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09/21/2023
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of ...
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07/05/2023
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Graph Neural Networks (GNNs) play a fundamental role in many deep learni...
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04/11/2023
r-softmax: Generalized Softmax with Controllable Sparsity Rate
Nowadays artificial neural network models achieve remarkable results in ...
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04/07/2023
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets
Deep learning has achieved impressive performance in many domains, such ...
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03/03/2023
Contrastive Hierarchical Clustering
Deep clustering has been dominated by flat models, which split a dataset...
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06/28/2022
SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier
Deep neural networks present impressive performance, yet they cannot rel...
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06/28/2022
Hebbian Continual Representation Learning
Continual Learning aims to bring machine learning into a more realistic ...
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12/17/2021
Semi-Supervised Clustering via Markov Chain Aggregation
We connect the problem of semi-supervised clustering to constrained Mark...
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10/26/2021
MisConv: Convolutional Neural Networks for Missing Data
Processing of missing data by modern neural networks, such as CNNs, rema...
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10/04/2021
Pharmacoprint – a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
Structural fingerprints and pharmacophore modeling are methodologies tha...
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09/18/2021
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Modern generative models achieve excellent quality in a variety of tasks...
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08/10/2021
Flow-based SVDD for anomaly detection
We propose FlowSVDD – a flow-based one-class classifier for anomaly/outl...
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07/28/2021
SONG: Self-Organizing Neural Graphs
Recent years have seen a surge in research on deep interpretable neural ...
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06/09/2021
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
The problem of reducing processing time of large deep learning models is...
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11/30/2020
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamen...
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10/26/2020
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete i...
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10/06/2020
Flow-based anomaly detection
We propose OneFlow - a flow-based one-class classifier for anomaly (outl...
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10/05/2020
Estimating conditional density of missing values using deep Gaussian mixture model
We consider the problem of estimating the conditional probability distri...
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06/17/2020
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data ...
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01/18/2020
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
In this paper, we introduce a neural network framework for semi-supervis...
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10/07/2019
Biologically-Inspired Spatial Neural Networks
We introduce bio-inspired artificial neural networks consisting of neuro...
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09/11/2019
Geometric Graph Convolutional Neural Networks
Graph Convolutional Networks (GCNs) have recently become the primary cho...
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06/21/2019
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
We propose a semi-supervised generative model, SeGMA, which learns a joi...
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06/03/2019
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
We present an efficient technique, which allows to train classification ...
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02/27/2019
Multi-task hypernetworks
Hypernetworks mechanism allows to generate and train neural networks (ta...
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10/03/2018
Deep processing of structured data
We construct a general unified framework for learning representation of ...
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05/18/2018
Processing of missing data by neural networks
We propose a general, theoretically justified mechanism for processing m...
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03/11/2018
Cascade context encoder for improved inpainting
In this paper, we analyze if cascade usage of the context encoder with i...
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07/11/2017
Efficient mixture model for clustering of sparse high dimensional binary data
In this paper we propose a mixture model, SparseMix, for clustering of s...
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05/04/2017
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized dat...
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05/03/2017
Semi-supervised cross-entropy clustering with information bottleneck constraint
In this paper, we propose a semi-supervised clustering method, CEC-IB, t...
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08/19/2015
