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Found 9 papers, 2 papers with code
Conditional out-of-sample generation for unpaired data using trVAE
While generative models have shown great success in generating high-dimensional samples conditional on low-dimensional descriptors (learning e. g. stroke thickness in MNIST, hair color in CelebA, or speaker identity in Wavenet), their generation out-of-sample poses fundamental problems.
Mixed Models with Multiple Instance Learning
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease.
Beyond Predictions in Neural ODEs: Identification and Interventions
Spurred by tremendous success in pattern matching and prediction tasks, researchers increasingly resort to machine learning to aid original scientific discovery.
SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching
Translating the relevance of preclinical models ($\textit{in vitro}$, animal models, or organoids) to their relevance in humans presents an important challenge during drug development.
Training Transitive and Commutative Multimodal Transformers with LoReTTa
We introduce LoReTTa (Linking mOdalities with a tRansitive and commutativE pre-Training sTrAtegy) to address this understudied problem.
Conditionally Invariant Representation Learning for Disentangling Cellular Heterogeneity
Specifically, the proposed approach helps to disentangle biological signals from data biases that are unrelated to the target task or the causal explanation of interest.
Causal machine learning for single-cell genomics
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells.
To Transformers and Beyond: Large Language Models for the Genome
In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges.
B-Cos Aligned Transformers Learn Human-Interpretable Features
Vision Transformers (ViTs) and Swin Transformers (Swin) are currently state-of-the-art in computational pathology.
