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.

2024

  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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. doi:10.1039/D4TC02458C.
  • 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.
  • 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.
  • 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), 35: 3955–3959. doi:10.1109/ICASSP48485.2024.10447849.
  • 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, pp. 1–16. doi:10.1109/TPAMI.2024.3434435.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • Lam, T.K., Birch, A. and Haddow, B. (2024) Compact Speech Translation Models via Discrete Speech Units Pretraining. doi:10.48550/arXiv.2402.19333.
  • 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.
  • Hu, H., Yu, S., Chen, P., et al. (2024) Fine-tuning Large Language Models with Sequential Instructions. doi:10.48550/arXiv.2403.07794.
  • 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.
  • 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.
  • 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.
  • Gerard, L., Scherbela, M., Sutterud, H., et al. (2024) Transferable Neural Wavefunctions for Solids. doi:10.48550/arXiv.2405.07599.
  • 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.
  • 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.
  • 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.
  • 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? doi:10.48550/arXiv.2406.12822.
  • 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.
  • 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. doi:10.48550/arXiv.2407.17114.
  • Chen, X. and Shaddick, G. (2024) Highly Multivariate High-dimensionality Spatial Stochastic Processes-A Mixed Conditional Approach. doi:10.48550/arXiv.2408.10396.
  • 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. doi:10.48550/arXiv.2408.12780.

2023

  • 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.
  • 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.
  • 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.
  • 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.
  • Włodarski, T., Streit, J.O., Mitropoulou, A., et al. (2023) CryoENsemble - a Bayesian approach for reweighting biomolecular structural ensembles using heterogeneous cryo-EM maps. doi:10.1101/2023.11.21.567999.
  • 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.
  • 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). doi:10.1109/CVPRW59228.2023.00267.
  • 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), 4: 12247–12257. doi:10.1109/ICCV51070.2023.01128.
  • 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), 2: 1–5. doi:10.1109/WASPAA58266.2023.10248118.
  • 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.
  • 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.
  • 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.
  • 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.
  • Zhang, B., Haddow, B. and Birch, A. (2023) Prompting Large Language Model for Machine Translation: A Case Study. doi:10.48550/arXiv.2301.07069.
  • 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.
  • Williams, C., Falck, F., Deligiannidis, G., et al. (2023) A Unified Framework for U-Net Design and Analysis. doi:10.48550/arXiv.2305.19638.
  • 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.
  • 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.
  • 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.
  • 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.
  • Iyer, V., Chen, P. and Birch, A. (2023) Towards Effective Disambiguation for Machine Translation with Large Language Models. doi:10.48550/arXiv.2309.11668.
  • Iyer, V., Barba, E., Birch, A., et al. (2023) Code-Switching with Word Senses for Pretraining in Neural Machine Translation. doi:10.48550/arXiv.2310.14050.
  • 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.
  • 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.

2022

  • 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.
  • 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.
  • 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.
  • 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), 36: 1654–1664. doi:10.1109/CVPR52688.2022.00171.
  • 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.
  • 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.
  • Qiu, Y. and Cohen, S.B. (2022) Abstractive Summarization Guided by Latent Hierarchical Document Structure. doi:10.48550/arXiv.2211.09458.

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.
  • Xiao, C. and Wang, S. (2021) An Experimental Study of Class Imbalance in Federated Learning. 2021 IEEE Symposium Series on Computational Intelligence (SSCI), 7: 1–7. doi:10.1109/SSCI50451.2021.9660072.