33 citations · 134 across the 22 of their papers we have counts for
19 papers · 1 filter
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill +6
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task…
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions
Chanakya Ekbote, Moksh Jain, Payel Das +1
Generative Flow Networks (GFlowNets) have demonstrated significant performance improvements for generating diverse discrete objects given a reward function , indicating t…
SynBench: Task-Agnostic Benchmarking of Pretrained Representations using Synthetic Data
Ching-Yun Ko, Pin-Yu Chen, Jeet Mohapatra +2
Recent success in fine-tuning large models, that are pretrained on broad data at scale, on downstream tasks has led to a significant paradigm shift in deep learning, from task-cent…
Learning Geometrically Disentangled Representations of Protein Folding Simulations
N. Joseph Tatro, Payel Das, Pin-Yu Chen +2
Massive molecular simulations of drug-target proteins have been used as a tool to understand disease mechanism and develop therapeutics. This work focuses on learning a generative…
Data-Efficient Graph Grammar Learning for Molecular Generation
Minghao Guo, Veronika Thost, Beichen Li +3
The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets…
Fourier Representations for Black-Box Optimization over Categorical Variables
Hamid Dadkhahi, Jesus Rios, Karthikeyan Shanmugam +1
Optimization of real-world black-box functions defined over purely categorical variables is an active area of research. In particular, optimization and design of biological sequenc…