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Maja Rudolph
Maja Rudolph
Senior Research Scientist, Bosch Center for AI
Geverifieerd e-mailadres voor cs.columbia.edu - Homepage
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Edward: A library for probabilistic modeling, inference, and criticism
D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei
arXiv preprint arXiv:1610.09787, 2016
3232016
Dynamic embeddings for language evolution
M Rudolph, D Blei
Proceedings of the 2018 World Wide Web Conference, 1003-1011, 2018
1372018
Exponential family embeddings
M Rudolph, F Ruiz, S Mandt, D Blei
Neural Information Processing Systems, 2016
1352016
Structured embedding models for grouped data
M Rudolph, F Ruiz, S Athey, D Blei
Neural Information Processing Systems, 2017
462017
Dynamic Bernoulli embeddings for language evolution
M Rudolph, D Blei
arXiv preprint arXiv:1703.08052, 2017
352017
Extending machine language models toward human-level language understanding
JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze
arXiv preprint arXiv:1912.05877, 2019
312019
Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models
JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze
Proceedings of the National Academy of Sciences 117 (42), 25966-25974, 2020
302020
Neural Transformation Learning for Deep Anomaly Detection Beyond Images
C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph
ICML 2021, 2021
182021
Objective variables for probabilistic revenue maximization in second-price auctions with reserve
MR Rudolph, JG Ellis, DM Blei
Proceedings of the 25th International Conference on World Wide Web, 1113-1122, 2016
182016
Edward: A library for probabilistic modeling, inference, and criticism. arXiv 2016
D Tran, A Kucukelbir, AB Dieng, M Rudolph, D Liang, DM Blei
arXiv preprint arXiv:1610.09787, 2016
82016
A joint model for who-to-follow and what-to-view recommendations on behance
MR Rudolph, M Hoffman, A Hertzmann
Proceedings of the 25th International Conference Companion on World Wide Web …, 2016
32016
Complex-Valued Autoencoders for Object Discovery
S Löwe, P Lippe, M Rudolph, M Welling
arXiv preprint arXiv:2204.02075, 2022
22022
Latent Outlier Exposure for Anomaly Detection with Contaminated Data
C Qiu, A Li, M Kloft, M Rudolph, S Mandt
arXiv preprint arXiv:2202.08088, 2022
22022
Modeling irregular time series with continuous recurrent units
M Schirmer, M Eltayeb, S Lessmann, M Rudolph
International Conference on Machine Learning, 19388-19405, 2022
12022
Detecting Anomalies within Time Series using Local Neural Transformations
T Schneider, C Qiu, M Kloft, DA Latif, S Staab, S Mandt, M Rudolph
arXiv preprint arXiv:2202.03944, 2022
12022
Variational dynamic mixtures
C Qiu, S Mandt, M Rudolph
arXiv preprint arXiv:2010.10403, 2020
12020
Deterministic Inference of Neural Stochastic Differential Equations.
A Look, C Qiu, M Rudolph, J Peters, M Kandemir
arXiv preprint arXiv:2006.08973, 2020
12020
Machine learned anomaly detection
C Qiu, MR Rudolph, T Pfrommer
US Patent App. 17/651,917, 2022
2022
Data driven recognition of anomalies and continuation of sensor data
T Bu, C Qiu, MR Rudolph
US Patent App. 17/570,542, 2022
2022
Raising the Bar in Graph-level Anomaly Detection
C Qiu, M Kloft, S Mandt, M Rudolph
arXiv preprint arXiv:2205.13845, 2022
2022
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