285 citations · 349 across the 10 of their papers we have counts for
10 papers
Learning What Matters: Steering Diffusion via Spectrally Anisotropic Forward Noise
Luca Scimeca, Thomas Jiralerspong, Berton Earnshaw +2
Diffusion Probabilistic Models (DPMs) have achieved strong generative performance, yet their inductive biases remain largely implicit. In this work, we aim to build inductive biase…
Virtual Cells: Predict, Explain, Discover
Emmanuel Noutahi, Jason Hartford, Prudencio Tossou +12
Drug discovery is fundamentally a process of inferring the effects of treatments on patients, and would therefore benefit immensely from computational models that can reliably simu…
TxPert: Leveraging Biochemical Relationships for Out-of-Distribution Transcriptomic Perturbation Prediction
Frederik Wenkel, Wilson Tu, Cassandra Masschelein +12
Accurately predicting cellular responses to genetic perturbations is essential for understanding disease mechanisms and designing effective therapies. Yet exhaustively exploring th…
Shaping Inductive Bias in Diffusion Models through Frequency-Based Noise Control
Thomas Jiralerspong, Berton Earnshaw, Jason Hartford +2
Diffusion Probabilistic Models (DPMs) are powerful generative models that have achieved unparalleled success in a number of generative tasks. In this work, we aim to build inductiv…
Automated Discovery of Pairwise Interactions from Unstructured Data
Zuheng, Xu, Moksh Jain +5
Pairwise interactions between perturbations to a system can provide evidence for the causal dependencies of the underlying underlying mechanisms of a system. When observations are…
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +14
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images. This work explores…
Masked Autoencoders are Scalable Learners of Cellular Morphology
Oren Kraus, Kian Kenyon-Dean, Saber Saberian +13
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research. Prior results…
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Maciej Sypetkowski, Morteza Rezanejad, Saber Saberian +9
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the…
MolE: a molecular foundation model for drug discovery
Oscar Méndez-Lucio, Christos Nicolaou, Berton Earnshaw
Models that accurately predict properties based on chemical structure are valuable tools in drug discovery. However, for many properties, public and private training sets are typic…
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund +20
Distribution shifts -- where the training distribution differs from the test distribution -- can substantially degrade the accuracy of machine learning (ML) systems deployed in the…