EMERGE’s findings will be communicated at scientific conferences and published in open-access journals. Find below the current list of publications.
Access all publication on the project's Zenodo community clicking here.
Quasi-Metacognitive Machines: Why We Don’t Need Morally Trustworthy AI and Communicating Reliability is Enough
This paper argues that developing morally trustworthy AI is not only unethical, as it promotes trust in an entity that cannot be trustworthy, but it is also unnecessary for optimal calibration.
Dorsch, J., Deroy, O. Philos. Technol. 37, 62 (2024). doi: 10.1007/s13347-024-00752-w.
Random Orthogonal Additive Filters: A Solution to the Vanishing/Exploding Gradient of Deep Neural Networks
In this paper, a new idea that permits to solve the V/E gradient issue of deep learning models trained via stochastic gradient descent (SGD) methods is proposed.
A. Ceni, in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2025.3538924.
Random Oscillators Network for Time Series Processing
This paper introduces the Random Oscillators Network (RON), a physically-inspired recurrent model derived from a network of heterogeneous oscillators.
Andrea Ceni, Andrea Cossu, Maximilian W Stölzle, Jingyue Liu, Cosimo Della Santina, Davide Bacciu, Claudio Gallicchio Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:4807-4815, 2024.
Randomly Coupled Oscillators for Time Series Processing
This paper investigates a physically-inspired recurrent neural network derived from a continuous-time ODE modelling a network of coupled oscillators.
Andrea Ceni, Andrea Cossu, Jingyue Liu, Maximilian Stölzle, Cosimo Della Santina, Claudio Gallicchio and Davide Bacciu, ICML Workshop on New Frontiers in Learning, Control, and Dynamical Systems, 2023.
Replay-free Online Continual Learning with Self-Supervised MultiPatches
This work proposes Continual MultiPatches (CMP), an effective plug-in for existing OCL self-supervised learning strategies that avoids the use of replay samples.
Cignoni, Giacomo & Cossu, Andrea & Gomez-Villa, Alexandra & Weijer, Joost & Carta, Antonio. (2025). 81-86. 10.14428/esann/2025.ES2025-180.
Reservoir Memory Networks
This paper introduces Reservoir Memory Networks (RMNs), a novel class of Reservoir Computing (RC) models that integrate a linear memory cell with a non-linear reservoir to enhance long-term information retention.
Claudio Gallicchio and Andrea Ceni, ESANN 2024 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium) and online event, 9-11 October 2024.
Residual Echo State Networks: Residual recurrent neural networks with stable dynamics and fast learning
This paper studies the architectural bias of residual connections in the context of recurrent neural networks (RNNs), specifically in the temporal dimension.
Andrea Ceni, Claudio Gallicchio, Neurocomputing, 597, 2024, 127966, doi: 10.1016/j.neucom.2024.127966.
Residual Reservoir Computing Neural Networks for Time-series Classification
In this paper, the authors augment standard Echo State Networks (ESNs) with linear reservoir-skip connections modulated by an untrained orthogonal weight matrix.
Andrea Ceni and Claudio Gallicchio, in ESANN 2023 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Bruges (Belgium), doi: 10.14428/esann/2023.ES2023-112
Residual Reservoir Memory Networks
This paper introduces a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs).
M. Pinna, A. Ceni and C. Gallicchio, 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-7, doi: 10.1109/IJCNN64981.2025.11227859.
Return of ChebNet: Understanding and Improving an Overlooked GNN on Long Range Tasks
This work revisits ChebNet, one of the earliest spectral GNNs, to shed light on its ability to model distant node interactions.
Ali Hariri et al. 39th Conference on Neural Information Processing Systems (NeurIPS 2025).
Safe Control of Soft Robots: Bridging Physics and Learned Models
The contributions presented in this abstract equip soft robots with the motor intelligence they need to function effectively in human-centric environments.
Stölzle, M. Abstract from 8th IEEE-RAS International Conference on Soft Robotics (2025), Lausanne, Switzerland.
Shared Awareness Across Domain-Specific Artificial Intelligence: An Alternative to Domain-General Intelligence and Artificial Consciousness
This paper argues that it will not require machines to be conscious and that simpler ways of sharing awareness are sufficient.
Deroy, O., Bacciu, D., Bahrami, B., Della Santina, C. and Hauert, S. (2024), Adv. Intell. Syst., 6: 2300740. doi: 10.1002/aisy.202300740.
Singular-Perturbation Control of a Tendon-Driven Soft Robot: Theory and Experiments
This work analyzes the influence of the actuation dynamics in tendon-driven continuum soft robots performing trajectory-tracking tasks.
L. N. Ribeiro, P. Borja, C. D. Santina and B. Deutschmann, in IEEE Transactions on Control Systems Technology, doi: 10.1109/TCST.2025.3546564.
Smell Driven Navigation for Soft Robotic Arms: Artificial Nose and Control
This work proposes an artificial nose on a soft robotic arm that ensures separate smell concentration readings.
F. Piqué, F. Stella, J. Hughes, E. Falotico and C. D. Santina, 2023 IEEE International Conference on Soft Robotics (RoboSoft), Singapore, Singapore, 2023, pp. 1-7, doi: 10.1109/RoboSoft55895.2023.10122116.
Social Judgment Interferes With Action Generation During Instrumental Learning
This work investigated how social judgment modulates behavioral and neural mechanisms underlying value-based decision-making.
Zahra Rezazadeh, Bahador Bahrami, and Simone Schütz-Bosbach, Journal of Psychophysiology 2025 39:2, 74-86. DOI: 10.1027/0269-8803/a000348
Soft Robot Shape Estimation With IMUs Leveraging PCC Kinematics for Drift Filtering
This letter proposes a method to eliminate this limitation by leveraging the Piecewise Constant Curvature model assumption.
F. Stella, C. D. Santina and J. Hughes, in IEEE Robotics and Automation Letters, vol. 9, no. 2, pp. 1945-1952, Feb. 2024, doi: 10.1109/LRA.2023.3339063.
SONAR: Long-Range Graph Propagation Through Information Waves
This work introduces SONAR, a novel GNN architecture inspired by the dynamics of wave propagation in continuous media.
Alessandro Trenta and Alessio Gravina and Davide Bacciu, in proceedings of The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025.
Sparse assemblies of recurrent neural networks with stability guarantees
This work introduces AdaDiag, a framework for constructing sparse assemblies of recurrent neural networks (RNNs) with formal stability guarantees.
Andrea Ceni, Valerio De Caro, Davide Bacciu, Claudio Gallicchio, Neurocomputing, v. 675, 2026, p. 132952, DOI: 10.1016/j.neucom.2026.132952.
