2 citations · 4 across the 11 of their papers we have counts for
12 papers
Power quotients of plactic-like monoids
Antoine Abram, Florent Hivert, James D. Mitchell +2
In this paper we describe the quotients of several plactic-like monoids by the least congruences containing the relations for for every generator .…
DiSCO: Defending text-to-image generation through distribution-guided contrastive prompt optimization
Tong Zhang, Motasem Alfarra, Carlos Hinojosa +2
As text-to-image generative models advance, they raise critical safety concerns, particularly the generation of Not-Safe-For-Work (NSFW) content such as violence and nudity, furthe…
Distilling Safe LLM Systems via Soft Prompts for On Device Settings
Motasem Alfarra, Cristina Pinneri, Dana Kianfar +2
Deploying safe large language models (LLMs) on resource-constrained edge devices presents a critical challenge: while dual-model systems combining LLMs with guard models provide ef…
Forget Less, Retain More: A Lightweight Regularizer for Rehearsal-Based Continual Learning
Lama Alssum, Hasan Abed Al Kader Hammoud, Motasem Alfarra +2
Deep neural networks suffer from catastrophic forgetting, where performance on previous tasks degrades after training on a new task. This issue arises due to the model's tendency t…
Masks Can Be Distracting: On Context Comprehension in Diffusion Language Models
Julianna Piskorz, Cristina Pinneri, Alvaro Correia +3
Masked Diffusion Language Models (MDLMs) have recently emerged as a promising alternative to Autoregressive Language Models (ARLMs), leveraging a denoising objective that, in princ…
ADVMEM: Adversarial Memory Initialization for Realistic Test-Time Adaptation via Tracklet-Based Benchmarking
Shyma Alhuwaider, Motasem Alfarra, Juan C. Perez +2
We introduce a novel tracklet-based dataset for benchmarking test-time adaptation (TTA) methods. The aim of this dataset is to mimic the intricate challenges encountered in real-wo…
Towards Faster and More Compact Foundation Models for Molecular Property Prediction
Yasir Ghunaim, Andrés Villa, Gergo Ignacz +3
Advancements in machine learning for molecular property prediction have improved accuracy but at the expense of higher computational cost and longer training times. Recently, the J…
EAGLE: Enhanced Visual Grounding Minimizes Hallucinations in Instructional Multimodal Models
Andrés Villa, Juan León Alcázar, Motasem Alfarra +3
Large language models and vision transformers have demonstrated impressive zero-shot capabilities, enabling significant transferability in downstream tasks. The fusion of these mod…
Deep Learning at the Intersection: Certified Robustness as a Tool for 3D Vision
Gabriel Pérez S, Juan C. Pérez, Motasem Alfarra +4
This paper presents preliminary work on a novel connection between certified robustness in machine learning and the modeling of 3D objects. We highlight an intriguing link between…
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical Imaging
Kumail Alhamoud, Yasir Ghunaim, Motasem Alfarra +5
For medical imaging AI models to be clinically impactful, they must generalize. However, this goal is hindered by (i) diverse types of distribution shifts, such as temporal, demogr…
Towards Interpretable Deep Local Learning with Successive Gradient Reconciliation
Yibo Yang, Xiaojie Li, Motasem Alfarra +4
Relieving the reliance of neural network training on a global back-propagation (BP) has emerged as a notable research topic due to the biological implausibility and huge memory con…
Test-Time Adaptation for Combating Missing Modalities in Egocentric Videos
Merey Ramazanova, Alejandro Pardo, Bernard Ghanem +1
Understanding videos that contain multiple modalities is crucial, especially in egocentric videos, where combining various sensory inputs significantly improves tasks like action r…