no code implementations • ACL 2022 • Ana Lucic, Maurits Bleeker, Samarth Bhargav, Jessica Forde, Koustuv Sinha, Jesse Dodge, Sasha Luccioni, Robert Stojnic
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.
1 code implementation • 27 Feb 2024 • Maurits Bleeker, Mariya Hendriksen, Andrew Yates, Maarten de Rijke
Hence, contrastive losses are not sufficient to learn task-optimal representations, i. e., representations that contain all task-relevant information shared between the image and associated captions.
no code implementations • 18 Apr 2023 • Maurits Bleeker, Pawel Swietojanski, Stefan Braun, Xiaodan Zhuang
By including approximate nearest neighbour phrases (ANN-P) in the context list, we encourage the learned representation to disambiguate between similar, but not identical, biasing phrases.
1 code implementation • 9 May 2022 • Michael Neely, Stefan F. Schouten, Maurits Bleeker, Ana Lucic
The validity of "attention as explanation" has so far been evaluated by computing the rank correlation between attention-based explanations and existing feature attribution explanations using LSTM-based models.
1 code implementation • 28 Apr 2022 • Maurits Bleeker, Andrew Yates, Maarten de Rijke
We add an additional decoder to the contrastive ICR framework, to reconstruct the input caption in a latent space of a general-purpose sentence encoder, which prevents the image and caption encoder from suppressing predictive features.
1 code implementation • 14 Feb 2022 • Maurits Bleeker, Maarten de Rijke
Recent progress in metric learning has given rise to new loss functions that outperform the triplet loss on tasks such as image retrieval and representation learning.
1 code implementation • 21 Dec 2021 • Mariya Hendriksen, Maurits Bleeker, Svitlana Vakulenko, Nanne van Noord, Ernst Kuiper, Maarten de Rijke
One aspect of this data is a category tree that is being used in search and recommendation.
no code implementations • 1 Nov 2021 • Ana Lucic, Maurits Bleeker, Sami Jullien, Samarth Bhargav, Maarten de Rijke
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.
3 code implementations • 11 May 2020 • David Stap, Maurits Bleeker, Sarah Ibrahimi, Maartje ter Hoeve
This can be done by conditioning the model on additional information.
1 code implementation • 8 Dec 2019 • Maurits Bleeker, Maarten de Rijke
We introduce the bidirectional Scene Text Transformer (Bi-STET), a novel bidirectional STR method with a single decoder for bidirectional text decoding.