Search Results for author:
Found 17 papers, 8 papers with code
Particle identification with machine learning from incomplete data in the ALICE experiment
We also present the integration of the ML project with the ALICE analysis software, and we discuss domain adaptation, the ML technique needed to transfer the knowledge between simulated and real experimental data.
GUIDE: Guidance-based Incremental Learning with Diffusion Models
We introduce GUIDE, a novel continual learning approach that directs diffusion models to rehearse samples at risk of being forgotten.
Machine-learning-based particle identification with missing data
In this work, we propose the first method for PID that can be trained with all of the available data examples, including incomplete ones.
Adapt & Align: Continual Learning with Generative Models Latent Space Alignment
In this work, we introduce Adapt & Align, a method for continual learning of neural networks by aligning latent representations in generative models.
Bayesian Flow Networks in Continual Learning
Bayesian Flow Networks (BFNs) has been recently proposed as one of the most promising direction to universal generative modelling, having ability to learn any of the data type.
Looking through the past: better knowledge retention for generative replay in continual learning
We notice that in VAE-based generative replay, this could be attributed to the fact that the generated features are far from the original ones when mapped to the latent space.
Machine Learning methods for simulating particle response in the Zero Degree Calorimeter at the ALICE experiment, CERN
In particular, we examine the performance of variational autoencoders and generative adversarial networks, expanding the GAN architecture by an additional regularisation network and a simple, yet effective postprocessing step.
Exploring Continual Learning of Diffusion Models
Diffusion models have achieved remarkable success in generating high-quality images thanks to their novel training procedures applied to unprecedented amounts of data.
Learning Data Representations with Joint Diffusion Models
Joint machine learning models that allow synthesizing and classifying data often offer uneven performance between those tasks or are unstable to train.
Modelling low-resource accents without accent-specific TTS frontend
Prior work on modelling accents assumes a phonetic transcription is available for the target accent, which might not be the case for low-resource, regional accents.
Selectively increasing the diversity of GAN-generated samples
Especially prone to mode collapse are conditional GANs, which tend to ignore the input noise vector and focus on the conditional information.
On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models
Their main strength comes from their unique setup in which a model (the backward diffusion process) is trained to reverse the forward diffusion process, which gradually adds noise to the input signal.
Logarithmic Continual Learning
Our approach leverages allocation of past data in a~set of generative models such that most of them do not require retraining after a~task.
On robustness of generative representations against catastrophic forgetting
Catastrophic forgetting of previously learned knowledge while learning new tasks is a widely observed limitation of contemporary neural networks.
Multiband VAE: Latent Space Alignment for Knowledge Consolidation in Continual Learning
We propose a new method for unsupervised generative continual learning through realignment of Variational Autoencoder's latent space.
BinPlay: A Binary Latent Autoencoder for Generative Replay Continual Learning
We introduce a binary latent space autoencoder architecture to rehearse training samples for the continual learning of neural networks.
End-to-end Sinkhorn Autoencoder with Noise Generator
To address these shortcomings, we introduce a novel method dubbed end-to-end Sinkhorn Autoencoder, that leverages sinkhorn algorithm to explicitly align distribution of encoded real data examples and generated noise.
