Search Results for author:
Found 36 papers, 13 papers with code
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of face images, such as expression, hairstyle, pose, or age.
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Graph Neural Networks (GNNs) play a fundamental role in many deep learning problems, in particular in cheminformatics.
Augmentation-aware Self-supervised Learning with Guided Projector
By learning to remain invariant to applied data augmentations, methods such as SimCLR and MoCo are able to reach quality on par with supervised approaches.
r-softmax: Generalized Softmax with Controllable Sparsity Rate
Nowadays artificial neural network models achieve remarkable results in many disciplines.
HyperTab: Hypernetwork Approach for Deep Learning on Small Tabular Datasets
We evaluated HyperTab on more than 40 tabular datasets of a varying number of samples and domains of origin, and compared its performance with shallow and deep learning models representing the current state-of-the-art.
Contrastive Hierarchical Clustering
Deep clustering has been dominated by flat models, which split a dataset into a predefined number of groups.
Ranked #1 on
Image Clustering
on MNIST
SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier
The proposed classifier is confident only if a single class has a high probability and other probabilities are negligible.
Hebbian Continual Representation Learning
Although this setting is natural for biological systems, it proves very difficult for machine learning models such as artificial neural networks.
MisConv: Convolutional Neural Networks for Missing Data
Processing of missing data by modern neural networks, such as CNNs, remains a fundamental, yet unsolved challenge, which naturally arises in many practical applications, like image inpainting or autonomous vehicles and robots.
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
Structural fingerprints and pharmacophore modeling are methodologies that have been used for at least two decades in various fields of cheminformatics: from similarity searching to machine learning (ML).
PluGeN: Multi-Label Conditional Generation From Pre-Trained Models
Modern generative models achieve excellent quality in a variety of tasks including image or text generation and chemical molecule modeling.
Flow-based SVDD for anomaly detection
We propose FlowSVDD -- a flow-based one-class classifier for anomaly/outliers detection that realizes a well-known SVDD principle using deep learning tools.
SONG: Self-Organizing Neural Graphs
Recent years have seen a surge in research on deep interpretable neural networks with decision trees as one of the most commonly incorporated tools.
Zero Time Waste: Recycling Predictions in Early Exit Neural Networks
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications.
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamental, yet unsolved challenge of intelligence, especially in the scope of complex and non-deterministic scenarios, such as modeling behavior of humans.
Processing of incomplete images by (graph) convolutional neural networks
We investigate the problem of training neural networks from incomplete images without replacing missing values.
OneFlow: One-class flow for anomaly detection based on a minimal volume region
We propose OneFlow - a flow-based one-class classifier for anomaly (outlier) detection that finds a minimal volume bounding region.
Estimating conditional density of missing values using deep Gaussian mixture model
We consider the problem of estimating the conditional probability distribution of missing values given the observed ones.
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data representations taken from the hidden layers of the target network.
Can auto-encoders help with filling missing data?
This paper introduces an approach to filling in missing data based on deep auto-encoder models, adequate to high-dimensional data exhibiting complex dependencies, such as images.
A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints
In this paper, we introduce a neural network framework for semi-supervised clustering (SSC) with pairwise (must-link or cannot-link) constraints.
Biologically-Inspired Spatial Neural Networks
We introduce bio-inspired artificial neural networks consisting of neurons that are additionally characterized by spatial positions.
Spatial Graph Convolutional Networks
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds.
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
We propose a semi-supervised generative model, SeGMA, which learns a joint probability distribution of data and their classes and which is implemented in a typical Wasserstein auto-encoder framework.
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
We present an efficient technique, which allows to train classification networks which are verifiably robust against norm-bounded adversarial attacks.
Hypernetwork functional image representation
Motivated by the human way of memorizing images we introduce their functional representation, where an image is represented by a neural network.
Set Aggregation Network as a Trainable Pooling Layer
Global pooling, such as max- or sum-pooling, is one of the key ingredients in deep neural networks used for processing images, texts, graphs and other types of structured data.
Deep processing of structured data
We construct a general unified framework for learning representation of structured data, i. e. data which cannot be represented as the fixed-length vectors (e. g. sets, graphs, texts or images of varying sizes).
Cascade context encoder for improved inpainting
For this purpose, we train context encoder for 64x64 pixels images in a standard way and use its resized output to fill in the missing input region of the 128x128 context encoder, both in training and evaluation phase.
Efficient mixture model for clustering of sparse high dimensional binary data
In this paper we propose a mixture model, SparseMix, for clustering of sparse high dimensional binary data, which connects model-based with centroid-based clustering.
Spherical Wards clustering and generalized Voronoi diagrams
In order to graphically represent and interpret the results the notion of Voronoi diagram was generalized to non Euclidean spaces and applied for introduced clustering method.
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data sets.
Semi-supervised cross-entropy clustering with information bottleneck constraint
By combining the ideas from cross-entropy clustering (CEC) with those from the information bottleneck method (IB), our method trades between three conflicting goals: the accuracy with which the data set is modeled, the simplicity of the model, and the consistency of the clustering with side information.
Pointed subspace approach to incomplete data
Incomplete data are often represented as vectors with filled missing attributes joined with flag vectors indicating missing components.
Generalized RBF kernel for incomplete data
We construct $\bf genRBF$ kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data.
Introduction to Cross-Entropy Clustering The R Package CEC
The R Package CEC performs clustering based on the cross-entropy clustering (CEC) method, which was recently developed with the use of information theory.
