Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Popov S., Morozov S., Babenko A.
, , et al., Journal of Industrial Information Integration 2021 Vol. 23 Article 100216
Automated early process fault detection and prediction remains a challenging problem in industrial processes. Traditionally it has been done by multivariate statistical analysis of sensor readings and, more recently, with the help of machine learning methods. The quality of machine learning models strongly depends on feature engineering, that in turn heavily relies on expertise of ...
Added: March 21, 2021
The Russian Drug Reaction Corpus and Neural Models for Drug Reactions and Effectiveness Detection in User Reviews
, , et al., Bioinformatics 2021 Vol. 37 No. 2 P. 243-249
Drugs and diseases play a central role in many areas of biomedical research and healthcare. Aggregating knowledge about these entities across a broader range of domains and languages is critical for information extraction (IE) applications. To facilitate text mining methods for analysis and comparison of patient’s health conditions and adverse drug reactions reported on the ...
Added: January 13, 2021
, , et al., Frontiers in Genetics 2021 Article 638191
We propose a method for generating an electrocardiogram (ECG) signal for one cardiac cycle using a variational autoencoder. Our goal was to encode the original ECG signal using as few features as possible. Using this method we extracted a vector of new 25 features, which in many cases can be interpreted. The generated ECG has ...
Added: October 29, 2021
, , , in : Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers. Communications in Computer and Information Science. Vol. 1086.: Springer, 2020. P. 154-159.
In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labeled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler ...
Added: November 7, 2019
, , et al., , in : Workshop of the 5th International Conference on Learning Representations (ICLR). : [б.и.], 2017. P. 1-4.
In this paper, we propose a new feature extraction technique for program execution logs. First, we automatically extract complex patterns from a program's behavior graph. Then, we embed these patterns into a continuous space by training an autoencoder. We evaluate the proposed features on a real-world malicious software detection task. We also find that the ...
Added: October 31, 2018
Интерфейс мозг-компьютер: опыт построения, использования и возможные пути повышения рабочих характеристик
, , et al., Журнал высшей нервной деятельности им. И.П. Павлова 2017 Т. 67 № 4 С. 504-520
Brain-computer interfaces find application in a number of different areas and have the potential to be used for research as well as for practical purposes. The clinical use of BCI includes current studies on neurorehabilitation ([Frolov et al., 2013; Ang et al., 2010]), and there is the prospect of using BCI to restore movement and ...
Added: October 19, 2017
, , , , in : European Conference on Visual Perception 2017 Abstract Book. : [б.и.], 2017. Ch. 2. P. 18-18.
Approximately twenty years ago, Laurent Itti and Christof Koch created a saliency map of visual attention in an attempt to recreate the work of biological pyramidal neurons by mimicking neurons with centre-surround receptive fields. The Saliency Model launched many studies that contributed to the understanding of layers of vision and the sphere of visual attention. ...
Added: October 15, 2018
, , et al., , in : The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations. : Springer, 2020. P. 295-315.
Added: February 20, 2021
, , , / International Conference on Machine Learning. Series 1 "Workshop on Learning to Generate Natural Language". 2017.
Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights. Recently proposed Sparse Variational Dropout (Molchanov et al., 2017) eliminates the majority of the weights in a feed-forward neural network without significant loss of quality. We apply this technique to sparsify recurrent neural ...
Added: October 19, 2017
, , , , in : Thirty-Sixth Conference on Neural Information Processing Systems : NeurIPS 2022. : Curran Associates, Inc., 2022. Ch. 1. P. 24991-25004.
Added: January 28, 2023
, , , in : The 2nd Workshop and Challenges for Out-of-Distribution Generalization in Computer Vision. ICCV 2023. : [б.и.], 2023.
Knowledge distillation (KD) is a powerful model compression technique broadly used in practical deep learning applications. It is focused on training a small student network to mimic a larger teacher network. While it is widely known that KD can offer an improvement to student generalization in i.i.d setting, its performance under domain shift, i.e. the ...
Added: November 20, 2023
, , et al., Trends in Cell Biology 2022
Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought ...
Added: January 21, 2022
, , et al., Life Science Alliance 2023 Vol. 6 No. 7 Article e202301962
Identifying roles for Z-DNA remains challenging given their dynamic nature. Here, we perform genome-wide interrogation with the DNABERT transformer algorithm trained on experimentally identified Z-DNA forming sequences (Z-flipons). The algorithm yields large performance enhancements (F1 = 0.83) over existing approaches and implements computational mutagenesis to assess the effects of base substitution on Z-DNA formation. We ...
Added: June 9, 2023
Traffic4cast at NeurIPS 2021 - Temporal and Spatial Few-Shot Transfer Learning in Gridded Geo-Spatial Processes
, , et al., , in : Proceedings of the NeurIPS 2021 Competitions and Demonstrations Track. : PMLR, 2022. P. 97-112.
Added: October 11, 2022
Speech decoding from a small set of spatially segregated minimally invasive intracranial EEG electrodes with a compact and interpretable neural network
, , et al., Journal of Neural Engineering 2022 Vol. 19 No. 6 Article 066016
Objective. Speech decoding, one of the most intriguing brain-computer interface applications, opens up plentiful opportunities from rehabilitation of patients to direct and seamless communication between human species. Typical solutions rely on invasive recordings with a large number of distributed electrodes implanted through craniotomy. Here we explored the possibility of creating speech prosthesis in a minimally ...
Added: December 9, 2022
, , et al., , in : Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. Third International Workshop, MLCN 2020, and Second International Workshop, RNO-AI 2020. Lecture Notes in Computer Science. Vol. 12449: Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology.: Springer, 2020. Ch. 5. P. 45-55.
Electroencephalography (EEG) is a well-established non-invasive technique to measure the brain activity, albeit with a limited spatial resolution. Variations in electric conductivity between different tissues distort the electric fields generated by cortical sources, resulting in smeared potential measurements on the scalp. One needs to solve an ill-posed inverse problem to recover the original neural activity. In this article, ...
Added: December 10, 2020
, , , in : 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). : Seul : IEEE, 2020. P. 2800-2805.
Added: March 29, 2021
, , et al., , in : Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP 2020). Vol. 4.: SciTePress, 2020. P. 214-221.
We propose a novel multi-texture synthesis model based on generative adversarial networks (GANs) with a user-controllable mechanism. The user control ability allows to explicitly specify the texture which should be generated by the model. This property follows from using an encoder part which learns a latent representation for each texture from the dataset. To ensure ...
Added: November 8, 2020
The performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing field of representation learning is concerned with questions surrounding how we can best learn meaningful and useful representations of data. We take a broad view of the field and include ...
Added: October 31, 2018
, , et al., , in : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2019). : IEEE, 2019. P. 9601-9611.
We introduce ABC-Dataset, a collection of one million Computer-Aided Design (CAD) models for research of geometric deep learning methods and applications. Each model is a collection of explicitly parametrized curves and surfaces, providing ground truth for differential quantities, patch segmentation, geometric feature detection, and shape reconstruction. Sampling the parametric descriptions of surfaces and curves allows ...
Added: November 26, 2019
, , et al., , in : Proceedings of the 7th International Conference on Learning Representations (ICLR 2019). : ICLR, 2019. P. 1-17.
Bayesian inference is known to provide a general framework for incorporating prior knowledge or specific properties into machine learning models via carefully choosing a prior distribution. In this work, we propose a new type of prior distributions for convolutional neural networks, deep weight prior (DWP), that exploit generative models to encourage a specific structure of ...
Added: September 2, 2019
, , et al., , in : Neural Fields across Fields: Methods and Applications of Implicit Neural Representations. ICLR 2023 Workshop. : [б.и.], 2023. Ch. 8.
In view synthesis, a neural radiance field approximates underlying density and radiance fields based on a sparse set of scene pictures. To generate a pixel of a novel view, it marches a ray through the pixel and computes a weighted sum of radiance emitted from a dense set of ray points. This rendering algorithm is ...
Added: July 18, 2023
, , , Siberian Journal of Life Sciences and Agriculture 2021 Т. 13 № 1 С. 144-155
Background. Development of a convolutional neural network model for detecting cassava diseases from a mobile phone photo. Materials and methods. The material for the research was taken images with various types of cassava diseases, published in open access of the Kaggle platform. Research methods: theory of design and development of information systems, programming, methods of augmentation and extension ...
Added: November 17, 2021
, , et al., Proceedings of Machine Learning Research 2020 P. 1-9
We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the simulator is stochastic. To address such cases, we introduce the use ...
Added: October 31, 2019