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Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

A. Creswell, M. Shanahan, and I. Higgins. ICLR, OpenReview.net, (2023)

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Michael Higgins

Dorothee Renate Higgins

Irina Bavykina

Investigation of ZnWO_1tn4 and CaMoO_1tn4 as target materials for the CRESST-II dark matter searchI. Bavykina. Uni München, (2009)

Irina Gillwald

Irina Prokofieva

Other publications of authors with the same name

SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision.I. Higgins, P. Wirnsberger, A. Jaegle, and A. Botev. NeurIPS, page 25591-25605. (2021)Life-Long Disentangled Representation Learning with Cross-Domain Latent Homologies.A. Achille, T. Eccles, L. Matthey, C. Burgess, N. Watters, A. Lerchner, and I. Higgins. NeurIPS, page 9895-9905. (2018)Representation Matters: Improving Perception and Exploration for Robotics.M. Wulfmeier, A. Byravan, T. Hertweck, I. Higgins, A. Gupta, T. Kulkarni, M. Reynolds, D. Teplyashin, R. Hafner, T. Lampe and 1 other author(s). CoRR, (2020)Representation learning for improved interpretability and classification accuracy of clinical factors from EEG.G. Honke, I. Higgins, N. Thigpen, V. Miskovic, K. Link, S. Duan, P. Gupta, J. Klawohn, and G. Hajcak. ICLR, OpenReview.net, (2021)Disentangling by Subspace Diffusion.D. Pfau, I. Higgins, A. Botev, and S. Racanière. NeurIPS, (2020)Unsupervised Model Selection for Variational Disentangled Representation Learning.S. Duan, L. Matthey, A. Saraiva, N. Watters, C. Burgess, A. Lerchner, and I. Higgins. ICLR, OpenReview.net, (2020)Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.A. Creswell, M. Shanahan, and I. Higgins. ICLR, OpenReview.net, (2023)Hamiltonian Generative Networks.P. Toth, D. Rezende, A. Jaegle, S. Racanière, A. Botev, and I. Higgins. ICLR, OpenReview.net, (2020)DARLA: Improving Zero-Shot Transfer in Reinforcement Learning.I. Higgins, A. Pal, A. Rusu, L. Matthey, C. Burgess, A. Pritzel, M. Botvinick, C. Blundell, and A. Lerchner. ICML, volume 70 of Proceedings of Machine Learning Research, page 1480-1490. PMLR, (2017)SCAN: Learning Hierarchical Compositional Visual Concepts.I. Higgins, N. Sonnerat, L. Matthey, A. Pal, C. Burgess, M. Bosnjak, M. Shanahan, M. Botvinick, D. Hassabis, and A. Lerchner. ICLR (Poster), OpenReview.net, (2018)

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Disambiguation of "Higgins, Irina"

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Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

Please choose a person to relate this publication to

Michael Higgins

Dorothee Renate Higgins

Irina Bavykina

Irina Gillwald

Irina Prokofieva

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Higgins, Irina"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.

Please choose a person to relate this publication to

Michael Higgins

Dorothee Renate Higgins

Irina Bavykina

Irina Gillwald

Irina Prokofieva

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoning.