A Unified Optimization View on Generalized Matching Pursuit and Frank-Wolfe. - Francesco Locatello; Rajiv Khanna; Michael Tschannen; Martin Jaggi (AISTATS, 2017)

Greedy Algorithms for Cone Constrained Optimization with Convergence Guarantees. - Francesco Locatello; Michael Tschannen; Gunnar Rätsch; Martin Jaggi (NIPS, 2017)

Boosting Variational Inference: an Optimization Perspective. - Francesco Locatello; Rajiv Khanna; Joydeep Ghosh; Gunnar Rätsch (AISTATS, 2018)

Clustering Meets Implicit Generative Models. - Francesco Locatello; Damien Vincent; Ilya O. Tolstikhin; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf (ICLR (Workshop), 2018)

On Matching Pursuit and Coordinate Descent. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (ICML, 2018)

A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming. - Alp Yurtsever; Olivier Fercoq; Francesco Locatello; Volkan Cevher (ICML, 2018)

Boosting Black Box Variational Inference. - Francesco Locatello; Gideon Dresdner; Rajiv Khanna; Isabel Valera; Gunnar Rätsch (NeurIPS, 2018)

Revisiting First-Order Convex Optimization Over Linear Spaces. - Francesco Locatello; Anant Raj; Sai Praneeth Karimireddy; Gunnar Rätsch; Bernhard Schölkopf; Sebastian U. Stich; Martin Jaggi (CoRR, 2018)

Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (CoRR, 2018)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (CoRR, 2018)

SOM-VAE: Interpretable Discrete Representation Learning on Time Series. - Vincent Fortuin; Matthias Hüser; Francesco Locatello; Heiko Strathmann; Gunnar Rätsch (ICLR (Poster), 2019)

Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (ICML, 2019)

On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset. - Muhammad Waleed Gondal; Manuel Wuthrich; Djordje Miladinovic; Francesco Locatello; Martin Breidt; Valentin Volchkov; Joel Akpo; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (NeurIPS, 2019)

On the Fairness of Disentangled Representations. - Francesco Locatello; Gabriele Abbati; Thomas Rainforth; Stefan Bauer; Bernhard Schölkopf; Olivier Bachem (NeurIPS, 2019)

Stochastic Frank-Wolfe for Composite Convex Minimization. - Francesco Locatello; Alp Yurtsever; Olivier Fercoq; Volkan Cevher (NeurIPS, 2019)

Are Disentangled Representations Helpful for Abstract Visual Reasoning? - Sjoerd van Steenkiste; Francesco Locatello; Jürgen Schmidhuber; Olivier Bachem (NeurIPS, 2019)

The Incomplete Rosetta Stone problem: Identifiability results for Multi-view Nonlinear ICA. - Luigi Gresele; Paul K. Rubenstein; Arash Mehrjou; Francesco Locatello; Bernhard Schölkopf (UAI, 2019)

Stochastic Conditional Gradient Method for Composite Convex Minimization. - Francesco Locatello; Alp Yurtsever; Olivier Fercoq; Volkan Cevher (CoRR, 2019)

Disentangling Factors of Variation Using Few Labels. - Francesco Locatello; Michael Tschannen; Stefan Bauer; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem (CoRR, 2019)

A Commentary on the Unsupervised Learning of Disentangled Representations. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (AAAI, 2020)

Disentangling Factors of Variations Using Few Labels. - Francesco Locatello; Michael Tschannen; Stefan Bauer; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem (ICLR, 2020)

Weakly-Supervised Disentanglement Without Compromises. - Francesco Locatello; Ben Poole; Gunnar Rätsch; Bernhard Schölkopf; Olivier Bachem; Michael Tschannen (ICML, 2020)

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. - Geoffrey Négiar; Gideon Dresdner; Alicia Y. Tsai; Laurent El Ghaoui; Francesco Locatello; Robert Freund; Fabian Pedregosa (ICML, 2020)

Object-Centric Learning with Slot Attention. - Francesco Locatello; Dirk Weissenborn; Thomas Unterthiner; Aravindh Mahendran; Georg Heigold; Jakob Uszkoreit; Alexey Dosovitskiy; Thomas Kipf (NeurIPS, 2020)

SCIM: universal single-cell matching with unpaired feature sets. - Stefan G. Stark; Joanna Ficek; Francesco Locatello; Ximena Bonilla; Stéphane Chevrier; Franziska Singer; Tumor Profiler Consortium; Gunnar Rätsch; Kjong-Van Lehmann (Bioinform., 2020)

Stochastic Frank-Wolfe for Constrained Finite-Sum Minimization. - Geoffrey Négiar; Gideon Dresdner; Alicia Y. Tsai; Laurent El Ghaoui; Francesco Locatello; Fabian Pedregosa (CoRR, 2020)

Is Independence all you need? On the Generalization of Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Anirudh Goyal; Francesco Locatello; Bernhard Schölkopf; Stefan Bauer (CoRR, 2020)

A Sober Look at the Unsupervised Learning of Disentangled Representations and their Evaluation. - Francesco Locatello; Stefan Bauer; Mario Lucic; Gunnar Rätsch; Sylvain Gelly; Bernhard Schölkopf; Olivier Bachem (J. Mach. Learn. Res., 2020)

Enforcing and Discovering Structure in Machine Learning. - Francesco Locatello (2020)

On the Transfer of Disentangled Representations in Realistic Settings. - Andrea Dittadi; Frederik Träuble; Francesco Locatello; Manuel Wuthrich; Vaibhav Agrawal; Ole Winther; Stefan Bauer; Bernhard Schölkopf (ICLR, 2021)

On Disentangled Representations Learned from Correlated Data. - Frederik Träuble; Elliot Creager; Niki Kilbertus; Francesco Locatello; Andrea Dittadi; Anirudh Goyal; Bernhard Schölkopf; Stefan Bauer (ICML, 2021)

Neighborhood Contrastive Learning Applied to Online Patient Monitoring. - Hugo Yèche; Gideon Dresdner; Francesco Locatello; Matthias Hüser; Gunnar Rätsch (ICML, 2021)

Boosting Variational Inference With Locally Adaptive Step-Sizes. - Gideon Dresdner; Saurav Shekhar; Fabian Pedregosa; Francesco Locatello; Gunnar Rätsch (IJCAI, 2021)

Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style. - Julius von Kügelgen; Yash Sharma 001; Luigi Gresele; Wieland Brendel; Bernhard Schölkopf; Michel Besserve; Francesco Locatello (NeurIPS, 2021)

Dynamic Inference with Neural Interpreters. - Nasim Rahaman; Muhammad Waleed Gondal; Shruti Joshi; Peter V. Gehler; Yoshua Bengio; Francesco Locatello; Bernhard Schölkopf (NeurIPS, 2021)

Backward-Compatible Prediction Updates: A Probabilistic Approach. - Frederik Träuble; Julius von Kügelgen; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Peter V. Gehler (NeurIPS, 2021)

Towards Causal Representation Learning. - Bernhard Schölkopf; Francesco Locatello; Stefan Bauer; Nan Rosemary Ke; Nal Kalchbrenner; Anirudh Goyal; Yoshua Bengio (CoRR, 2021)

Representation Learning for Out-Of-Distribution Generalization in Reinforcement Learning. - Andrea Dittadi; Frederik Träuble; Manuel Wüthrich; Felix Widmaier; Peter V. Gehler; Ole Winther; Francesco Locatello; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (CoRR, 2021)

Enforcing and Discovering Structure in Machine Learning. - Francesco Locatello (CoRR, 2021)

Toward Causal Representation Learning. - Bernhard Schölkopf; Francesco Locatello; Stefan Bauer; Nan Rosemary Ke; Nal Kalchbrenner; Anirudh Goyal; Yoshua Bengio (Proc. IEEE, 2021)

Faster One-Sample Stochastic Conditional Gradient Method for Composite Convex Minimization. - Gideon Dresdner; Maria-Luiza Vladarean; Gunnar Rätsch; Francesco Locatello; Volkan Cevher; Alp Yurtsever (AISTATS, 2022)

Leveling Down in Computer Vision: Pareto Inefficiencies in Fair Deep Classifiers. - Dominik Zietlow; Michael Lohaus; Guha Balakrishnan; Matthäus Kleindessner; Francesco Locatello; Bernhard Schölkopf; Chris Russell 001 (CVPR, 2022)

You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction. - Osama Makansi; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Dominik Janzing; Thomas Brox; Bernhard Schölkopf (ICLR, 2022)

Visual Representation Learning Does Not Generalize Strongly Within the Same Domain. - Lukas Schott; Julius von Kügelgen; Frederik Träuble; Peter Vincent Gehler; Chris Russell 001; Matthias Bethge; Bernhard Schölkopf; Francesco Locatello; Wieland Brendel (ICLR, 2022)

The Role of Pretrained Representations for the OOD Generalization of RL Agents. - Frederik Träuble; Andrea Dittadi; Manuel Wuthrich; Felix Widmaier; Peter Vincent Gehler; Ole Winther; Francesco Locatello; Olivier Bachem; Bernhard Schölkopf; Stefan Bauer (ICLR, 2022)

Generalization and Robustness Implications in Object-Centric Learning. - Andrea Dittadi; Samuele S. Papa; Michele De Vita; Bernhard Schölkopf; Ole Winther; Francesco Locatello (ICML, 2022)

Score Matching Enables Causal Discovery of Nonlinear Additive Noise Models. - Paul Rolland; Volkan Cevher; Matthäus Kleindessner; Chris Russell 001; Dominik Janzing; Bernhard Schölkopf; Francesco Locatello (ICML, 2022)

Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks. - Michael Lohaus; Matthäus Kleindessner; Krishnaram Kenthapadi; Francesco Locatello; Chris Russell 001 (NeurIPS, 2022)

Neural Attentive Circuits. - Martin Weiss; Nasim Rahaman; Francesco Locatello; Chris Pal; Yoshua Bengio; Bernhard Schölkopf; Li Erran Li; Nicolas Ballas (NeurIPS, 2022)

Assaying Out-Of-Distribution Generalization in Transfer Learning. - Florian Wenzel; Andrea Dittadi; Peter V. Gehler; Carl-Johann Simon-Gabriel; Max Horn; Dominik Zietlow; David Kernert; Chris Russell 001; Thomas Brox; Bernt Schiele; Bernhard Schölkopf; Francesco Locatello (NeurIPS, 2022)

Self-supervised Amodal Video Object Segmentation. - Jian Yao 004; Yuxin Hong; Chiyu Wang; Tianjun Xiao; Tong He 002; Francesco Locatello; David P. Wipf; Yanwei Fu 001; Zheng Zhang 001 (NeurIPS, 2022)

Compositional Multi-Object Reinforcement Learning with Linear Relation Networks. - Davide Mambelli; Frederik Träuble; Stefan Bauer; Bernhard Schölkopf; Francesco Locatello (CoRR, 2022)

A General Purpose Neural Architecture for Geospatial Systems. - Nasim Rahaman; Martin Weiss; Frederik Träuble; Francesco Locatello; Alexandre Lacoste; Yoshua Bengio; Chris Pal; Li Erran Li; Bernhard Schölkopf (CoRR, 2022)

Causal Triplet: An Open Challenge for Intervention-centric Causal Representation Learning. - Yuejiang Liu; Alexandre Alahi; Chris Russell 001; Max Horn; Dominik Zietlow; Bernhard Schölkopf; Francesco Locatello (CLeaR, 2023)

Causal Discovery with Score Matching on Additive Models with Arbitrary Noise. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

Scalable Causal Discovery with Score Matching. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Kun Zhang 001; Francesco Locatello (CLeaR, 2023)

Unsupervised Object Learning via Common Fate. - Matthias Tangemann; Steffen Schneider 001; Julius von Kügelgen; Francesco Locatello; Peter Vincent Gehler; Thomas Brox; Matthias Kümmerer; Matthias Bethge; Bernhard Schölkopf (CLeaR, 2023)

Unsupervised Open-Vocabulary Object Localization in Videos. - Ke Fan; Zechen Bai; Tianjun Xiao; Dominik Zietlow; Max Horn; Zixu Zhao; Carl-Johann Simon-Gabriel; Mike Zheng Shou; Francesco Locatello; Bernt Schiele; Thomas Brox; Zheng Zhang 001; et al. (ICCV, 2023)

Object-Centric Multiple Object Tracking. - Zixu Zhao; Jiaze Wang; Max Horn; Yizhuo Ding; Tong He 002; Zechen Bai; Dominik Zietlow; Carl-Johann Simon-Gabriel; Bing Shuai; Zhuowen Tu; Thomas Brox; Bernt Schiele; et al. (ICCV, 2023)

Relative representations enable zero-shot latent space communication. - Luca Moschella; Valentino Maiorca; Marco Fumero; Antonio Norelli; Francesco Locatello; Emanuele Rodolà (ICLR, 2023)

Bridging the Gap to Real-World Object-Centric Learning. - Maximilian Seitzer; Max Horn; Andrii Zadaianchuk; Dominik Zietlow; Tianjun Xiao; Carl-Johann Simon-Gabriel; Tong He 002; Zheng Zhang 001; Bernhard Schölkopf; Thomas Brox; Francesco Locatello (ICLR, 2023)

Unsupervised Semantic Segmentation with Self-supervised Object-centric Representations. - Andrii Zadaianchuk; Matthäus Kleindessner; Yi Zhu; Francesco Locatello; Thomas Brox (ICLR, 2023)

Benign Overfitting in Deep Neural Networks under Lazy Training. - Zhenyu Zhu; Fanghui Liu 001; Grigorios Chrysos 002; Francesco Locatello; Volkan Cevher (ICML, 2023)

Leveraging sparse and shared feature activations for disentangled representation learning. - Marco Fumero; Florian Wenzel; Luca Zancato; Alessandro Achille; Emanuele Rodolà; Stefano Soatto; Bernhard Schölkopf; Francesco Locatello (NeurIPS, 2023)

Rotating Features for Object Discovery. - Sindy Löwe; Phillip Lippe; Francesco Locatello; Max Welling (NeurIPS, 2023)

Latent Space Translation via Semantic Alignment. - Valentino Maiorca; Luca Moschella; Antonio Norelli; Marco Fumero; Francesco Locatello; Emanuele Rodolà (NeurIPS, 2023)

Assumption violations in causal discovery and the robustness of score matching. - Francesco Montagna; Atalanti-Anastasia Mastakouri; Elias Eulig; Nicoletta Noceti; Lorenzo Rosasco; Dominik Janzing; Bryon Aragam; Francesco Locatello (NeurIPS, 2023)

ASIF: Coupled Data Turns Unimodal Models to Multimodal without Training. - Antonio Norelli; Marco Fumero; Valentino Maiorca; Luca Moschella; Emanuele Rodolà; Francesco Locatello (NeurIPS, 2023)

Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling. - Zhenyu Zhu; Francesco Locatello; Volkan Cevher (NeurIPS, 2023)

Preface of UniReps: the First Workshop on Unifying Representations in Neural Models. - Marco Fumero; Emanuele Rodolà; Clémentine C. J. Dominé; Francesco Locatello; Karolina Dziugaite; Mathilde Caron (UniReps, 2023)

TeST: Test-time Self-Training under Distribution Shift. - Samarth Sinha; Peter V. Gehler; Francesco Locatello; Bernt Schiele (WACV, 2023)

Divided Attention: Unsupervised Multi-Object Discovery with Contextually Separated Slots. - Dong Lao; Zhengyang Hu 002; Francesco Locatello; Yanchao Yang 001; Stefano Soatto (CoRR, 2023)

A data augmentation perspective on diffusion models and retrieval. - Max F. Burg; Florian Wenzel; Dominik Zietlow; Max Horn; Osama Makansi; Francesco Locatello; Chris Russell 001 (CoRR, 2023)

Unsupervised Conditional Slot Attention for Object Centric Learning. - Avinash Kori; Francesco Locatello; Francesca Toni; Ben Glocker (CoRR, 2023)

Shortcuts for causal discovery of nonlinear models by score matching. - Francesco Montagna; Nicoletta Noceti; Lorenzo Rosasco; Francesco Locatello (CoRR, 2023)

Image retrieval outperforms diffusion models on data augmentation. - Max F. Burg; Florian Wenzel; Dominik Zietlow; Max Horn; Osama Makansi; Francesco Locatello; Chris Russell 001 (Trans. Mach. Learn. Res., 2023)

Self-Compatibility: Evaluating Causal Discovery without Ground Truth. - Philipp Michael Faller; Leena C. Vankadara; Atalanti-Anastasia Mastakouri; Francesco Locatello; Dominik Janzing (AISTATS, 2024)

Adaptive Slot Attention: Object Discovery with Dynamic Slot Number. - Ke Fan; Zechen Bai; Tianjun Xiao; Tong He 002; Max Horn; Yanwei Fu 001; Francesco Locatello; Zheng Zhang 001 (CVPR, 2024)

Grounded Object-Centric Learning. - Avinash Kori; Francesco Locatello; Fabio De Sousa Ribeiro; Francesca Toni; Ben Glocker (ICLR, 2024)

Multi-View Causal Representation Learning with Partial Observability. - Dingling Yao; Danru Xu; Sébastien Lachapelle; Sara Magliacane; Perouz Taslakian; Georg Martius; Julius von Kügelgen; Francesco Locatello (ICLR, 2024)

Unsupervised Concept Discovery Mitigates Spurious Correlations. - Md Rifat Arefin; Yan Zhang; Aristide Baratin; Francesco Locatello; Irina Rish; Dianbo Liu; Kenji Kawaguchi (ICML, 2024)

Mechanistic Neural Networks for Scientific Machine Learning. - Adeel Pervez; Francesco Locatello; Stratis Gavves (ICML, 2024)

A Sparsity Principle for Partially Observable Causal Representation Learning. - Danru Xu; Dingling Yao; Sébastien Lachapelle; Perouz Taslakian; Julius von Kügelgen; Francesco Locatello; Sara Magliacane (ICML, 2024)

Smoke and Mirrors in Causal Downstream Tasks. - Riccardo Cadei; Lukas Lindorfer; Sylvia Cremer; Cordelia Schmid; Francesco Locatello (NeurIPS, 2024)

Identifying General Mechanism Shifts in Linear Causal Representations. - Tianyu Chen; Kevin Bello; Francesco Locatello; Bryon Aragam; Pradeep Ravikumar (NeurIPS, 2024)

Latent Functional Maps: a spectral framework for representation alignment. - Marco Fumero; Marco Pegoraro 002; Valentino Maiorca; Francesco Locatello; Emanuele Rodolà (NeurIPS, 2024)

Identifiable Object-Centric Representation Learning via Probabilistic Slot Attention. - Avinash Kori; Francesco Locatello; Ainkaran Santhirasekaram; Francesca Toni; Ben Glocker; Fabio De Sousa Ribeiro (NeurIPS, 2024)

Marrying Causal Representation Learning with Dynamical Systems for Science. - Dingling Yao; Caroline Muller; Francesco Locatello (NeurIPS, 2024)

Binding Dynamics in Rotating Features. - Sindy Löwe; Francesco Locatello; Max Welling (CoRR, 2024)

Mechanistic Neural Networks for Scientific Machine Learning. - Adeel Pervez; Francesco Locatello; Efstratios Gavves (CoRR, 2024)

Two Tricks to Improve Unsupervised Segmentation Learning. - Alp Eren Sari; Francesco Locatello; Paolo Favaro (CoRR, 2024)

Demystifying amortized causal discovery with transformers. - Francesco Montagna; Max Cairney-Leeming; Dhanya Sridhar; Francesco Locatello (CoRR, 2024)

Scalable unsupervised alignment of general metric and non-metric structures. - Sanketh Vedula; Valentino Maiorca; Lorenzo Basile; Francesco Locatello; Alexander M. Bronstein (CoRR, 2024)

Latent Functional Maps. - Marco Fumero; Marco Pegoraro 002; Valentino Maiorca; Francesco Locatello; Emanuele Rodolà (CoRR, 2024)

Latent Space Translation via Inverse Relative Projection. - Valentino Maiorca; Luca Moschella; Marco Fumero; Francesco Locatello; Emanuele Rodolà (CoRR, 2024)

NeCo: Improving DINOv2's spatial representations in 19 GPU hours with Patch Neighbor Consistency. - Valentinos Pariza; Mohammadreza Salehi; Gertjan J. Burghouts; Francesco Locatello; Yuki M. Asano (CoRR, 2024)

Adjusting Pretrained Backbones for Performativity. - Berker Demirel; Lingjing Kong; Kun Zhang 001; Theofanis Karaletsos; Celestine Mendler-Dünner; Francesco Locatello (CoRR, 2024)

Look Around and Find Out: OOD Detection with Relative Angles. - Berker Demirel; Marco Fumero; Francesco Locatello (CoRR, 2024)

Score matching through the roof: linear, nonlinear, and latent variables causal discovery. - Francesco Montagna; Philipp Michael Faller; Patrick Blöbaum; Elke Kirschbaum; Francesco Locatello (CLeaR, 2025)

Scalable Mechanistic Neural Networks. - Jiale Chen 004; Dingling Yao; Adeel Pervez; Dan Alistarh; Francesco Locatello (ICLR, 2025)

How to Probe: Simple Yet Effective Techniques for Improving Post-hoc Explanations. - Siddhartha Gairola; Moritz Böhle; Francesco Locatello; Bernt Schiele (ICLR, 2025)

Near, far: Patch-ordering enhances vision foundation models' scene understanding. - Valentinos Pariza; Mohammadreza Salehi; Gertjan J. Burghouts; Francesco Locatello; Yuki M. Asano (ICLR, 2025)

Unifying Causal Representation Learning with the Invariance Principle. - Dingling Yao; Dario Rancati; Riccardo Cadei; Marco Fumero; Francesco Locatello (ICLR, 2025)

ResiDual Transformer Alignment with Spectral Decomposition. - Lorenzo Basile; Valentino Maiorca; Luca Bortolussi; Emanuele Rodolà; Francesco Locatello (Trans. Mach. Learn. Res., 2025)

Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences. - Riccardo Cadei; Ilker Demirel; Piersilvio De Bartolomeis; Lukas Lindorfer; Sylvia Cremer; Cordelia Schmid; Francesco Locatello (CoRR, 2025)

Mechanistic PDE Networks for Discovery of Governing Equations. - Adeel Pervez; Efstratios Gavves; Francesco Locatello (CoRR, 2025)

The Third Pillar of Causal Analysis? A Measurement Perspective on Causal Representations. - Dingling Yao; Shimeng Huang; Riccardo Cadei; Kun Zhang 001; Francesco Locatello (CoRR, 2025)

Navigating the Latent Space Dynamics of Neural Models. - Marco Fumero; Luca Moschella; Emanuele Rodolà; Francesco Locatello (CoRR, 2025)

Connecting Neural Models Latent Geometries with Relative Geodesic Representations. - Hanlin Yu; Berfin Inal; Georgios Arvanitidis; Søren Hauberg; Francesco Locatello; Marco Fumero (CoRR, 2025)