Search Results for author:
Found 26 papers, 10 papers with code
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
We introduce ProtoSeg, a novel model for interpretable semantic image segmentation, which constructs its predictions using similar patches from the training set.
ProPaLL: Probabilistic Partial Label Learning
Partial label learning is a type of weakly supervised learning, where each training instance corresponds to a set of candidate labels, among which only one is true.
Bounding Evidence and Estimating Log-Likelihood in VAE
Many crucial problems in deep learning and statistics are caused by a variational gap, i. e., a difference between evidence and evidence lower bound (ELBO).
Interpretable Image Classification with Differentiable Prototypes Assignment
We introduce ProtoPool, an interpretable image classification model with a pool of prototypes shared by the classes.
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.
Efficient GPU implementation of randomized SVD and its applications
In this work, we leverage efficient processing operations that can be run in parallel on modern Graphical Processing Units (GPUs), predominant computing architecture used e. g. in deep learning, to reduce the computational burden of computing matrix decompositions.
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).
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.
HyperPocket: Generative Point Cloud Completion
In this work, we reformulate the problem of point cloud completion into an object hallucination task.
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery
In this paper, we introduce ProtoPShare, a self-explained method that incorporates the paradigm of prototypical parts to explain its predictions.
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.
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.
LocoGAN -- Locally Convolutional GAN
In the paper we construct a fully convolutional GAN model: LocoGAN, which latent space is given by noise-like images of possibly different resolutions.
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.
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.
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models
In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space.
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).
Processing of missing data by neural networks
We propose a general, theoretically justified mechanism for processing missing data by neural networks.
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.
Semi-supervised model-based clustering with controlled clusters leakage
In this paper, we focus on finding clusters in partially categorized data sets.
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.
