| Multi-Facet Clustering Variational Autoencoders F Falck, H Zhang, M Willetts, G Nicholson, C Yau, CC Holmes NeurIPS 2021, 2021 | 41 | 2021 |
| A Unified Framework for U-Net Design and Analysis C Williams, F Falck, G Deligiannidis, C Holmes, A Doucet, S Syed NeurIPS 2023, 2023 | 39 | 2023 |
| Measuring Proximity Between Newspapers and Political Parties: The Sentiment Political Compass F Falck, J Marstaller, N Stoehr, S Maucher, J Ren, A Thalhammer, ... Policy & Internet 12 (3), 367-399, 2020 | 37* | 2020 |
| Machine Learning for Health (ML4H) 2020: Advancing Healthcare for All SK Sarkar, S Roy, E Alsentzer, MBA McDermott, F Falck, I Bica, G Adams, ... Proceedings of Machine Learning Research, 1-11, 2020 | 16 | 2020 |
| DE VITO: A dual-arm, high degree-of-freedom, lightweight, inexpensive, passive upper-limb exoskeleton for robot teleoperation F Falck, K Larppichet, P Kormushev TAROS 2019 (Best Paper Award), 78-89, 2019 | 13 | 2019 |
| Neural Score Matching for High-Dimensional Causal Inference O Clivio, F Falck, B Lehmann, G Deligiannidis, C Holmes AISTATS 2022, 2022 | 10 | 2022 |
| Ivy: Templated Deep Learning for Inter-Framework Portability D Lenton, F Pardo, F Falck, S James, R Clark arXiv preprint arXiv:2102.02886, 2021 | 9 | 2021 |
| A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs F Falck, C Williams, D Danks, G Deligiannidis, C Yau, CC Holmes, ... NeurIPS 2022 (oral), 2022 | 8 | 2022 |
| Human-centered manipulation and navigation with Robot DE NIRO F Falck, S Doshi, N Smuts, J Lingi, K Rants, P Kormushev IROS 2018 Workshop Towards Robots that Exhibit Manipulation Intelligence …, 2018 | 8 | 2018 |
| MAIRA-2: Grounded Radiology Report Generation S Bannur, K Bouzid, DC Castro, A Schwaighofer, S Bond-Taylor, M Ilse, ... arXiv preprint arXiv:2406.04449, 2024 | 7 | 2024 |
| Robot DE NIRO: a human-centered, autonomous, mobile research platform for cognitively-enhanced manipulation F Falck, S Doshi, M Tormento, G Nersisyan, N Smuts, J Lingi, K Rants, ... Frontiers in Robotics and AI 7, 66, 2020 | 7 | 2020 |
| Machine Learning for Health (ML4H) 2021 S Roy, S Pfohl, GA Tadesse, L Oala, F Falck, Y Zhou, L Shen, G Zamzmi, ... Machine Learning for Health, 1-12, 2021 | 6 | 2021 |
| Comparing View-Based and Map-Based Semantic Labelling in Real-Time SLAM Z Landgraf, F Falck, M Bloesch, S Leutenegger, A Davison ICRA 2020, 2020 | 6 | 2020 |
| Machine Learning for Health (ML4H) 2019: What Makes Machine Learning in Medicine Different? AV Dalca, MBA McDermott, E Alsentzer, SG Finlayson, M Oberst, F Falck, ... Proceedings of Machine Learning Research, 1-9, 2020 | 4 | 2020 |
| Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective F Falck, Z Wang, C Holmes ICML 2024; SeT LLM workshop @ ICLR 2024 (oral), 2024 | 3 | 2024 |
| A critical review of Causal Inference benchmarks for Large Language Models L Yang, O Clivio, V Shirvaikar, F Falck AAAI 2024 workshop on Are Large Language Models Simply Causal Parrots? (oral), 2024 | 3 | 2024 |
| Detecting Patterns of Physiological Response to Hemodynamic Stress via Unsupervised Deep Learning C Gao, F Falck, M Goswami, A Wertz, MR Pinsky, A Dubrawski NeurIPS 2019 Workshop Machine Learning for Health (ML4H), 2019 | 3 | 2019 |
| ML4H Abstract Track 2019 M McDermott, E Alsentzer, S Finlayson, M Oberst, F Falck, T Naumann, ... arXiv preprint arXiv:2002.01584, 2020 | 2 | 2020 |
| Deep sequence modeling for hemorrhage diagnosis F Falck, MR Pinsky, A Dubrawski NeurIPS Machine Learning for Health (ML4H) Workshop (spotlight), 2018 | 2 | 2018 |
| Are Large Language Models Bayesian? A Martingale Perspective on In-Context Learning F Falck, Z Wang, CC Holmes ICLR 2024 Workshop on Secure and Trustworthy Large Language Models, 0 | 2 | |
Chris HolmesVerified email at stats.ox.ac.uk
