Here you can find a consolidated (a.k.a. slowly updated) list of my publications. A frequently updated (and possibly noisy) list of works is available on my Google Scholar profile.
Please find below a short list of highlight publications for my recent activity.
Valenti, Andrea; Bacciu, Davide Modular Representations for Weak Disentanglement Conference Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022), 2022. Valenti, Andrea; Bacciu, Davide Leveraging Relational Information for Learning Weakly Disentangled Representations Conference Proceedings of the 2022 IEEE World Congress on Computational Intelligence, IEEE, 2022. Numeroso, Danilo; Bacciu, Davide; Veličković, Petar Learning heuristics for A* Workshop ICRL 2022 Workshop on Anchoring Machine Learning in Classical Algorithmic Theory (GroundedML 2022), 2022. Collodi, Lorenzo; Bacciu, Davide; Bianchi, Matteo; Averta, Giuseppe Learning with few examples the semantic description of novel human-inspired grasp strategies from RGB data Journal Article In: IEEE Robotics and Automation Letters, pp. 2573 - 2580, 2022.
@conference{Valenti2022c,
title = {Modular Representations for Weak Disentanglement},
author = {Andrea Valenti and Davide Bacciu},
editor = {Michel Verleysen},
url = {https://arxiv.org/pdf/2209.05336.pdf},
year = {2022},
date = {2022-10-05},
urldate = {2022-10-05},
booktitle = {Proceedings of the 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN 2022)},
abstract = {The recently introduced weakly disentangled representations proposed to relax some constraints of the previous definitions of disentanglement, in exchange for more flexibility. However, at the moment, weak disentanglement can only be achieved by increasing the amount of supervision as the number of factors of variations of the data increase. In this paper, we introduce modular representations for weak disentanglement, a novel method that allows to keep the amount of supervised information constant with respect the number of generative factors. The experiments shows that models using modular representations can increase their performance with respect to previous work without the need of additional supervision.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@conference{Valenti2022,
title = { Leveraging Relational Information for Learning Weakly Disentangled Representations },
author = {Andrea Valenti and Davide Bacciu
},
url = {https://arxiv.org/abs/2205.10056, Arxiv},
year = {2022},
date = {2022-07-18},
urldate = {2022-07-18},
booktitle = {Proceedings of the 2022 IEEE World Congress on Computational Intelligence},
publisher = {IEEE},
abstract = {Disentanglement is a difficult property to enforce in neural representations. This might be due, in part, to a formalization of the disentanglement problem that focuses too heavily on separating relevant factors of variation of the data in single isolated dimensions of the neural representation. We argue that such a definition might be too restrictive and not necessarily beneficial in terms of downstream tasks. In this work, we present an alternative view over learning (weakly) disentangled representations, which leverages concepts from relational learning. We identify the regions of the latent space that correspond to specific instances of generative factors, and we learn the relationships among these regions in order to perform controlled changes to the latent codes. We also introduce a compound generative model that implements such a weak disentanglement approach. Our experiments shows that the learned representations can separate the relevant factors of variation in the data, while preserving the information needed for effectively generating high quality data samples.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@workshop{Numeroso2022,
title = {Learning heuristics for A*},
author = { Danilo Numeroso and Davide Bacciu and Petar Veličković},
year = {2022},
date = {2022-04-29},
urldate = {2022-04-29},
booktitle = {ICRL 2022 Workshop on Anchoring Machine Learning in Classical Algorithmic Theory (GroundedML 2022)},
abstract = {Path finding in graphs is one of the most studied classes of problems in computer science. In this context, search algorithms are often extended with heuristics for a more efficient search of target nodes. In this work we combine recent advancements in Neural Algorithmic Reasoning to learn efficient heuristic functions for path finding problems on graphs. At training time, we exploit multi-task learning to learn jointly the Dijkstra's algorithm and a {it consistent} heuristic function for the A* search algorithm. At inference time, we plug our learnt heuristics into the A* algorithm. Results show that running A* over the learnt heuristics value can greatly speed up target node searching compared to Dijkstra, while still finding minimal-cost paths.
},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{Collodi2022,
title = {Learning with few examples the semantic description of novel human-inspired grasp strategies from RGB data},
author = { Lorenzo Collodi and Davide Bacciu and Matteo Bianchi and Giuseppe Averta},
url = {https://www.researchgate.net/profile/Giuseppe-Averta/publication/358006552_Learning_With_Few_Examples_the_Semantic_Description_of_Novel_Human-Inspired_Grasp_Strategies_From_RGB_Data/links/61eae01e8d338833e3857251/Learning-With-Few-Examples-the-Semantic-Description-of-Novel-Human-Inspired-Grasp-Strategies-From-RGB-Data.pdf, Open Version},
doi = {https://doi.org/10.1109/LRA.2022.3144520},
year = {2022},
date = {2022-04-04},
urldate = {2022-04-04},
journal = { IEEE Robotics and Automation Letters},
pages = { 2573 - 2580},
publisher = {IEEE},
abstract = {Data-driven approaches and human inspiration are fundamental to endow robotic manipulators with advanced autonomous grasping capabilities. However, to capitalize upon these two pillars, several aspects need to be considered, which include the number of human examples used for training; the need for having in advance all the required information for classification (hardly feasible in unstructured environments); the trade-off between the task performance and the processing cost. In this paper, we propose a RGB-based pipeline that can identify the object to be grasped and guide the actual execution of the grasping primitive selected through a combination of Convolutional and Gated Graph Neural Networks. We consider a set of human-inspired grasp strategies, which are afforded by the geometrical properties of the objects and identified from a human grasping taxonomy, and propose to learn new grasping skills with only a few examples. We test our framework with a manipulator endowed with an under-actuated soft robotic hand. Even though we use only 2D information to minimize the footprint of the network, we achieve 90% of successful identifications of the most appropriate human-inspired grasping strategy over ten different classes, of which three were few-shot learned, outperforming an ideal model trained with all the classes, in sample-scarce conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}