Edinburgh Research Explorer

Data Science CDT

Organisational unit: Sub-School

  1. 2019
  2. Analogies Explained: Towards Understanding Word Embeddings

    Allen, C. & Hospedales, T., 3 Jul 2019, Proceedings of the 36th International Conference on Machine Learning (ICML). Chaudhuri, K. & Salakhutdinov, R. (eds.). Long Beach, USA: PMLR, Vol. 97. p. 223-231 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  3. BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning

    Cooper Stickland, A. & Murray, I., 3 Jul 2019, Proceedings of the 36th International Conference on Machine Learning (ICML). Chaudhuri, K. & Salakhutdinov, R. (eds.). Long Beach, USA: PMLR, Vol. 97. p. 5986-5995 12 p. (PMLR; vol. 97).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  4. The problem with probabilistic DAG automata for semantic graphs

    Vasiljeva, I., Gilroy, S. & Lopez, A., 7 Jun 2019, Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics. Minneapolis, Minnesota: Association for Computational Linguistics, Vol. 1. p. 902–911 9 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  5. Efficient Bayesian Experimental Design for Implicit Models

    Kleinegesse, S. & Gutmann, M., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Naha, Okinawa, Japan: PMLR, Vol. 89. p. 476-485 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  6. Sequential Neural Likelihood: Fast Likelihood-free Inference with Autoregressive Flows

    Papamakarios, G., C, D. & Murray, I., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019. Naha, Okinawa, Japan: PMLR, Vol. 89. p. 837-848 12 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  7. Variational Noise-Contrastive Estimation

    Rhodes, B. & Gutmann, M., 25 Apr 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS 2019). Naha, Okinawa, Japan: PMLR, Vol. 89. p. 2741-2750 14 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  8. Inverting Supervised Representations with Autoregressive Neural Density Models

    Nash, C., Kushman, N. & Williams, C. K. I., 18 Apr 2019, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics. Lawrence, N. & Reid, M. (eds.). PMLR, Vol. 89. 10 p. (Proceedings of Machine Learning Research).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  9. Analysing discussion forum data: a replication study avoiding data contamination

    Farrow, E., Moore, J. & Gasevic, D., 4 Mar 2019, Proceedings of the 9th International Learning Analytics & Knowledge Conference (LAK-19). Tempe, Arizona, USA: ACM, p. 170-179 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  10. Probabilistic Programming with Densities in SlicStan: Efficient, Flexible, and Deterministic

    Gorinova, M. I., Gordon, A. D. & Sutton, C., 2 Jan 2019, In : Proceedings of the ACM on Programming Languages (PACMPL). 3, POPL, p. 35:1-35:30 30 p., 35.

    Research output: Contribution to journalArticle

  11. EXOTica: An Extensible Optimization Toolset for Prototyping and Benchmarking Motion Planning and Control

    Ivan, V., Yang, Y., Merkt, W., Camilleri, M. P. & Vijayakumar, S., 2019, Robot Operating System (ROS): The Complete Reference (Volume 3). Koubaa, A. (ed.). Cham: Springer International Publishing, p. 211-240 30 p.

    Research output: Chapter in Book/Report/Conference proceedingChapter

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