Alex Lu

Orcid: 0000-0001-9568-3155

Affiliations:
  • University of Toronto, ON, Canada


According to our database1, Alex Lu authored at least 18 papers between 2019 and 2024.

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Bibliography

2024
Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas.
CoRR, 2024

Feature Reuse and Scaling: Understanding Transfer Learning with Protein Language Models.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

ASL STEM Wiki: Dataset and Benchmark for Interpreting STEM Articles.
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023
ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition.
CoRR, 2023

ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Domain adaptation using optimal transport for invariant learning using histopathology datasets.
Proceedings of the Medical Imaging with Deep Learning, 2023

2022
Discovering molecular features of intrinsically disordered regions by using evolution for contrastive learning.
PLoS Comput. Biol., 2022

Protein structure generation via folding diffusion.
CoRR, 2022

2021
Unsupervised machine learning for hypothesis discovery and representation learning in biological image and sequence analysis.
PhD thesis, 2021

CytoImageNet: A large-scale pretraining dataset for bioimage transfer learning.
CoRR, 2021

Random Embeddings and Linear Regression can Predict Protein Function.
CoRR, 2021

Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2021

2020
Evolution Is All You Need: Phylogenetic Augmentation for Contrastive Learning.
CoRR, 2020

Improved Conditional Flow Models for Molecule to Image Synthesis.
CoRR, 2020

2019
Learning unsupervised feature representations for single cell microscopy images with paired cell inpainting.
PLoS Comput. Biol., 2019

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers.
CoRR, 2019

YeastSpotter: accurate and parameter-free web segmentation for microscopy images of yeast cells.
Bioinform., 2019

The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019


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