink benchmark.
Towards Reproducible Machine Learning Research in Natural Language Processing
While recent progress in the field of ML has been significant, the reproducibility of these cutting-edge results is often lacking, with many submissions lacking the necessary information in order to ensure subsequent reproducibility.
RecFusion: A Binomial Diffusion Process for 1D Data for Recommendation
In this paper we propose RecFusion, which comprise a set of diffusion models for recommendation.
When the Music Stops: Tip-of-the-Tongue Retrieval for Music
We present a study of Tip-of-the-tongue (ToT) retrieval for music, where a searcher is trying to find an existing music entity, but is unable to succeed as they cannot accurately recall important identifying information.
Market-Aware Models for Efficient Cross-Market Recommendation
We consider the cross-market recommendation (CMR) task, which involves recommendation in a low-resource target market using data from a richer, auxiliary source market.
Reproducibility as a Mechanism for Teaching Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence
In this work, we explain the setup for a technical, graduate-level course on Fairness, Accountability, Confidentiality, and Transparency in Artificial Intelligence (FACT-AI) at the University of Amsterdam, which teaches FACT-AI concepts through the lens of reproducibility.
Controllable Recommenders using Deep Generative Models and Disentanglement
We show that by updating the disentangled latent space based on user feedback, and by exploiting the generative nature of the recommender, controlled and personalized recommendations can be produced.
Robustness Evaluation of Entity Disambiguation Using Prior Probes:the Case of Entity Overshadowing
We evaluate and report the performance of popular EL systems on the ShadowLink benchmark.
Sinkhorn AutoEncoders
We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equivalent to the unconstrained min-min optimization of the p-Wasserstein distance between the encoder aggregated posterior and the prior in latent space, plus a reconstruction error.
