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08/16/2023
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
Prototypical parts-based networks are becoming increasingly popular due ...
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06/18/2023
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
Multiple Instance Learning (MIL) is a weakly-supervised problem in which...
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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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01/28/2023
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
We introduce ProtoSeg, a novel model for interpretable semantic image se...
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08/21/2022
ProPaLL: Probabilistic Partial Label Learning
Partial label learning is a type of weakly supervised learning, where ea...
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06/19/2022
Bounding Evidence and Estimating Log-Likelihood in VAE
Many crucial problems in deep learning and statistics are caused by a va...
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12/06/2021
Interpretable Image Classification with Differentiable Prototypes Assignment
We introduce ProtoPool, an interpretable image classification model with...
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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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10/05/2021
Efficient GPU implementation of randomized SVD and its applications
Matrix decompositions are ubiquitous in machine learning, including appl...
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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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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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02/11/2021
HyperPocket: Generative Point Cloud Completion
Scanning real-life scenes with modern registration devices typically giv...
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11/29/2020
ProtoPShare: Prototype Sharing for Interpretable Image Classification and Similarity Discovery
In this paper, we introduce ProtoPShare, a self-explained method that in...
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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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02/18/2020
LocoGAN – Locally Convolutional GAN
In the paper we construct a fully convolutional GAN model: LocoGAN, whic...
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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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09/11/2019
Geometric Graph Convolutional Neural Networks
Graph Convolutional Networks (GCNs) have recently become the primary cho...
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04/06/2019
Interpolation in generative models
We show how to construct smooth and realistic interpolations for generat...
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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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05/04/2017