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. 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. 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.
  • 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.
  • 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.
  • 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.

2023

  • Xiao, C. and Wang, S. (2023) Triplets Oversampling for Class Imbalanced Federated Datasets. Lecture Notes in Computer Science, 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). 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). 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. 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. 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. 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.
  • Lou, W.T., Sutterud, H., Cassella, G., et al. (2023) Neural Wave Functions for Superfluids. arXiv. doi:10.48550/arxiv.2305.06989.
  • 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.
  • Pfau, D., Axelrod, S., Sutterud, H., et al. (2023) Natural Quantum Monte Carlo Computation of Excited States. doi:10.48550/arXiv.2308.16848.
  • 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.
  • Iyer, V., Chen, P. and Birch, A. (2023) Towards Effective Disambiguation for Machine Translation with Large Language Models. doi:10.48550/arXiv.2309.11668.
  • 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.
  • Rockwell, K., Hirst, J., Ostler, T.A., et al. (2023) Pseudo-spectral Landau-Lifshitz description of magnetization dynamics. arXiv. doi:10.48550/arXiv.2312.14068.

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? Lecture Notes in Computer Science, 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). 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. 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). doi:10.1109/SSCI50451.2021.9660072.