Search Results for author:
Found 9 papers, 3 papers with code
Syntactic Transfer to Kyrgyz Using the Treebank Translation Method
The Kyrgyz language, as a low-resource language, requires significant effort to create high-quality syntactic corpora.
Benchmarking Multilabel Topic Classification in the Kyrgyz Language
Kyrgyz is a very underrepresented language in terms of modern natural language processing resources.
RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
Recent research has shown the advantages of using autoencoders based on deep neural networks for collaborative filtering.
Ranked #1 on
Recommendation Systems
on MovieLens 20M
(Recall@50 metric)
Synthetic Data for Deep Learning
Synthetic data is an increasingly popular tool for training deep learning models, especially in computer vision but also in other areas.
AspeRa: Aspect-based Rating Prediction Model
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items and at the same time discovers coherent aspects of reviews that can be used to explain predictions or profile users.
Adapting Convolutional Neural Networks for Geographical Domain Shift
We present the winning solution for the Inclusive Images Competition organized as part of the Conference on Neural Information Processing Systems (NeurIPS 2018) Competition Track.
Learning State Representations in Complex Systems with Multimodal Data
Representation learning becomes especially important for complex systems with multimodal data sources such as cameras or sensors.
SEIGAN: Towards Compositional Image Generation by Simultaneously Learning to Segment, Enhance, and Inpaint
We present a novel approach to image manipulation and understanding by simultaneously learning to segment object masks, paste objects to another background image, and remove them from original images.
Label Denoising with Large Ensembles of Heterogeneous Neural Networks
Despite recent advances in computer vision based on various convolutional architectures, video understanding remains an important challenge.
