3 citations · 4 across the 6 of their papers we have counts for
7 papers
Testing the Planning Claims of LLM-Based Agents against Formal Semantics
Oriol Miro-Lopez-Feliu, Oscar Miró López-Feliu, Victor Gimenez-Abalos +3
LLM-based agents are increasingly credited with the competences of practical reasoning: planning, monitoring, and correcting their own actions. Their success is attributed to the p…
Explaining Autonomous Vehicles with Intention-aware Policy Graphs
Sara Montese, Victor Gimenez-Abalos, Atia Cortés +2
The potential to improve road safety, reduce human driving error, and promote environmental sustainability have enabled the field of autonomous driving to progress rapidly over rec…
Intention-aware policy graphs: answering what, how, and why in opaque agents
Victor Gimenez-Abalos, Sergio Alvarez-Napagao, Adrian Tormos +2
Agents are a special kind of AI-based software in that they interact in complex environments and have increased potential for emergent behaviour. Explaining such emergent behaviour…
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…
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…
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;…
Feature discriminativity estimation in CNNs for transfer learning
Victor Gimenez-Abalos, Armand Vilalta, Dario Garcia-Gasulla +2
The purpose of feature extraction on convolutional neural networks is to reuse deep representations learnt for a pre-trained model to solve a new, potentially unrelated problem. Ho…