8 citations · 59 across the 37 of their papers we have counts for
39 papers
RAMP: Robust Adaptive Mixed-Precision Quantization for Edge CPU Vision Models
David Población-Criado, Dario Garcia-Gasulla, Eduardo Quinones
Deploying deep learning models on edge CPUs is bottlenecked by computational and memory constraints. Mixed-precision quantization promises to reduce inference latency while preserv…
Aloe-Vision: Robust Vision-Language Models for Healthcare
Jaume Guasch-Martí, Enrique Lopez-Cuena, Martín Suárez-Fernández +3
Large Vision-Language Models (LVLMs) specialized in healthcare are emerging as a promising research direction due to their potential impact in clinical and biomedical applications.…
Scalable Deep Learning Framework for Global High-Resolution Land Use Reconstruction
Amirpasha Mozaffari, Marina Castaño, Stefano Materia +7
Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and vari…
RuC: HDL-Agnostic Rule Completion Benchmark Generation
Arnau Ayguadé Domingo, Miquel Alberti-Binimelis, Cristian Gutierrez-Gomez +5
Large Language Models (LLMs) have rapidly improved in performance across code-related tasks, making their integration into Register Transfer Level (RTL) development increasingly at…
Language Models Can Explain Visual Features via Steering
Javier Ferrando, Enrique Lopez-Cuena, Pablo Agustin Martin-Torres +3
Sparse Autoencoders uncover thousands of features in vision models, yet explaining these features without requiring human intervention remains an open challenge. While previous wor…
NotSoTiny: A Large, Living Benchmark for RTL Code Generation
Razine Moundir Ghorab, Emanuele Parisi, Cristian Gutierrez +4
LLMs have shown early promise in generating RTL code, yet evaluating their capabilities in realistic setups remains a challenge. So far, RTL benchmarks have been limited in scale,…
TuRTLe: A Unified Evaluation of LLMs for RTL Generation
Dario Garcia-Gasulla, Gokcen Kestor, Emanuele Parisi +6
The rapid advancements in LLMs have driven the adoption of generative AI in various domains, including Electronic Design Automation (EDA). Unlike traditional software development,…
The Aloe Family Recipe for Open and Specialized Healthcare LLMs
Dario Garcia-Gasulla, Jordi Bayarri-Planas, Ashwin Kumar Gururajan +10
Purpose: With advancements in Large Language Models (LLMs) for healthcare, the need arises for competitive open-source models to protect the public interest. This work contributes…
Efficient Safety Retrofitting Against Jailbreaking for LLMs
Dario Garcia-Gasulla, Adrian Tormos, Anna Arias-Duart +4
Direct Preference Optimization (DPO) is an efficient alignment technique that steers LLMs towards preferable outputs by training on preference data, bypassing the need for explicit…
Automatic Evaluation of Healthcare LLMs Beyond Question-Answering
Anna Arias-Duart, Pablo Agustin Martin-Torres, Daniel Hinjos +7
Current Large Language Models (LLMs) benchmarks are often based on open-ended or close-ended QA evaluations, avoiding the requirement of human labor. Close-ended measurements evalu…
Data Augmentation with Diffusion Models for Colon Polyp Localization on the Low Data Regime: How much real data is enough?
Adrian Tormos, Blanca Llauradó, Fernando Núñez +3
The scarcity of data in medical domains hinders the performance of Deep Learning models. Data augmentation techniques can alleviate that problem, but they usually rely on functiona…
Pareto-Optimized Open-Source LLMs for Healthcare via Context Retrieval
Jordi Bayarri-Planas, Ashwin Kumar Gururajan, Dario Garcia-Gasulla
This study leverages optimized context retrieval to enhance open-source Large Language Models (LLMs) for cost-effective, high performance healthcare AI. We demonstrate that this ap…
Present and Future Generalization of Synthetic Image Detectors
Pablo Bernabeu-Perez, Enrique Lopez-Cuena, Dario Garcia-Gasulla
The continued release of increasingly realistic image generation models creates a demand for synthetic image detectors. To build effective detectors we must first understand how fa…
Padding Aware Neurons
Dario Garcia-Gasulla, Victor Gimenez-Abalos, Pablo Martin-Torres
Convolutional layers are a fundamental component of most image-related models. These layers often implement by default a static padding policy (\eg zero padding), to control the sc…
Exploring the Role of Explainability in AI-Assisted Embryo Selection
Lucia Urcelay, Daniel Hinjos, Pablo A. Martin-Torres +5
In Vitro Fertilization is among the most widespread treatments for infertility. One of its main challenges is the evaluation and selection of embryo for implantation, a process wit…
When & How to Transfer with Transfer Learning
Adrian Tormos, Dario Garcia-Gasulla, Victor Gimenez-Abalos +1
In deep learning, transfer learning (TL) has become the de facto approach when dealing with image related tasks. Visual features learnt for one task have been shown to be reusable…
Healthy Twitter discussions? Time will tell
Dmitry Gnatyshak, Dario Garcia-Gasulla, Sergio Alvarez-Napagao +2
Studying misinformation and how to deal with unhealthy behaviours within online discussions has recently become an important field of research within social studies. With the rapid…
Focus! Rating XAI Methods and Finding Biases
Anna Arias-Duart, Ferran Parés, Dario Garcia-Gasulla +1
AI explainability improves the transparency of models, making them more trustworthy. Such goals are motivated by the emergence of deep learning models, which are obscure by nature;…
Signs for Ethical AI: A Route Towards Transparency
Dario Garcia-Gasulla, Atia Cortés, Sergio Alvarez-Napagao +1
Today, Artificial Intelligence (AI) has a direct impact on the daily life of billions of people. Being applied to sectors like finance, health, security and advertisement, AI fuels…
Private Sources of Mobility Data Under COVID-19
Raquel Pérez Arnal, David Conesa, Sergio Alvarez-Napagao +4
The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility is at the epicenter of that change, as the greatest facilitator for the spread o…
The MAMe Dataset: On the relevance of High Resolution and Variable Shape image properties
Ferran Parés, Anna Arias-Duart, Dario Garcia-Gasulla +4
In the image classification task, the most common approach is to resize all images in a dataset to a unique shape, while reducing their precision to a size which facilitates experi…
DOME: Recommendations for supervised machine learning validation in biology
Ian Walsh, Dmytro Fishman, Dario Garcia-Gasulla +6
Modern biology frequently relies on machine learning to provide predictions and improve decision processes. There have been recent calls for more scrutiny on machine learning perfo…
Global Data Science Project for COVID-19
Toyotaro Suzumura, Dario Garcia-Gasulla, Sergio Alvarez Napagao +14
This paper aims at providing the summary of the Global Data Science Project (GDSC) for COVID-19. as on May 31 2020. COVID-19 has largely impacted on our societies through both dire…
The Impact of COVID-19 on Flight Networks
Toyotaro Suzumura, Hiroki Kanezashi, Mishal Dholakia +5
As COVID-19 transmissions spread worldwide, governments have announced and enforced travel restrictions to prevent further infections. Such restrictions have a direct effect on the…