1 citations · 1 across the 8 of their papers we have counts for
9 papers
Perturb-and-Solve: Efficient Learned-Operator Conditioning for Latent Diffusion Inverse Problems
Abduragim Shtanchaev, Arip Asadulaev, Luiza Labazanova +3
Latent diffusion models serve as powerful priors for solving inverse problems in image restoration, such as deblurring, inpainting, and super-resolution. Current methods have a tra…
Optimizers for Diffusion Models: A Controlled Benchmark
Arman Bolatov, Egor Shulgin, David Li +6
Discrete diffusion models now match autoregressive language models on several benchmarks, while the question of how best to train them has received far less attention: the optimize…
Convex Compositional Reasoning Models
Meir Roketlishvili, Semyon Semenov, Maksim Bobrin +7
Compositional energy-based models can generalize to larger combinatorial reasoning problems by reusing a learned factor energy across many local constraints. In our paper, we show…
Sparse Scheduled Diffusion Guidance for Inverse Problems
Abduragim Shtanchaev, Albina Ilina, Yazid Janati +3
Pretrained diffusion models are effective priors for Bayesian inverse problems, but posterior sampling with these priors is often costly because data-consistency guidance is applie…
Curriculum-Augmented GFlowNets For mRNA Sequence Generation
Aya Laajil, Abduragim Shtanchaev, Sajan Muhammad +4
We develop an approach for the de novo synthesis of messenger RNA (mRNA) sequences encoding target proteins. This requires optimizing multiple biological objectives over large comb…
Y-Shaped Generative Flows
Arip Asadulaev, Semyon Semenov, Abduragim Shtanchaev +3
Modern continuous-time generative models typically induce \emph{V-shaped} flows: each sample travels independently along a nearly straight trajectory from the prior to the data. Al…
All Languages Matter: Evaluating LMMs on Culturally Diverse 100 Languages
Ashmal Vayani, Dinura Dissanayake, Hasindri Watawana +66
Existing Large Multimodal Models (LMMs) generally focus on only a few regions and languages. As LMMs continue to improve, it is increasingly important to ensure they understand cul…
Extract More from Less: Efficient Fine-Grained Visual Recognition in Low-Data Regimes
Dmitry Demidov, Abduragim Shtanchaev, Mihail Mihaylov +1
The emerging task of fine-grained image classification in low-data regimes assumes the presence of low inter-class variance and large intra-class variation along with a highly limi…
Automated Remote Sensing Forest Inventory Using Satellite Imagery
Abduragim Shtanchaev, Artur Bille, Olga Sutyrina +1
For many countries like Russia, Canada, or the USA, a robust and detailed tree species inventory is essential to manage their forests sustainably. Since one can not apply unmanned…