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Victor Prokhorov
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On the Importance of the Kullback-Leibler Divergence Term in Variational Autoencoders for Text Generation
V Prokhorov, E Shareghi, Y Li, MT Pilehvar, N Collier
3rd Workshop on Neural Generation and Translation (WNGT 2019), 10, 2019
432019
Card-660: A Reliable Evaluation Framework for Rare Word Representation Models.
MT Pilehvar, D Kartsaklis, V Prokhorov, N Collier
EMNLP, 1391-1401, 2018
43*2018
Faster-than-real-time linear lane detection implementation using soc dsp tms320c6678
R Fan, V Prokhorov, N Dahnoun
2016 IEEE International Conference on Imaging Systems and Techniques (IST …, 2016
322016
Unseen word representation by aligning heterogeneous lexical semantic spaces
V Prokhorov, MT Pilehvar, D Kartsaklis, P Lio, N Collier
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 6900-6907, 2019
25*2019
Generating Knowledge Graph Paths from Textual Definitions using Sequence-to-Sequence Models
V Prokhorov, MT Pilehvar, N Collier
NAACL 2019, 2019
112019
Learning Sparse Sentence Encoding without Supervision: An Exploration of Sparsity in Variational Autoencoders
V Prokhorov, Y Li, E Shareghi, N Collier
arXiv preprint arXiv:2009.12421, 2020
6*2020
Unsupervised representation disentanglement of text: An evaluation on synthetic datasets
L Zhang, V Prokhorov, E Shareghi
arXiv preprint arXiv:2106.03631, 2021
52021
StrAE: Autoencoding for Pre-Trained Embeddings using Explicit Structure
M Opper, V Prokhorov, N Siddharth
arXiv preprint arXiv:2305.05588, 2023
22023
Autoencoding Conditional Neural Processes for Representation Learning
V Prokhorov, I Titov, N Siddharth
ICML 2024, 2023
2023
Injecting Inductive Biases into Distributed Representations of Text
V Prokhorov
University of Cambridge, 2021
2021
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Articles 1–10