Search Results for author:
Found 8 papers, 4 papers with code
On the Scalability of GNNs for Molecular Graphs
However, structure-based architectures such as Graph Neural Networks (GNNs) are yet to show the benefits of scale mainly due to the lower efficiency of sparse operations, large data requirements, and lack of clarity about the effectiveness of various architectures.
Masked Autoencoders for Microscopy are Scalable Learners of Cellular Biology
Featurizing microscopy images for use in biological research remains a significant challenge, especially for large-scale experiments spanning millions of images.
Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets
Recently, pre-trained foundation models have enabled significant advancements in multiple fields.
Masked Autoencoders are Scalable Learners of Cellular Morphology
Inferring biological relationships from cellular phenotypes in high-content microscopy screens provides significant opportunity and challenge in biological research.
Spatial Latent Representations in Generative Adversarial Networks for Image Generation
In this work, we define a family of spatial latent spaces for StyleGAN2, capable of capturing more details and representing images that are out-of-sample in terms of the number and arrangement of object parts, such as an image of multiple faces or a face with more than two eyes.
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task.
Insights From the NeurIPS 2021 NetHack Challenge
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge.
Augmentation Inside the Network
We propose a modification that is 30% faster than the flip test-time augmentation and achieves the same results for CIFAR-100.
