13 citations · 35 across the 6 of their papers we have counts for
6 papers
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
Ran Liu, Sahil Khose, Jingyun Xiao +4
Despite significant advances in deep learning, models often struggle to generalize well to new, unseen domains, especially when training data is limited. To address this challenge,…
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
Jorge Quesada, Lakshmi Sathidevi, Ran Liu +10
There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the i…
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Ran Liu, Mehdi Azabou, Max Dabagia +2
Complex time-varying systems are often studied by abstracting away from the dynamics of individual components to build a model of the population-level dynamics from the start. Howe…
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Ran Liu, Mehdi Azabou, Max Dabagia +5
Meaningful and simplified representations of neural activity can yield insights into how and what information is being processed within a neural circuit. However, without labels, f…
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review
Ran Liu
Traditional supervised learning methods are hitting a bottleneck because of their dependency on expensive manually labeled data and their weaknesses such as limited generalization…
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
Mehdi Azabou, Mohammad Gheshlaghi Azar, Ran Liu +10
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample. Without sufficient…