research
∙
09/21/2023
Face Identity-Aware Disentanglement in StyleGAN
Conditional GANs are frequently used for manipulating the attributes of ...
share
research
∙
08/16/2023
Interpretability Benchmark for Evaluating Spatial Misalignment of Prototypical Parts Explanations
Prototypical parts-based networks are becoming increasingly popular due ...
research
∙
07/05/2023
ChiENN: Embracing Molecular Chirality with Graph Neural Networks
Graph Neural Networks (GNNs) play a fundamental role in many deep learni...
research
∙
06/21/2023
Fantastic Weights and How to Find Them: Where to Prune in Dynamic Sparse Training
Dynamic Sparse Training (DST) is a rapidly evolving area of research tha...
research
∙
06/18/2023
ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
Multiple Instance Learning (MIL) is a weakly-supervised problem in which...
research
∙
05/17/2023
MultiPlaneNeRF: Neural Radiance Field with Non-Trainable Representation
NeRF is a popular model that efficiently represents 3D objects from 2D i...
research
∙
04/11/2023
r-softmax: Generalized Softmax with Controllable Sparsity Rate
Nowadays artificial neural network models achieve remarkable results in ...
research
∙
04/03/2023
Gaussian model for closed curves
Gaussian Mixture Models (GMM) do not adapt well to curved and strongly n...
research
∙
03/10/2023
NeRFlame: FLAME-based conditioning of NeRF for 3D face rendering
Traditional 3D face models are based on mesh representations with textur...
research
∙
03/03/2023
Contrastive Hierarchical Clustering
Deep clustering has been dominated by flat models, which split a dataset...
research
∙
02/09/2023
Hypernetworks build Implicit Neural Representations of Sounds
Implicit Neural Representations (INRs) are nowadays used to represent mu...
research
∙
02/08/2023
Revisiting Offline Compression: Going Beyond Factorization-based Methods for Transformer Language Models
Recent transformer language models achieve outstanding results in many n...
research
∙
01/28/2023
ProtoSeg: Interpretable Semantic Segmentation with Prototypical Parts
We introduce ProtoSeg, a novel model for interpretable semantic image se...
research
∙
11/03/2022
HyperSound: Generating Implicit Neural Representations of Audio Signals with Hypernetworks
Implicit neural representations (INRs) are a rapidly growing research fi...
research
∙
10/06/2022
Hypernetwork approach to Bayesian MAML
The main goal of Few-Shot learning algorithms is to enable learning from...
research
∙
08/21/2022
ProPaLL: Probabilistic Partial Label Learning
Partial label learning is a type of weakly supervised learning, where ea...
research
∙
06/29/2022
LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihood
Most of the existing methods for estimating the local intrinsic dimensio...
research
∙
06/28/2022
SLOVA: Uncertainty Estimation Using Single Label One-Vs-All Classifier
Deep neural networks present impressive performance, yet they cannot rel...
research
∙
06/19/2022
Bounding Evidence and Estimating Log-Likelihood in VAE
Many crucial problems in deep learning and statistics are caused by a va...
research
∙
06/16/2022
Continual Learning with Guarantees via Weight Interval Constraints
We introduce a new training paradigm that enforces interval constraints ...
research
∙
03/21/2022
HyperShot: Few-Shot Learning by Kernel HyperNetworks
Few-shot models aim at making predictions using a minimal number of labe...
research
∙
12/06/2021
Interpretable Image Classification with Differentiable Prototypes Assignment
We introduce ProtoPool, an interpretable image classification model with...
research
∙
10/26/2021
MisConv: Convolutional Neural Networks for Missing Data
Processing of missing data by modern neural networks, such as CNNs, rema...
research
∙
10/26/2021
Non-Gaussian Gaussian Processes for Few-Shot Regression
Gaussian Processes (GPs) have been widely used in machine learning to mo...
research
∙
10/12/2021
Relative Molecule Self-Attention Transformer
Self-supervised learning holds promise to revolutionize molecule propert...
research
∙
08/10/2021
Flow-based SVDD for anomaly detection
We propose FlowSVDD – a flow-based one-class classifier for anomaly/outl...
research
∙
07/28/2021
SONG: Self-Organizing Neural Graphs
Recent years have seen a surge in research on deep interpretable neural ...
research
∙
06/15/2021
Direction is what you need: Improving Word Embedding Compression in Large Language Models
The adoption of Transformer-based models in natural language processing ...
research
∙
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...
research
∙
02/11/2021
HyperPocket: Generative Point Cloud Completion
Scanning real-life scenes with modern registration devices typically giv...
research
∙
11/30/2020
RegFlow: Probabilistic Flow-based Regression for Future Prediction
Predicting future states or actions of a given system remains a fundamen...
research
∙
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...
research
∙
10/06/2020
Flow-based anomaly detection
We propose OneFlow - a flow-based one-class classifier for anomaly (outl...
research
∙
09/15/2020
Generative models with kernel distance in data space
Generative models dealing with modeling a joint data distribution are ge...
research
∙
06/17/2020
Adversarial Examples Detection and Analysis with Layer-wise Autoencoders
We present a mechanism for detecting adversarial examples based on data ...
research
∙
06/15/2020
HyperFlow: Representing 3D Objects as Surfaces
In this work, we present HyperFlow - a novel generative model that lever...
research
∙
05/25/2020
Kernel Self-Attention in Deep Multiple Instance Learning
Multiple Instance Learning (MIL) is weakly supervised learning, which as...
research
∙
04/17/2020
Finding the Optimal Network Depth in Classification Tasks
We develop a fast end-to-end method for training lightweight neural netw...
research
∙
02/21/2020
The Break-Even Point on Optimization Trajectories of Deep Neural Networks
The early phase of training of deep neural networks is critical for thei...
research
∙
02/19/2020
Molecule Attention Transformer
Designing a single neural network architecture that performs competitive...
research
∙
02/18/2020
LocoGAN – Locally Convolutional GAN
In the paper we construct a fully convolutional GAN model: LocoGAN, whic...
research
∙
01/13/2020
WICA: nonlinear weighted ICA
Independent Component Analysis (ICA) aims to find a coordinate system in...
research
∙
10/07/2019
Biologically-Inspired Spatial Neural Networks
We introduce bio-inspired artificial neural networks consisting of neuro...
research
∙
09/11/2019
Geometric Graph Convolutional Neural Networks
Graph Convolutional Networks (GCNs) have recently become the primary cho...
research
∙
06/21/2019
SeGMA: Semi-Supervised Gaussian Mixture Auto-Encoder
We propose a semi-supervised generative model, SeGMA, which learns a joi...
research
∙
06/03/2019
Fast and Stable Interval Bounds Propagation for Training Verifiably Robust Models
We present an efficient technique, which allows to train classification ...
research
∙
05/31/2019
Independent Component Analysis based on multiple data-weighting
Independent Component Analysis (ICA) - one of the basic tools in data an...
research
∙
05/30/2019
One-element Batch Training by Moving Window
Several deep models, esp. the generative, compare the samples from two d...
research
∙
04/06/2019
Interpolation in generative models
We show how to construct smooth and realistic interpolations for generat...
research
∙
03/01/2019
