Research publications that used Baskerville services are listed below. To acknowledge the use of Baskerville and add your publication to the Baskerville publications list, see the guidance in the Baskerville documentation.

2025

  • Asebiah, D.C., Peters, A.N., Borgia, L., et al. (2025) (NH3(CH2)7NH3)2Sn3I10, a Vacancy-Ordered Three-Dimensional Tin(II) Perovskite-Derived Semiconductor. Chemistry of Materials, 37 (5): 1983–1994. doi:10.1021/acs.chemmater.4c03411.
  • Bellos, D., Allsopp, J., Ho, E.M.L., et al. (2025) FlowCron - Increasing access to HPC by wrapping Globus into a function-as-a-service. Wellcome Open Research, 10: 4. doi:10.12688/wellcomeopenres.23491.1.
  • Cassingham, M.A., Lamahewage, S.N.S., Goh, Y.G., et al. (2025) Ordered Cationic Mixing in a 1D Organic–Inorganic Hybrid. Chemistry of Materials, 37 (7): 2418–2426. doi:10.1021/acs.chemmater.4c02364.
  • Claes, R. and Scanlon, D.O. (2025) New Insights into the Intrinsic Transport Properties of Sb2O5 and ZnSb2O6. physica status solidi (RRL) – Rapid Research Letters. doi:10.1002/pssr.202400399.
  • Knight, J.C., Jersakova, R. and James Bishop (2025) Practical Perspectives on Black-Box Critical Error Detection for Machine Translation. The Alan Turing Institute. doi:10.5281/zenodo.14639666.
  • Lam, T.K., Gaido, M., Papi, S., et al. (2025) Prepending or Cross-Attention for Speech-to-Text? An Empirical Comparison. doi:10.48550/arXiv.2501.02370.
  • Lin, D., Hu, H. and Jiao, J. (2025) What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images. doi:10.48550/arXiv.2503.17899.
  • Streit, J.O., Chan, S.H.S., Burridge, C., et al. (2025) Long-range electrostatic forces govern how proteins fold on the ribosome. doi:10.1101/2025.02.10.637539.
  • Wood, R. and McDonough, K. (2025) MapReader_railspace_London_imago_mundi_2025. doi:10.5281/zenodo.14522925.

2024

  • Ahn, M., Streit, J.O., Waudby, C.A., et al. (2024) Amyloid forming human lysozyme intermediates are stabilised by non-native amide-π interactions. doi:10.1101/2024.12.20.629167.
  • Aljasem, D. and Howes, A. (2024) Understanding the Effects of Visual Impairment on Visual Search. Universal Access in Human-Computer Interaction, pp. 363–381. doi:10.1007/978-3-031-60884-1_25.
  • Asebiah, D.C., Mozur, E.M., Koegel, A.A., et al. (2024) Defect-Limited Mobility and Defect Thermochemistry in Mixed A-Cation Tin Perovskites: (CH3NH3)1–xCsxSnBr3. ACS Applied Energy Materials, 7 (18): 7992–8003. doi:10.1021/acsaem.4c01708.
  • Ball, N.D., MacArt, J.F. and Sirignano, J. (2024) Online Optimisation of Machine Learning Collision Models to Accelerate Direct Molecular Simulation of Rarefied Gas Flows. doi:10.48550/arXiv.2411.13423.
  • Bartlett, J.J., Davey, C.E., Johnston, L.A., et al. (2024) Recovering high‐quality fiber orientation distributions from a reduced number of diffusion‐weighted images using a model‐driven deep learning architecture. Magnetic Resonance in Medicine, 92 (5): 2193–2206. doi:10.1002/mrm.30187.
  • Baziotis, C., Zhang, B., Birch, A., et al. (2024) When Does Monolingual Data Help Multilingual Translation: The Role of Domain and Model Scale. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 6297–6324. doi:10.18653/v1/2024.naacl-long.349.
  • Chen, H., Hou, Y., Qu, C., et al. (2024) $360+x$: A Panoptic Multi-modal Scene Understanding Dataset. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19373–19382. doi:10.1109/CVPR52733.2024.01833.
  • Chen, P., Yu, S., Guo, Z., et al. (2024) Is It Good Data for Multilingual Instruction Tuning or Just Bad Multilingual Evaluation for Large Language Models? Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 9706–9726. doi:10.18653/v1/2024.emnlp-main.542.
  • Chen, X., Diggle, P., Zidek, J.V., et al. (2024) Highly Multivariate Large-scale Spatial Stochastic Processes -- A Cross-Markov Random Field Approach. doi:10.48550/arXiv.2408.10396.
  • Cheng, X., Jia, X., Lu, W., et al. (2024) WiNet: Wavelet-Based Incremental Learning for Efficient Medical Image Registration. Medical Image Computing and Computer Assisted Intervention – MICCAI 2024, pp. 761–771. doi:10.1007/978-3-031-72069-7_71.
  • De Leon, F.A.L., Madabushi, H.T. and Lee, M. (2024) Code-Mixed Probes Show How Pre-Trained Models Generalise On Code-Switched Text. doi:10.48550/arXiv.2403.04872.
  • Dou, W., Spooner, K., Kavanagh, S., et al. (2024) Giant Band Degeneracy via Orbital Engineering Enhances Thermoelectric Performance from Sb2Si2Te6 to Sc2Si2Te6. doi:10.26434/chemrxiv-2024-hm6vh.
  • Evans, M.L., Bergsma, J., Merkys, A., et al. (2024) Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange. Digital Discovery, 3 (8): 1509–1533. doi:10.1039/D4DD00039K.
  • Falck, F., Wang, Z. and Holmes, C. (2024) Is In-Context Learning in Large Language Models Bayesian? A Martingale Perspective. doi:10.48550/arXiv.2406.00793.
  • Fonseca, M. and Cohen, S. (2024) Can Large Language Model Summarizers Adapt to Diverse Scientific Communication Goals? Findings of the Association for Computational Linguistics ACL 2024, pp. 8599–8618. doi:10.18653/v1/2024.findings-acl.508.
  • Gerard, L., Scherbela, M., Sutterud, H., et al. (2024) Transferable Neural Wavefunctions for Solids. doi:10.48550/arXiv.2405.07599.
  • Hackenburg, K., Tappin, B.M., Röttger, P., et al. (2024) Evidence of a log scaling law for political persuasion with large language models. doi:10.48550/arXiv.2406.14508.
  • Hu, H., Yu, S., Chen, P., et al. (2024) Fine-tuning Large Language Models with Sequential Instructions. doi:10.48550/arXiv.2403.07794.
  • Hu, J., Squires, A.G., Kondek, J., et al. (2024) Enabling ionic transport in Li3AlP2 the roles of defects and disorder. doi:10.26434/chemrxiv-2024-3s0kh-v2.
  • Iyer, V., Malik, B., Stepachev, P., et al. (2024) Quality or Quantity? On Data Scale and Diversity in Adapting Large Language Models for Low-Resource Translation. Proceedings of the Ninth Conference on Machine Translation, pp. 1393–1409. doi:10.18653/v1/2024.wmt-1.128.
  • Iyer, V., Malik, B., Zhu, W., et al. (2024) Exploring Very Low-Resource Translation with LLMs: The University of Edinburgh’s Submission to AmericasNLP 2024 Translation Task. Proceedings of the 4th Workshop on Natural Language Processing for Indigenous Languages of the Americas (AmericasNLP 2024), pp. 209–220. doi:10.18653/v1/2024.americasnlp-1.25.
  • Jiang, D., Fonseca, M. and Cohen, S. (2024) LeanReasoner: Boosting Complex Logical Reasoning with Lean. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 7497–7510. doi:10.18653/v1/2024.naacl-long.416.
  • Kavanagh, S.R., Nielsen, R.S., Hansen, J.L., et al. (2024) Intrinsic point defect tolerance in selenium for indoor and tandem photovoltaics. doi:10.26434/chemrxiv-2024-91h02.
  • Khanuja, S., Iyer, V., He, C., et al. (2024) Towards Automatic Evaluation for Image Transcreation. doi:10.48550/arXiv.2412.13717.
  • Kin Lam, T., Birch, A. and Haddow, B. (2024) Compact Speech Translation Models via Discrete Speech Units Pretraining. Proceedings of the 21st International Conference on Spoken Language Translation (IWSLT 2024), pp. 114–124. doi:10.18653/v1/2024.iwslt-1.16.
  • Klimaszewski, M., Andruszkiewicz, P. and Birch, A. (2024) Is Modularity Transferable? A Case Study through the Lens of Knowledge Distillation. doi:10.48550/arXiv.2403.18804.
  • Klimaszewski, M., Andruszkiewicz, P. and Birch, A. (2024) No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement. doi:10.48550/arXiv.2404.15737.
  • Ko, S., Dorrell, J., Alter, E., et al. (2024) Extreme Defect Tolerance for Electrochemical Intercalation in Wadsley–Roth Structures Demonstrated by Metastable NaNb7O18. doi:10.26434/chemrxiv-2024-z07w1.
  • Lazauskas, T. and Llewellyn-Jones, D. (2024) Benchmarking the performance of GPT-2 type applications on GPU-accelerated computing resources. The Alan Turing Institute. doi:10.5281/zenodo.13349540.
  • Li, Z., Yang, X. and Zhang, J. (2024) GAMAFlow: Estimating 3D Scene Flow via Grouped Attention and Global Motion Aggregation. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3955–3959. doi:10.1109/ICASSP48485.2024.10447849.
  • Lou, W.T., Sutterud, H., Cassella, G., et al. (2024) Neural Wave Functions for Superfluids. Physical Review X, 14 (2). doi:10.1103/PhysRevX.14.021030.
  • Machado, I.P., Reithmeir, A., Kogl, F., et al. (2024) A Self-supervised Image Registration Approach for Measuring Local Response Patterns in Metastatic Ovarian Cancer. Biomedical Image Registration, pp. 295–307. doi:10.1007/978-3-031-73480-9_23.
  • Malik, Z., Broadley, S., Herkelrath, S.J.C., et al. (2024) Observation and enhancement through alkali metal doping of p-type conductivity in the layered oxyselenides Sr2ZnO2Cu2Se2 and Ba2Zn1−xO2−xCu2Se2. Journal of Materials Chemistry C, 12 (43): 17574–17586. doi:10.1039/D4TC02458C.
  • Malik, Z., Kemp, L., Grosso, B.F., et al. (2024) Transport Properties of Doped Wide Band Gap Layered Oxychalcogenide Semiconductors Sr2GaO3CuCh, Sr2ScO3CuCh, and Sr2InO3CuCh (Ch = S or Se). Chemistry of Materials, 36 (22): 11326–11337. doi:10.1021/acs.chemmater.4c02760.
  • McDonough, K., Beelen, K., Wilson, D.C.S., et al. (2024) Reading Maps at a Distance: Texts on Maps as New Historical Data. Imago Mundi, 76 (2): 296–307. doi:10.1080/03085694.2024.2453336.
  • Mohamed, M., Cunningham, H.J., Deisenroth, M.P., et al. (2024) RecMoDiffuse: Recurrent Flow Diffusion for Human Motion Generation. doi:10.48550/arXiv.2406.07169.
  • Murdock, B.E., Cen, J., Squires, A.G., et al. (2024) Li‐Site Defects Induce Formation of Li‐Rich Impurity Phases: Implications for Charge Distribution and Performance of LiNi0.5−xMxMn1.5O4 Cathodes (M = Fe and Mg; x = 0.05–0.2). Advanced Materials, 36 (32). doi:10.1002/adma.202400343.
  • Pal, P., Birch, A. and Heafield, K. (2024) Document-Level Machine Translation with Large-Scale Public Parallel Corpora. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 13185–13197. doi:10.18653/v1/2024.acl-long.712.
  • Pfau, D., Axelrod, S., Sutterud, H., et al. (2024) Accurate computation of quantum excited states with neural networks. Science, 385 (6711). doi:10.1126/science.adn0137.
  • Qiu, Y., Zhao, Z., Ziser, Y., et al. (2024) Spectral Editing of Activations for Large Language Model Alignment. doi:10.48550/arXiv.2405.09719.
  • Qiu, Y., Zhao, Z., Ziser, Y., et al. (2024) Are Large Language Model Temporally Grounded? Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 7064–7083. doi:10.18653/v1/2024.naacl-long.391.
  • Ramírez, G., Birch, A. and Titov, I. (2024) Optimising Calls to Large Language Models with Uncertainty-Based Two-Tier Selection. doi:10.48550/arXiv.2405.02134.
  • Ramírez, G., Lindemann, M., Birch, A., et al. (2024) Cache & Distil: Optimising API Calls to Large Language Models. Findings of the Association for Computational Linguistics ACL 2024, pp. 11838–11853. doi:10.18653/v1/2024.findings-acl.704.
  • Rockwell, K., Hirst, J., Ostler, T.A., et al. (2024) Pseudospectral Landau-Lifshitz description of magnetization dynamics. Physical Review B, 109 (18). doi:10.1103/PhysRevB.109.L180404.
  • Spooner, K.B., Einhorn, M., Davies, D.W., et al. (2024) ThermoParser: Streamlined Analysis of Thermoelectric Properties. Journal of Open Source Software, 9 (97): 6340. doi:10.21105/joss.06340.
  • Squires, A.G., Ganeshkumar, L., Savory, C.N., et al. (2024) Oxygen Dimerization as a Defect-Driven Process in Bulk LiNiO2. ACS Energy Letters, 9 (8): 4180–4187. doi:10.1021/acsenergylett.4c01307.
  • Streit, J.O., Bukvin, I.V., Chan, S.H.S., et al. (2024) The ribosome lowers the entropic penalty of protein folding. Nature, 633 (8028): 232–239. doi:10.1038/s41586-024-07784-4.
  • Streit, J.O., Chan, S.H.S., Daya, S., et al. (2024) Rational design of19F NMR labelling sites to probe protein structure and interactions. doi:10.1101/2024.12.11.627779.
  • Sun, Z. and Miceli-Barone, A.V. (2024) Scaling Behavior of Machine Translation with Large Language Models under Prompt Injection Attacks. doi:10.48550/arXiv.2403.09832.
  • Wang, W., Haddow, B. and Birch, A. (2024) Retrieval-Augmented Multilingual Knowledge Editing. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 335–354. doi:10.18653/v1/2024.acl-long.21.
  • Wang, W., Haddow, B., Wu, M., et al. (2024) Sharing Matters: Analysing Neurons Across Languages and Tasks in LLMs. doi:10.48550/arXiv.2406.09265.
  • Wang, W., Wu, M., Haddow, B., et al. (2024) Bridging the Language Gaps in Large Language Models with Inference-Time Cross-Lingual Intervention. doi:10.48550/arXiv.2410.12462.
  • Włodarski, T., Streit, J.O., Mitropoulou, A., et al. (2024) Bayesian reweighting of biomolecular structural ensembles using heterogeneous cryo-EM maps with the cryoENsemble method. Scientific Reports, 14 (1). doi:10.1038/s41598-024-68468-7.
  • Xiao, C., Zuo, Z. and Wang, S. (2024) FedGA: Federated Learning with Gradient Alignment for Error Asymmetry Mitigation. doi:10.48550/arXiv.2412.16582.
  • Ye, Z., Lovell, L., Faramarzi, A., et al. (2024) Sam-Based Instance Segmentation Models for the Automation of Structural Damage Detection. doi:10.2139/ssrn.4750668.
  • Zhang, T., Zheng, S., Cheng, J., et al. (2024) Structure and Intensity Unbiased Translation for 2D Medical Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 46 (12): 10060–10075. doi:10.1109/TPAMI.2024.3434435.
  • Zhao, Z., Ziser, Y. and Cohen, S.B. (2024) Layer by Layer: Uncovering Where Multi-Task Learning Happens in Instruction-Tuned Large Language Models. doi:10.48550/arXiv.2410.20008.

2023

  • Bogoychev, N. and Chen, P. (2023) Terminology-Aware Translation with Constrained Decoding and Large Language Model Prompting. Proceedings of the Eighth Conference on Machine Translation, pp. 890–896. doi:10.18653/v1/2023.wmt-1.80.
  • Bogoychev, N., van der Linde, J., Nail, G., et al. (2023) OpusCleaner and OpusTrainer, open source toolkits for training Machine Translation and Large language models. doi:10.48550/arXiv.2311.14838.
  • Cassella, G., Sutterud, H., Azadi, S., et al. (2023) Discovering Quantum Phase Transitions with Fermionic Neural Networks. Physical Review Letters, 130 (3). doi:10.1103/PhysRevLett.130.036401.
  • Chen, H., Qu, C., Zhang, Y., et al. (2023) Multi-view Self-supervised Disentanglement for General Image Denoising. 2023 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 12247–12257. doi:10.1109/ICCV51070.2023.01128.
  • Chen, P., Ji, S., Bogoychev, N., et al. (2023) Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca. doi:10.48550/arXiv.2309.08958.
  • Deng, Z., Ma, Y., Liu, Y., et al. (2023) MusiLingo: Bridging Music and Text with Pre-trained Language Models for Music Captioning and Query Response. arXiv. doi:10.48550/arXiv.2309.08730.
  • Duan, J., Jia, X., Bartlett, J., et al. (2023) Arbitrary Order Total Variation for Deformable Image Registration. Pattern Recognition, 137: 109318. doi:10.1016/j.patcog.2023.109318.
  • Falck, F., Williams, C., Danks, D., et al. (2023) A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs. doi:10.48550/arXiv.2301.08187.
  • Fonseca, M. and Cohen, S.B. (2023) Can Large Language Models Follow Concept Annotation Guidelines? A Case Study on Scientific and Financial Domains. doi:10.48550/arXiv.2311.08704.
  • Hermann, J., Spencer, J., Choo, K., et al. (2023) Ab initio quantum chemistry with neural-network wavefunctions. Nature Reviews Chemistry, 7 (10): 692–709. doi:10.1038/s41570-023-00516-8.
  • Hirst, J., Ruta, S., Jackson, J., et al. (2023) Simulations of magnetization reversal in FM/AFM bilayers with THz frequency pulses. Scientific Reports, 13 (1). doi:10.1038/s41598-023-39175-6.
  • Iyer, V., Barba, E., Birch, A., et al. (2023) Code-Switching with Word Senses for Pretraining in Neural Machine Translation. Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 12889–12901. doi:10.18653/v1/2023.findings-emnlp.859.
  • Iyer, V., Chen, P. and Birch, A. (2023) Towards Effective Disambiguation for Machine Translation with Large Language Models. Proceedings of the Eighth Conference on Machine Translation, pp. 482–495. doi:10.18653/v1/2023.wmt-1.44.
  • Jia, X., Bartlett, J., Chen, W., et al. (2023) Fourier-Net: Fast Image Registration with Band-Limited Deformation. Proceedings of the AAAI Conference on Artificial Intelligence, 37 (1): 1015–1023. doi:10.1609/aaai.v37i1.25182.
  • Jia, X., Thorley, A., Gomez, A., et al. (2023) Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration. doi:10.48550/arXiv.2307.02997.
  • McGough, W., Buddenkotte, T., Ursprung, S., et al. (2023) Automated Small Kidney Cancer Detection in Non-Contrast Computed Tomography. doi:10.48550/arXiv.2312.05258.
  • Miceli Barone, A.V., Barez, F., Cohen, S.B., et al. (2023) The Larger they are, the Harder they Fail: Language Models do not Recognize Identifier Swaps in Python. Findings of the Association for Computational Linguistics: ACL 2023, pp. 272–292. doi:10.18653/v1/2023.findings-acl.19.
  • Qiu, Y., Ziser, Y., Korhonen, A., et al. (2023) Detecting and Mitigating Hallucinations in Multilingual Summarisation. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 8914–8932. doi:10.18653/v1/2023.emnlp-main.551.
  • Sarkar, S., Thorpe, L., Benetos, E., et al. (2023) Leveraging Synthetic Data for Improving Chamber Ensemble Separation. 2023 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA), pp. 1–5. doi:10.1109/WASPAA58266.2023.10248118.
  • Wang, H., Oh, J.O., Jin Chang, H., et al. (2023) GazeCaps: Gaze Estimation with Self-Attention-Routed Capsules. 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 2669–2677. doi:10.1109/CVPRW59228.2023.00267.
  • Wang, H., Zhang, Z., Cheng, Y., et al. (2023) High-Fidelity Eye Animatable Neural Radiance Fields for Human Face. doi:10.48550/arXiv.2308.00773.
  • Williams, C., Falck, F., Deligiannidis, G., et al. (2023) A Unified Framework for U-Net Design and Analysis. doi:10.48550/arXiv.2305.19638.
  • Xia, X., Jarsve, K.T., Dijkstra, T., et al. (2023) An integrated hydrodynamic model for runoff-generated debris flows with novel formulation of bed erosion and deposition. Engineering Geology, 326: 107310. doi:10.1016/j.enggeo.2023.107310.
  • Xiao, C. and Wang, S. (2023) Triplets Oversampling for Class Imbalanced Federated Datasets. Machine Learning and Knowledge Discovery in Databases: Research Track, pp. 368–383. doi:10.1007/978-3-031-43415-0_22.
  • Zhang, B., Haddow, B. and Birch, A. (2023) Prompting Large Language Model for Machine Translation: A Case Study. doi:10.48550/arXiv.2301.07069.
  • Zhao, Z., Ziser, Y., Webber, B., et al. (2023) A Joint Matrix Factorization Analysis of Multilingual Representations. Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 12764–12783. doi:10.18653/v1/2023.findings-emnlp.851.

2022

  • Grenander, M., Cohen, S.B. and Steedman, M. (2022) Sentence-Incremental Neural Coreference Resolution. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 427–443. doi:10.18653/v1/2022.emnlp-main.28.
  • Jia, X., Bartlett, J., Zhang, T., et al. (2022) U-Net vs Transformer: Is U-Net Outdated in Medical Image Registration? Machine Learning in Medical Imaging, pp. 151–160. doi:10.1007/978-3-031-21014-3_16.
  • Mould, M. and Gerosa, D. (2022) Gravitational-wave population inference at past time infinity. Physical Review D, 105 (2). doi:10.1103/PhysRevD.105.024076.
  • Mould, M., Gerosa, D. and Taylor, S.R. (2022) Deep learning and Bayesian inference of gravitational-wave populations: Hierarchical black-hole mergers. Physical Review D, 106 (10). doi:10.1103/PhysRevD.106.103013.
  • Na, J., Han, D., Chang, H.J., et al. (2022) Contrastive Vicinal Space for Unsupervised Domain Adaptation. Computer Vision – ECCV 2022, pp. 92–110. doi:10.1007/978-3-031-19830-4_6.
  • Qiu, Y. and Cohen, S.B. (2022) Abstractive Summarization Guided by Latent Hierarchical Document Structure. doi:10.48550/arXiv.2211.09458.
  • Shao, S., Ziser, Y. and Cohen, S.B. (2022) Gold Doesn't Always Glitter: Spectral Removal of Linear and Nonlinear Guarded Attribute Information. doi:10.48550/arXiv.2203.07893.
  • Tse, T.H.E., Kim, K.I., Leonardis, A., et al. (2022) Collaborative Learning for Hand and Object Reconstruction with Attention-guided Graph Convolution. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1654–1664. doi:10.1109/CVPR52688.2022.00171.
  • Tse, T.H.E., Zhang, Z., Kim, K.I., et al. (2022) S$$^2$$Contact: Graph-Based Network for 3D Hand-Object Contact Estimation with Semi-supervised Learning. Computer Vision – ECCV 2022, pp. 568–584. doi:10.1007/978-3-031-19769-7_33.

2021

  • Azadi, S., Drummond, N.D. and Foulkes, W.M.C. (2021) Quasiparticle Effective Mass of the Three-Dimensional Fermi Liquid by Quantum Monte Carlo. Physical Review Letters, 127 (8). doi:10.1103/PhysRevLett.127.086401.
  • Coavoux, M. and Cohen, S.B. (2021) Learning to Match Mathematical Statements with Proofs. doi:10.48550/arXiv.2102.02110.
  • Xiao, C. and Wang, S. (2021) An Experimental Study of Class Imbalance in Federated Learning. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–7. doi:10.1109/SSCI50451.2021.9660072.