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MIT AI Accelerator
MIT AI Accelerator
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Title
Cited by
Cited by
Year
Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach
A Fallah, A Mokhtari, A Ozdaglar
Advances in neural information processing systems 33, 3557-3568, 2020
8112020
The computational limits of deep learning
NC Thompson, K Greenewald, K Lee, GF Manso
arXiv preprint arXiv:2007.05558 10, 2020
6242020
Personalized federated learning: A meta-learning approach
A Fallah, A Mokhtari, A Ozdaglar
arXiv preprint arXiv:2002.07948, 2020
5892020
On the convergence theory of gradient-based model-agnostic meta-learning algorithms
A Fallah, A Mokhtari, A Ozdaglar
International Conference on Artificial Intelligence and Statistics, 1082-1092, 2020
2362020
Is conditional generative modeling all you need for decision-making?
A Ajay, Y Du, A Gupta, J Tenenbaum, T Jaakkola, P Agrawal
arXiv preprint arXiv:2211.15657, 2022
2312022
Relative uncertainty learning for facial expression recognition
Y Zhang, C Wang, W Deng
Advances in Neural Information Processing Systems 34, 17616-17627, 2021
220*2021
Neural circuit policies enabling auditable autonomy
M Lechner, R Hasani, A Amini, TA Henzinger, D Rus, R Grosu
Nature Machine Intelligence 2 (10), 642-652, 2020
2072020
Bao: Making learned query optimization practical
R Marcus, P Negi, H Mao, N Tatbul, M Alizadeh, T Kraska
Proceedings of the 2021 International Conference on Management of Data, 1275 …, 2021
1912021
Integration of neural network-based symbolic regression in deep learning for scientific discovery
S Kim, PY Lu, S Mukherjee, M Gilbert, L Jing, V Čeperić, M Soljačić
IEEE transactions on neural networks and learning systems 32 (9), 4166-4177, 2020
1662020
Breeds: Benchmarks for subpopulation shift
S Santurkar, D Tsipras, A Madry
arXiv preprint arXiv:2008.04859, 2020
1642020
From imagenet to image classification: Contextualizing progress on benchmarks
D Tsipras, S Santurkar, L Engstrom, A Ilyas, A Madry
International Conference on Machine Learning, 9625-9635, 2020
1502020
Rapid locomotion via reinforcement learning
GB Margolis, G Yang, K Paigwar, T Chen, P Agrawal
The International Journal of Robotics Research 43 (4), 572-587, 2024
1422024
Hydra: A real-time spatial perception system for 3D scene graph construction and optimization
N Hughes, Y Chang, L Carlone
arXiv preprint arXiv:2201.13360, 2022
1382022
Tsunami: A learned multi-dimensional index for correlated data and skewed workloads
J Ding, V Nathan, M Alizadeh, T Kraska
arXiv preprint arXiv:2006.13282, 2020
1342020
Beyond expertise and roles: A framework to characterize the stakeholders of interpretable machine learning and their needs
H Suresh, SR Gomez, KK Nam, A Satyanarayan
Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems …, 2021
1192021
Equivariant contrastive learning
R Dangovski, L Jing, C Loh, S Han, A Srivastava, B Cheung, P Agrawal, ...
arXiv preprint arXiv:2111.00899, 2021
1142021
Use of neural networks for stable, accurate and physically consistent parameterization of subgrid atmospheric processes with good performance at reduced precision
J Yuval, PA O'Gorman, CN Hill
Geophysical Research Letters 48 (6), e2020GL091363, 2021
1102021
Generative models as a data source for multiview representation learning
A Jahanian, X Puig, Y Tian, P Isola
arXiv preprint arXiv:2106.05258, 2021
1072021
Do GANs always have Nash equilibria?
F Farnia, A Ozdaglar
International Conference on Machine Learning, 3029-3039, 2020
1042020
The low-rank simplicity bias in deep networks
M Huh, H Mobahi, R Zhang, B Cheung, P Agrawal, P Isola
arXiv preprint arXiv:2103.10427, 2021
962021
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