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Jordan T. Ash
Jordan T. Ash
Microsoft Research NYC
Adresse e-mail validée de princeton.edu - Page d'accueil
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Année
Deep batch active learning by diverse, uncertain gradient lower bounds
JT Ash, C Zhang, A Krishnamurthy, J Langford, A Agarwal
International Conference on Learning Representations, 2020
2812020
Learning deep resnet blocks sequentially using boosting theory
F Huang, JT Ash, J Langford, R Schapire
International Conference on Machine Learning, 2017
822017
On warm-starting neural network training
JT Ash, RP Adams
Neural Information Processing Systems, 2020
54*2020
Automated particle picking for low-contrast macromolecules in cryo-electron microscopy
R Langlois, J Pallesen, JT Ash, DN Ho, JL Rubinstein, J Frank
Journal of structural biology 186 (1), 1-7, 2014
532014
Joint analysis of gene expression levels and histological images identifies genes associated with tissue morphology
JT Ash, G Darnell, D Munro, B Engelhardt
Nature Communications, 458711, 2021
44*2021
A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation
T Xue, A Beatson, M Chiaramonte, G Roeder, JT Ash, Y Menguc, ...
Soft matter 16 (32), 7524-7534, 2020
212020
End-to-end training of deep probabilistic CCA on paired biomedical observations
G Gundersen, B Dumitrascu, JT Ash, BE Engelhardt
Uncertainty in Artificial Intelligence, 2020
192020
Learning Composable Energy Surrogates for PDE Order Reduction
A Beatson, JT Ash, G Roeder, T Xie, RP Adams
Neural Information Processing Systems, 2020
122020
Investigating the Role of Negatives in Contrastive Representation Learning
JT Ash, S Goel, A Krishnamurthy, D Misra
Artificial Intelligence and Statistics, 2022
112022
Understanding contrastive learning requires incorporating inductive biases
N Saunshi, J Ash, S Goel, D Misra, C Zhang, S Arora, S Kakade, ...
arXiv preprint arXiv:2202.14037, 2022
92022
Unsupervised domain adaptation using approximate label matching
JT Ash, RE Schapire, BE Engelhardt
ICML workshop on implicit generative models, 2017
72017
Scratchable devices: user-friendly programming for household appliances
J Ash, M Babes, G Cohen, S Jalal, S Lichtenberg, M Littman, V Marivate, ...
International Conference on Human-Computer Interaction, 137-146, 2011
72011
Gone Fishing: Neural Active Learning with Fisher Embeddings
JT Ash, S Goel, A Krishnamurthy, S Kakade
Neural Information Processing Systems, 2021
42021
Anti-Concentrated Confidence Bonuses for Scalable Exploration
JT Ash, C Zhang, S Goel, A Krishnamurthy, S Kakade
International Conference on Learning Representations, 2022
32022
Fully Automated Particle Selection and Verification in Single-Particle Cryo-EM
R Langlois, JT Ash, J Pallesen, J Frank
Computational Methods for Three-Dimensional Microscopy Reconstruction, 43-66, 2014
12014
Catastrophic Failures of Neural Active Learning on Heteroskedastic Distributions
S Khosla, A Lamb, JT Ash, C Zhang, K Kawaguchi
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
2021
Towards Flexible Active and Online Learning with Neural Networks
JT Ash
Princeton University, 2020
2020
A Novel Domain Adaptation Solution to the Transductive Transfer Learning Problem
JT Ash
Princeton University, 2015
2015
Familiarity Dominates Shape-From-Motion Signals in the Concave-to-Convex 3D illusion
J Ash, J Ravaliya, J Hughes, B Keane, A Jain, Q Zaidi, T Papathomas
Journal of Vision 12 (9), 1196-1196, 2012
2012
Role of the cognitive influence of familiarity in processing kinetic-depth-effect signals
JT Ash, JM Hughes, TV Papathomas
2012 38th Annual Northeast Bioengineering Conference (NEBEC), 183-184, 2012
2012
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