Sitemap
A list of all the posts and pages found on the site. For you robots out there, there is an XML version available for digesting as well.
Pages
Posts
Future Blog Post
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Blog Post number 4
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 3
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 2
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
Blog Post number 1
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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.
portfolio
Portfolio item number 1
Short description of portfolio item number 1
Portfolio item number 2
Short description of portfolio item number 2 
publications
Multimodal Distributions in Variational Autoencoders
Published in MSc Thesis at Skoltech, 2017

Recommended citation: E. Iakovleva. "Multimodal Distributions in Variational Autoencoders." MSc Thesis at Skoltech, 2017.
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Exploring Generative Image Modeling with Block PixelCNNs
Published in MSc Thesis at Grenoble INP, 2018
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Recommended citation: E. Iakovleva. "Exploring Generative Image Modeling with Block PixelCNNs." MSc Thesis at Grenoble INP, 2018.
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Meta-Learning with Shared Amortized Variational Inference
Published in International Conference on Machine Learning, 2020

Recommended citation: E. Iakovleva, J. Verbeek and K. Alahari. "Meta-Learning with Shared Amortized Variational Inference." In ICML'20.
Download Paper | Download Slides | Download Bibtex
Multi-Domain Learning with Modulation Adapters
Published in arXiv, 2023

Recommended citation: E. Iakovleva, K. Alahari and J. Verbeek. "Multi-Domain Learning with Modulation Adapters." In arXiv:2307.08528'23.
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Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing
Published in British Machine Vision Conference, 2025

Recommended citation: E. Iakovleva, F. Pizzati, P. Torr and S. Lathuilière. "Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing." In BMVC'25.
Download Paper | Download Slides | Download Bibtex | Paper Code
How I Met Your Bias: Investigating Bias Amplification in Diffusion Models
Published in IEEE/CVF Winter Conference on Applications of Computer Vision, 2026

Recommended citation: N. Roos, E. Iakovleva, A. Gjergji, V. Pastore and E. Tartaglione. "How I Met Your Bias: Investigating Bias Amplification in Diffusion Models." In WACV'26.
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Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models
Published in IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2026

Recommended citation: I. Matos, A. Saoud, E. Iakovleva, V. Pastore and E. Tartaglione. "Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models." In CVPR'26.
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talks
Meta-Learning at ICML’20
Published:
Video presentation of the paper “Meta-Learning with Shared Amortized Variational Inference” at the International Conference on Machine Learning 2020.
Text-Based Image Editing at BMVC’25
Published:
Video presentation of the paper “Specify and Edit: Overcoming Ambiguity in Text-Based Image Editing” at the British Machine Vision Conference 2025.
teaching
Deep Learning for Computer Vision
Master course, Télécom Paris, 2024
TP (lab) sessions “Domain Adaptation” and “CLIP”, final exam with grading.
Deep Leaning for Multimedia
Master course, Télécom Paris, 2024
Lecture on Transformers and Transfer Learning.
Introduction to Deep Learning
Master course, Télécom Paris, 2025
TP (laboratory) session “Convolutional Neural Networks (CNNs)” and lab grading.
Practice in Deep Learning
Master course, ATHENS, 2025
TP (laboratory) sessions “Introduction to Python and MLPs” and “Convolutional Neural Networks (CNNs)”, lab grading.
Deep Leaning for Multimedia
Master course, Télécom Paris, 2025
Lecture on Recurrent Neural Networks (RNNs) and Transformers.
