activity
20242026
collaborators

8 papers

cs.CL2026

Sem-Detect: Semantic Level Detection of AI Generated Peer-Reviews

André V. Duarte, Brian Tufts, Aditya Oke +3

How can we distinguish whether a peer review was written by a human or generated by an AI model? We argue that, in this setting, authorship should not be attributed solely from the…

cs.CL2026

RECAP: Reproducing Copyrighted Data from LLMs Training with an Agentic Pipeline

André V. Duarte, Xuying li, Bin Zeng +3

If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself…

cs.CV2025

Deep Feedback Models

David Calhas, Arlindo L. Oliveira

Deep Feedback Models (DFMs) are a new class of stateful neural networks that combine bottom up input with high level representations over time. This feedback mechanism introduces d…

cs.CV2025

Are ECGs enough? Deep learning classification of pulmonary embolism using electrocardiograms

Joao D. S. Marques, Arlindo L. Oliveira

Pulmonary embolism is a leading cause of out of hospital cardiac arrest that requires fast diagnosis. While computed tomography pulmonary angiography is the standard diagnostic too…

cs.CV2025

Deep Recurrence for Dynamical Segmentation Models

David Calhas, Arlindo L. Oliveira

While biological vision systems rely heavily on feedback connections to iteratively refine perception, most artificial neural networks remain purely feedforward, processing input i…

cs.CV2025

DIS-CO: Discovering Copyrighted Content in VLMs Training Data

André V. Duarte, Xuandong Zhao, Arlindo L. Oliveira +1

How can we verify whether copyrighted content was used to train a large vision-language model (VLM) without direct access to its training data? Motivated by the hypothesis that a V…