Publications

Advancements in Affective and Behavior Analysis: The 8th ABAW Workshop and Competition
Dimitrios Kollias
Panagiotis Tzirakis
Alan Cowen
Stefanos Zafeiriou
Irene Kotsia
Eric Granger
Marco Pedersoli
Simon Bacon
Alice Baird
Chris Gagne
Chunchang Shao
Guanyu Hu
Soufiane Belharbi
Muhammad Haseeb Aslam
2024-12-31
CVPR Workshops (publié)
doi.org
Advocacy for Children With Surgical Diseases in Nigeria: National Policy Status, Gaps, and Solutions
Justina O. Seyi-Olajide
Ayla Gerk
Elena Guadagno
Adesoji Ademuyiwa
Emmanuel A. Ameh
Dan Poenaru
2024-12-31
Journal of Pediatric Surgery (publié)
doi.org
AFRIDOC-MT: Document-level MT Corpus for African Languages
Jesujoba Oluwadara Alabi
Israel Abebe Azime
Miaoran Zhang
Cristina España-Bonet
Rachel Bawden
Dawei Zhu
David Ifeoluwa Adelani
Clement Odoje
Idris Akinade
Iffat Maab
Davis David
Shamsuddeen Hassan Muhammad
Neo Putini
David O. Ademuyiwa
Andrew Caines
Dietrich Klakow
This paper introduces AFRIDOC-MT, a document-level multi-parallel translation dataset covering English and five African languages: Amharic, … (voir plus)Hausa, Swahili, Yor\`ub\'a, and Zulu. The dataset comprises 334 health and 271 information technology news documents, all human-translated from English to these languages. We conduct document-level translation benchmark experiments by evaluating neural machine translation (NMT) models and large language models (LLMs) for translations between English and these languages, at both the sentence and pseudo-document levels. These outputs are realigned to form complete documents for evaluation. Our results indicate that NLLB-200 achieved the best average performance among the standard NMT models, while GPT-4o outperformed general-purpose LLMs. Fine-tuning selected models led to substantial performance gains, but models trained on sentences struggled to generalize effectively to longer documents. Furthermore, our analysis reveals that some LLMs exhibit issues such as under-generation, repetition of words or phrases, and off-target translations, especially for African languages.
2024-12-31
EMNLP (publié)
doi.org
arxiv.org
Anatomically-Focused Patches for Lightweight and Explainable Knee OA Grading
Tien-en Chang
Hervé Lombaert
2024-12-31
ShapeMI@MICCAI (publié)
doi.org
Anticancer Monotherapy and Polytherapy Drug Response Prediction Using Deep Learning: Guidelines and Best Practices
Amin Emad
David Earl Hostallero
2024-12-31
Methods in Molecular Biology (inconnu)
doi.org
Anti-patterns and Code Smells for Multi-language Systems
Mouna Abidi
Manel Grichi
Foutse Khomh
Yann‐Gaël Guéhéneuc
2024-12-31
Transactions on Pattern Languages of Programming (publié)
doi.org
Attention as a Hypernetwork
Simon Schug
Seijin Kobayashi
Yassir Akram
João Sacramento
Razvan Pascanu
Transformers can under some circumstances generalize to novel problem instances whose constituent parts might have been encountered during t… (voir plus)raining, but whose compositions have not. What mechanisms underlie this ability for compositional generalization? By reformulating multi-head attention as a hypernetwork, we reveal that a composable, low-dimensional latent code specifies key-query specific operations. We find empirically that this latent code is predictive of the subtasks the network performs on unseen task compositions, revealing that latent codes acquired during training are reused to solve unseen problem instances. To further examine the hypothesis that the intrinsic hypernetwork of multi-head attention supports compositional generalization, we ablate whether making the hypernetwork-generated linear value network nonlinear strengthens compositionality. We find that this modification improves compositional generalization on abstract reasoning tasks. In particular, we introduce a symbolic version of the Raven's Progressive Matrices human intelligence test, which gives us precise control over the problem compositions encountered during training and evaluation. We demonstrate on this task how scaling model size and data enables compositional generalization in transformers and gives rise to a functionally structured latent space.
2024-12-31
ICLR (publié)
doi.org
arxiv.org
Audio Prototypical Network For Controllable Music Recommendation
Fırat Öncel
Emiliano Penaloza
Haolun Wu
Shubham Gupta
Mirco Ravanelli
Laurent Charlin
Cem Subakan
Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While thes… (voir plus)e models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system's modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation. In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.
2024-12-31
MLSP (publié)
doi.org
openreview.net
AURA: A Multi-modal Medical Agent for Understanding, Reasoning and Annotation
Nima Fathi
Amar Kumar
Tal Arbel
2024-12-31
Agentic AI/CREATE/Clinical MLLMs@MICCAI (publié)
doi.org
Automated UML Visualization of Software Ecosystems: Tracking Versions, Dependencies, and Security Updates
Vanessa Kan
M. P. Lnu
Solomon Berhe
C. El Kari
Marc Maynard
Foutse Khomh
2024-12-31
ANT/EDI40 (publié)
doi.org
Balancing Profit and Fairness in Risk-Based Pricing Markets
Jesse Thibodeau
Hadi Nekoei
Afaf Taïk
Janarthanan Rajendran
Golnoosh Farnadi
Dynamic, risk-based pricing can systematically exclude vulnerable consumer groups from essential resources such as health insurance and cons… (voir plus)umer credit. We show that a regulator can realign private incentives with social objectives through a learned, interpretable tax schedule. First, we provide a formal proposition that bounding each firm's \emph{local} demographic gap implicitly bounds the \emph{global} opt-out disparity, motivating firm-level penalties. Building on this insight we introduce \texttt{MarketSim} -- an open-source, scalable simulator of heterogeneous consumers and profit-maximizing firms -- and train a reinforcement learning (RL) social planner (SP) that selects a bracketed fairness-tax while remaining close to a simple linear prior via an
2024-12-31
arXiv (prépublication)
doi.org
arxiv.org
Beyond Model Collapse: Scaling Up with Synthesized Data Requires Verification
Yunzhen Feng
Elvis Dohmatob
Pu Yang
Francois Charton
Julia Kempe
Large Language Models (LLM) are increasingly trained on data generated by other LLM, either because generated text and images become part of… (voir plus) the pre-training corpus, or because synthetized data is used as a replacement for expensive human-annotation. This raises concerns about \emph{model collapse}, a drop in model performance when their training sets include generated data. Considering that it is easier for both humans and machines to tell between good and bad examples than to generate high-quality samples, we investigate the use of verification on synthesized data to prevent model collapse. We provide a theoretical characterization using Gaussian mixtures, linear classifiers, and linear verifiers to derive conditions with measurable proxies to assess whether the verifier can effectively select synthesized data that leads to optimal performance. We experiment with two practical tasks -- computing matrix eigenvalues with transformers and news summarization with LLMs -- which both exhibit model collapse when trained on generated data, and show that verifiers, even imperfect ones, can indeed be harnessed to prevent model collapse and that our proposed proxy measure strongly correlates with performance.
2024-12-31
ICLR (publié)
doi.org
openreview.net