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Karush Suri

4 accepted papers

2025

A Cross Modal Knowledge Distillation & Data Augmentation Recipe for Improving Transcriptomics Representations through Morphological Features

ICML 2025poster

Understanding cellular responses to stimuli is crucial for biological discovery and drug development. Transcriptomics provides interpretable, gene-level insights, while microscopy imaging offers rich predictive features but is harder to interpret. Weakly paired datasets, where samples share biologic…

Cited by 0SourcePDFScholar
2024

How Molecules Impact Cells: Unlocking Contrastive PhenoMolecular Retrieval

NeurIPS 2024poster

Predicting molecular impact on cellular function is a core challenge in therapeutic design. Phenomic experiments, designed to capture cellular morphology, utilize microscopy based techniques and demonstrate a high throughput solution for uncovering molecular impact on the cell. In this work, we lear…

Cited by 2SourcePDFScholar
2024

On the Scalability of GNNs for Molecular Graphs

NeurIPS 2024poster

Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong relationship between model size, dataset size, and performance. However, structure-based architectures such as Graph Neural Networks (GNNs) are ye…

Cited by 12SourcePDFScholar
2022

Surprise Minimizing Multi-Agent Learning with Energy-based Models

NeurIPS 2022accept

Multi-Agent Reinforcement Learning (MARL) has demonstrated significant suc2 cess by virtue of collaboration across agents. Recent work, on the other hand, introduces surprise which quantifies the degree of change in an agent’s environ4 ment. Surprise-based learning has received significant attention…

Cited by 1SourcePDFScholar