2 citations · 3 across the 4 of their papers we have counts for
5 papers
Score-based Idempotent Distillation of Diffusion Models
Shehtab Zaman, Chengyan Liu, Kenneth Chiu
Idempotent generative networks (IGNs) are a new line of generative models based on idempotent mapping to a target manifold. IGNs support both single-and multi-step generation, allo…
BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models
Evan R. Antoniuk, Shehtab Zaman, Tal Ben-Nun +10
Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules…
PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval
Ryuma Nakahata, Shehtab Zaman, Mingyuan Zhang +2
Ptychography is a computational method of microscopy that recovers high-resolution transmission images of samples from a series of diffraction patterns. While conventional phase re…
Distributed Reinforcement Learning for Molecular Design: Antioxidant case
Huanyi Qin, Denis Akhiyarov, Sophie Loehle +2
Deep reinforcement learning has successfully been applied for molecular discovery as shown by the Molecule Deep Q-network (MolDQN) algorithm. This algorithm has challenges when app…
STRIDE: Structure-guided Generation for Inverse Design of Molecules
Shehtab Zaman, Denis Akhiyarov, Mauricio Araya-Polo +1
Machine learning and especially deep learning has had an increasing impact on molecule and materials design. In particular, given the growing access to an abundance of high-quality…