Publications

Overcoming the Accuracy–Generalization Tradeoff in Docking and Scoring for Prospective Virtual Screening

Overcoming the Accuracy–Generalization Tradeoff in Docking and Scoring for Prospective Virtual Screening

Garik Petrosyan, Vahagn Altunyan, Tsolak Ghukasyan, Tigran M. Abramyan, Grigor Arakelov, Aram Davtyan, Tigran Aghajanyan, Iskander Nakipov, Gagik Navasardyan, Arman Fahradyan, Hazarapet Tunanyan, Arman Simonyan, Paweł Ł Janczyk, Das De Silva, Hayk Saribekyan, Lev Tsidilkovski, Vahram Arakelov, Narek Ginoyan, Hasmik Mnatsakanyan, Max Ratnikov, Khachik Smbatyan, Ashot Papoyan, and Garegin A. Papoian

bioRxiv preprint, 2026

We introduce DODock and DOScore, docking and scoring ML/physics hybrid frameworks that are also highly expressive, yet generalize much better out of distribution compared with the prior ML approaches.

bioRxiv

Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else

Multi-Concept T2I-Zero: Tweaking Only The Text Embeddings and Nothing Else

Hazarapet Tunanyan, Dejia Xu, Shant Navasardyan, Zhangyang Wang, and Humphrey Shi

Arxiv preprint, 2023

In this work, we consider a more ambitious goal: natural multi-concept generation using a pre-trained diffusion model, and with almost no extra cost.

arXiv Project

Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style

Specialist Diffusion: Plug-and-Play Sample-Efficient Fine-Tuning of Text-to-Image Diffusion Models to Learn Any Unseen Style

Haoming Lu, Hazarapet Tunanyan, Kai Wang, Shant Navasardyan, Zhangyang Wang, and Humphrey Shi

IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023

In this paper, we aim to learn an unseen style, so that the fine-tuned model can generate high-quality images of arbitrary objects in this style.

PDF Code Project Poster

C4Net: Contextual Compression and Complementary Combination Network for Salient Object Detection

C4Net: Contextual Compression and Complementary Combination Network for Salient Object Detection

Hazarapet Tunanyan

BMVC, 2021

The paper proposes a salient object detection model using feature concatenation, complementary feature extraction, excessiveness loss, and pyramid-semantic guidance.

arXiv