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Ivan Kobyzev
Ivan Kobyzev
ML Researcher, Borealis AI
Verified email at uwaterloo.ca
Title
Cited by
Cited by
Year
Normalizing flows: An introduction and review of current methods
I Kobyzev, SJD Prince, MA Brubaker
IEEE transactions on pattern analysis and machine intelligence 43 (11), 3964 …, 2020
4432020
Representation Learning for Dynamic Graphs: A Survey
SM Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, P Poupart
Journal of Machine Learning Research 21 (70), 1-73, 2020
176*2020
Normalizing flows: Introduction and ideas
I Kobyzev, S Prince, MA Brubaker
arXiv preprint arXiv:1908.09257, 1551-3203, 2019
492019
Tails of lipschitz triangular flows
P Jaini, I Kobyzev, Y Yu, M Brubaker
International Conference on Machine Learning, 4673-4681, 2020
20*2020
Polarized-vae: Proximity based disentangled representation learning for text generation
V Balasubramanian, I Kobyzev, H Bahuleyan, I Shapiro, O Vechtomova
arXiv preprint arXiv:2004.10809, 2020
132020
A categorical approach to cyclic cohomology of quasi-Hopf algebras and Hopf algebroids
I Kobyzev, I Shapiro
Applied Categorical Structures 27 (1), 85-109, 2019
122019
Generating emotionally aligned responses in dialogues using affect control theory
N Asghar, I Kobyzev, J Hoey, P Poupart, MB Sheikh
arXiv preprint arXiv:2003.03645, 2020
92020
Normalizing Flows: An Introduction and Review of Current Methods, arXiv e-prints
I Kobyzev, SJD Prince, MA Brubaker
arXiv preprint arXiv:1908.09257, 2019
62019
Anti-Yetter-Drinfeld modules for quasi-Hopf algebras
I Kobyzev, I Shapiro
Symmetry, Integrability and Geometry: Methods and Applications (SIGMA) 14 (098), 2018
62018
Normalizing Flows: An Introduction and Review of Current Methods, arXiv e-prints (Aug., 2019)
I Kobyzev, SJD Prince, MA Brubaker
arXiv preprint arXiv:1908.09257, 1908
41908
A semi-holographic hyperdimensional representation system for hardware-friendly cognitive computing
A Serb, I Kobyzev, J Wang, T Prodromakis
Philosophical Transactions of the Royal Society A 378 (2164), 20190162, 2020
22020
G-theory of root stacks and equivariant K-theory
A Dhillon, I Kobyzev
Annals of K-Theory 4 (2), 151-183, 2019
22019
Learning Functions on Multiple Sets using Multi-Set Transformers
KA Selby, A Rashid, I Kobyzev, M Rezagholizadeh, P Poupart
The 38th Conference on Uncertainty in Artificial Intelligence, 2022
12022
A Short Study on Compressing Decoder-Based Language Models
T Li, YE Mesbahi, I Kobyzev, A Rashid, A Mahmud, N Anchuri, ...
arXiv preprint arXiv:2110.08460, 2021
12021
Equivariant Discrete Normalizing Flows
AJ Bose, I Kobyzev
arXiv preprint arXiv:2110.08649, 2021
12021
Computer-implemented method for creating encoded data
S Alexander, J Wang, I Kobyzev
US Patent App. 17/613,979, 2022
2022
Towards Understanding Label Regularization for Fine-tuning Pre-trained Language Models
I Kobyzev, A Jafari, M Rezagholizadeh, T Li, A Do-Omri, P Lu, A Ghodsi, ...
arXiv preprint arXiv:2205.12428, 2022
2022
Pseudo Knowledge Distillation: Towards Learning Optimal Instance-specific Label Smoothing Regularization
P Lu, A Rashid, I Kobyzev, M Rezagholizadeh, P Langlais
2021
Representation Learning for Dynamic Graphs: A Survey
S Mehran Kazemi, R Goel, K Jain, I Kobyzev, A Sethi, P Forsyth, ...
arXiv e-prints, arXiv: 1905.11485, 2019
2019
A JOURNAL OF THE K-THEORY FOUNDATION
A Dhillon, I Kobyzev
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Articles 1–20