10 citations · 33 across the 16 of their papers we have counts for
17 papers
Reasoning with Neural Cellular Automata
Mayalen Etcheverry, Pietro Miotti, Aidan Sirbu +7
Modern AI architectures used to solve visual reasoning tasks typically rely heavily on global connectivity and synchronization. As biological systems demonstrate, though, sophistic…
Let It Go or Learn to Self-Correct: Continuous Diffusion for Constrained Discrete Tasks
Mariia Drozdova, Stéphane Liem Nguyen, François Fleuret
Denoising Diffusion Probabilistic Models (DDPMs) generate samples by starting from noise and repeatedly denoising while keeping each update close to the current noisy state. This b…
Diffusion as a Training Curriculum for Timestep-Free Iterative Reasoning
Mariia Drozdova, Aidan Sirbu, Pietro Miotti +4
Diffusion models and recursive reasoners are both iterative, but they carry information across iterations differently. We add a persistent hidden state to a diffusion denoiser and…
Learning Radio Astronomical Representations with LeJEPA and Very Small Models
Erica Lastufka, Mariia Drozdova, Vitaliy Kinakh +4
Representations learned by vision foundation models pretrained on natural images have been shown to be useful for out-of-domain astronomical images. Performance on scientific downs…
DINOspec: Efficient Multimodal Alignment of Vision and Spectral Foundation Models for Astronomy
Erica Lastufka, Mariia Drozdova, Daniel Schaerer +1
Astronomical observations provide multimodal views of physical systems, with images and spectra capturing complementary properties of celestial objects. Scientific foundation model…
Exploring Image-Text Alignment for Radio Galaxy Morphologies
Erica Lastufka, Mariia Drozdova, Svyatoslav Volosynovskiy
We investigate whether specially constructed text captions can capture the same morphological information as radio galaxy images. Using the MiraBest dataset, we generate captions w…
Radio Astronomy in the Era of Vision-Language Models: Prompt Sensitivity and Adaptation
Mariia Drozdova, Erica Lastufka, Vitaliy Kinakh +3
Vision-Language Models (VLMs), such as recent Qwen and Gemini models, are positioned as general-purpose AI systems capable of reasoning across domains. Yet their capabilities in sc…
Semi-Supervised Fine-Tuning of Vision Foundation Models with Content-Style Decomposition
Mariia Drozdova, Vitaliy Kinakh, Yury Belousov +2
In this paper, we present a semi-supervised fine-tuning approach designed to improve the performance of pre-trained foundation models on downstream tasks with limited labeled data.…
Bridging the Gap: Examining Vision Foundation Models for Optical and Radio Astronomy Applications
E. Lastufka, O. Bait, M. Drozdova +7
Vision foundation models, which have demonstrated significant potential in many multimedia applications, are often underutilized in the natural sciences. This is primarily due to m…
Self-Supervised Learning on MeerKAT Wide-Field Continuum Images
Erica Lastufka, Omkar Bait, Olga Taran +8
Self-supervised learning (SSL) applied to natural images has demonstrated a remarkable ability to learn meaningful, low-dimension representations without labels, resulting in model…
Solar synthetic imaging: Introducing denoising diffusion probabilistic models on SDO/AIA data
Francesco P. Ramunno, S. Hackstein, V. Kinakh +4
Given the rarity of significant solar flares compared to smaller ones, training effective machine learning models for solar activity forecasting is challenging due to insufficient…
Radio-astronomical Image Reconstruction with Conditional Denoising Diffusion Model
Mariia Drozdova, Vitaliy Kinakh, Omkar Bait +6
Reconstructing sky models from dirty radio images for accurate source localization and flux estimation is crucial for studying galaxy evolution at high redshift, especially in deep…
MV-MR: multi-views and multi-representations for self-supervised learning and knowledge distillation
Vitaliy Kinakh, Mariia Drozdova, Slava Voloshynovskiy
We present a new method of self-supervised learning and knowledge distillation based on the multi-views and multi-representations (MV-MR). The MV-MR is based on the maximization of…
Turbo-Sim: a generalised generative model with a physical latent space
Guillaume Quétant, Mariia Drozdova, Vitaliy Kinakh +2
We present Turbo-Sim, a generalised autoencoder framework derived from principles of information theory that can be used as a generative model. By maximising the mutual information…
Information-theoretic stochastic contrastive conditional GAN: InfoSCC-GAN
Vitaliy Kinakh, Mariia Drozdova, Guillaume Quétant +2
Conditional generation is a subclass of generative problems where the output of the generation is conditioned by the attribute information. In this paper, we present a stochastic c…
Generation of data on discontinuous manifolds via continuous stochastic non-invertible networks
Mariia Drozdova, Vitaliy Kinakh, Guillaume Quétant +2
The generation of discontinuous distributions is a difficult task for most known frameworks such as generative autoencoders and generative adversarial networks. Generative non-inve…
A study of Neural networks point source extraction on simulated Fermi/LAT Telescope images
Mariia Drozdova, Anton Broilovskiy, Andrey Ustyuzhanin +1
Astrophysical images in the GeV band are challenging to analyze due to the strong contribution of the background and foreground astrophysical diffuse emission and relatively broad…