Luca Weihs
Luca Weihs
Research Scientist, Allen Institute for Artificial Intelligence
Vahvistettu sähköpostiosoite verkkotunnuksessa
Ai2-thor: An interactive 3d environment for visual ai
E Kolve, R Mottaghi, W Han, E VanderBilt, L Weihs, A Herrasti, D Gordon, ...
arXiv preprint arXiv:1712.05474, 2019
Generic identifiability of linear structural equation models by ancestor decomposition
M Drton, L Weihs
Scandinavian Journal of Statistics 43 (4), 1035-1045, 2016
Symmetric rank covariances: a generalized framework for nonparametric measures of dependence
L Weihs, M Drton, N Meinshausen
Biometrika 105 (3), 547-562, 2018
Efficient computation of the Bergsma–Dassios sign covariance
L Weihs, M Drton, D Leung
Computational Statistics 31 (1), 315-328, 2016
Learning to predict citation-based impact measures
L Weihs, O Etzioni
2017 ACM/IEEE joint conference on digital libraries (JCDL), 1-10, 2017
Large-sample theory for the Bergsma-Dassios sign covariance
P Nandy, L Weihs, M Drton
Electronic Journal of Statistics 10 (2), 2287-2311, 2016
Marginal likelihood and model selection for Gaussian latent tree and forest models
M Drton, S Lin, L Weihs, P Zwiernik
Bernoulli 23 (2), 1202-1232, 2017
Two body problem: Collaborative visual task completion
U Jain, L Weihs, E Kolve, M Rastegari, S Lazebnik, A Farhadi, ...
Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2019
RoboTHOR: An Open Simulation-to-Real Embodied AI Platform
M Deitke, W Han, A Herrasti, A Kembhavi, E Kolve, R Mottaghi, J Salvador, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Gender trends in computer science authorship
LL Wang, G Stanovsky, L Weihs, O Etzioni
arXiv preprint arXiv:1906.07883, 2019
Nested covariance determinants and restricted trek separation in Gaussian graphical models
M Drton, E Robeva, L Weihs
arXiv preprint arXiv:1807.07561, 2018
A cordial sync: Going beyond marginal policies for multi-agent embodied tasks
U Jain, L Weihs, E Kolve, A Farhadi, S Lazebnik, A Kembhavi, A Schwing
European Conference on Computer Vision, 471-490, 2020
Determinantal generalizations of instrumental variables
L Weihs, B Robinson, E Dufresne, J Kenkel, KKR McGee II, MGII Reginald, ...
Journal of Causal Inference 6 (1), 2018
sBIC: computing the singular BIC for multiple models
L Weihs, M Plummer
R Package Version 0.2, 2016
TauStar: Efficient computation of the t* statistic of Bergsma and Dassios (2014), 2015
L Weihs
URL http://CRAN. R-project. org/package= TauStar. R package version 1 (0), 0
Allenact: A framework for embodied ai research
L Weihs, J Salvador, K Kotar, U Jain, KH Zeng, R Mottaghi, A Kembhavi
arXiv preprint arXiv:2008.12760, 2020
Visual Reaction: Learning to Play Catch with Your Drone
KH Zeng, R Mottaghi, L Weihs, A Farhadi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Semid: Identifiability of linear structural equation models, 2015
RF Barber, M Drton, L Weihs
URL https://CRAN. R-project. org/package= SEMID. R package version 0.3 1, 0
Bridging the imitation gap by adaptive insubordination
L Weihs, U Jain, J Salvador, S Lazebnik, A Kembhavi, A Schwing
arXiv preprint arXiv:2007.12173, 2020
Grounded Situation Recognition
S Pratt, M Yatskar, L Weihs, A Farhadi, A Kembhavi
arXiv preprint arXiv:2003.12058, 2020
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Artikkelit 1–20