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Found 10 papers, 7 papers with code
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-structured data.
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Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis
Natural behavior consists of dynamics that are complex and unpredictable, especially when trying to predict many steps into the future.
Learning signatures of decision making from many individuals playing the same game
In addition to predicting future choices, we show that our model can learn rich representations of human behavior over multiple timescales and provide signatures of differences in individuals.
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
Learning Behavior Representations Through Multi-Timescale Bootstrapping
Natural behavior consists of dynamics that are both unpredictable, can switch suddenly, and unfold over many different timescales.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample.
Large-Scale Representation Learning on Graphs via Bootstrapping
To address these challenges, we introduce Bootstrapped Graph Latents (BGRL) - a graph representation learning method that learns by predicting alternative augmentations of the input.
Making transport more robust and interpretable by moving data through a small number of anchor points
Optimal transport (OT) is a widely used technique for distribution alignment, with applications throughout the machine learning, graphics, and vision communities.
