Fast track thesis proposals

Tirocini e tesi “fast track” sono stati approvati in via sperimentale dal Consiglio di Corso di Studio il 7 Gennaio 2019. Una descrizione di come funzioni questa modalità è disponibile online; si noti che potrebbe essere aggiornata da successivi atti del Consiglio di Corso di Studio: per maggiori dettagli contattare i tutori o il coordinatore del corso di studio.

Su come si prepari un riassunto di un articolo scientifico ci sono molte indicazioni e vari punti di vista: uno che trovo sintetico e sensato è questo. Consiglio comunque di contattarmi per fare una chiacchierata nel caso ci sia interesse a seguire questa modalità.

Currently available papers # ↰

• Kofinas, Miltiadis, et al. “Graph Neural Networks for Learning Equivariant Representations of Neural Networks.” arXiv preprint arXiv:2403.12143 (2024).
• Maddigan, Paula, Andrew Lensen, and Bing Xue. “Explaining Genetic Programming Trees using Large Language Models.” arXiv preprint arXiv:2403.03397 (2024).
• Chang, Yi-Hsiang, et al. “Reusability and Transferability of Macro Actions for Reinforcement Learning.” ACM Transactions on Evolutionary Learning and Optimization 2.1 (2022): 1-16.
• Zahedi, Keyan, and Nihat Ay. “Quantifying morphological computation.” Entropy 15.5 (2013): 1887-1915.
• Paul, Chandana. “Morphological computation: A basis for the analysis of morphology and control requirements.” Robotics and Autonomous Systems 54.8 (2006): 619-630.
• Füchslin, Rudolf M., et al. “Morphological computation and morphological control: steps toward a formal theory and applications.” Artificial life 19.1 (2013): 9-34.
• Soltanian, Khabat, Ali Ebnenasir, and Mohsen Afsharchi. “Modular Grammatical Evolution for the Generation of Artificial Neural Networks.” Evolutionary Computation (2021): 1-36.
• Ross, Alexis, et al. “Tailor: Generating and Perturbing Text with Semantic Controls.” arXiv preprint arXiv:2107.07150 (2021).
• Chollet, François. “On the measure of intelligence.” arXiv preprint arXiv:1911.01547 (2019)
• Fu, Justin, et al. “From Language to Goals: Inverse Reinforcement Learning for Vision-Based Instruction Following.” arXiv preprint arXiv:1902.07742 (2019)
• Yu, Wenhao, et al. “Sim-to-Real Transfer for Biped Locomotion.” arXiv preprint arXiv:1903.01390 (2019)
• Chan, Bert Wang-Chak. “Lenia and Expanded Universe.” arXiv preprint arXiv:2005.03742 (2020)
• Olesen, Thor VAN, et al. “Evolutionary Planning in Latent Space.” arXiv preprint arXiv:2011.11293 (2020).
• Cao, Yding, et al. “Visualizing Collective Idea Generation and Innovation Processes in Social Networks.” arXiv preprint arXiv:2110.09893 (2021).
• Kim, Jason Z., et al. “A Neural Programming Language for the Reservoir Computer”, arXiv preprint arXiv:2203.05032 (2022).
• Wang, Rouyao, et al. “ScienceWorld: Is your Agent Smarter than a 5th Grader?”, preprint (2022).
• Kamyar, Sayed, et al. “Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning.” arXiv preprint arXiv:2203.13733 (2022).
• Ha, D., and Tang, Y. (2022). “Collective intelligence for deep learning: A survey of recent developments.” Collective Intelligence, 1(1).
• Hinton, G. (2022). “The Forward-Forward Algorithm: Some Preliminary Investigations”
• Chen, Xiangning, et al. “Symbolic Discovery of Optimization Algorithms.” arXiv preprint arXiv:2302.06675 (2023)
• Oktay, Deniz, et al. “Neuromechanical Autoencoders: Learning to Couple Elastic and Neural Network Nonlinearity.” arXiv preprint arXiv:2302.00032 (2023)
• Bhoopchand, Avishkar, et al. “Learning few-shot imitation as cultural transmission.” Nature Communications 14.1 (2023): 7536.
• Romera-Paredes, B., Barekatain, M., Novikov, A. et al. “Mathematical discoveries from program search with large language models”. Nature (2023).
• Hu, Yuhang, Jiong Lin, and Hod Lipson. “Teaching Robots to Build Simulations of Themselves.” arXiv preprint arXiv:2311.12151 (2023).
• Hamon, Gautier, et al. “Discovering Sensorimotor Agency in Cellular Automata using Diversity Search.” arXiv preprint arXiv:2402.10236 (2024).

Ongoing theses # ↰

• Wang, Zhiquan, et al. “Evolution-based Shape and Behavior Co-design of Virtual Agents.” IEEE Transactions on Visualization and Computer Graphics (2024).
• Ben Zion, Matan Yah, et al. “Morphological computation and decentralized learning in a swarm of sterically interacting robots.” Science Robotics 8.75 (2023): eabo6140.

Done # ↰

• Vorobyov, Kostyantyn, et al. “Synthesis of Java Deserialisation Filters from Examples.” 2022 IEEE 46th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2022. (student: Alessandro Querenghi)
• Ferigo, Andrea, and Giovanni Iacca. “Self-building Neural Networks.” arXiv preprint arXiv:2304.01086 (2023). (student: Chiara Botter)
• Kudithipudi, Dhireesha, et al. “Biological underpinnings for lifelong learning machines”, Nature Machine Intelligence (2022). (student: Alice Lazzaretto)
• Pratt, Sarah, Luca Weihs, and Ali Farhadi. “The Introspective Agent: Interdependence of Strategy, Physiology, and Sensing for Embodied Agents.” arXiv preprint arXiv:2201.00411 (2022). (student: Stefano Casagrande)
• Bongard, Joshua, and Michael Levin. “Living Things Are Not (20th Century) Machines: Updating Mechanism Metaphors in Light of the Modern Science of Machine Behavior.” Frontiers in Ecology and Evolution 9 (2021): 147. (student: Simone Cappiello)
• Spielberg, Andrew, et al. “Co-Learning of Task and Sensor Placement for Soft Robotics.” IEEE Robotics and Automation Letters 6.2 (2021): 1208-1215. (student: Riccardo Weis)
• Bove, David “SoK: The Evolution of Trusted UI on Mobile”, ASIA CCS 2022. (student: Simone Cossaro)
• Suarez, Joseph, et al. “Neural MMO: A Massively Multiagent Game Environment for Training and Evaluating Intelligent Agents.” arXiv preprint arXiv:1903.00784 (2019) (student: Lorenzo Giaccari)
• Sayama, Hiroki. “Extreme Ideas Emerging from Social Conformity and Homophily: An Adaptive Social Network Model.” Artificial Life Conference Proceedings. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2020 (student: Letizia Ferrari)
• Eiben, A. E. “Real-world robot evolution: why would it (not) work?.” Frontiers in Robotics and AI: 243. (student: Daniel Costantino)
• Pontes-Filho, Nichele. “A Conceptual Bio-Inspired Framework for the Evolution of Artificial General Intelligence.” arXiv preprint arXiv:1903.10410 (2020) (student: Nicoletta Giurgevich)
• Matthews, David, and Josh Bongard. “Crowd grounding: finding semantic and behavioral alignment through human robot interaction.” Artificial Life Conference Proceedings. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2020 (student: Ivan Antonutti)
• Paton, Norman W. “Automating Data Preparation: Can We? Should We? Must We?” (student: Samuele Bertollo)
• McDonald, Andrew WE, Sean Grimes, and David E. Breen. “Ortus: An emotion–driven approach to (artificial) biological intelligence.” Artificial Life Conference Proceedings 14. One Rogers Street, Cambridge, MA 02142-1209 USA journals-info@ mit. edu: MIT Press, 2017 (student: Martina Silvestrini)
• Kaiser, Tanja Katharina, and Heiko Hamann. “Engineered Self-Organization for Resilient Robot Self-Assembly with Minimal Surprise.” arXiv preprint arXiv:1902.05485 (2019) (student: Sergio Milo)
• Hallawa, Ahmed, et al. “EVO-RL: Evolutionary-Driven Reinforcement Learning.” arXiv preprint arXiv:2007.04725 (2020) (student: Marco Giberna)