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08/28/2023
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
Despite significant advances in deep learning, models often struggle to ...
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08/17/2023
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-str...
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03/15/2023
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...
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02/21/2023
Learning signatures of decision making from many individuals playing the same game
Human behavior is incredibly complex and the factors that drive decision...
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01/01/2023
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
There are multiple scales of abstraction from which we can describe the ...
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10/10/2022
The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective
Data augmentation (DA) is a powerful workhorse for bolstering performanc...
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06/14/2022
Learning Behavior Representations Through Multi-Timescale Bootstrapping
Natural behavior consists of dynamics that are both unpredictable, can s...
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06/10/2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
Complex time-varying systems are often studied by abstracting away from ...
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02/08/2022
Learning Sinkhorn divergences for supervised change point detection
Many modern applications require detecting change points in complex sequ...
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11/03/2021
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Meaningful and simplified representations of neural activity can yield i...
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09/09/2021
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Advances in neural recording present increasing opportunities to study n...
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02/19/2021
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build repres...
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12/21/2020
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 align...
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06/04/2020
Bayesian optimization for modular black-box systems with switching costs
Most existing black-box optimization methods assume that all variables i...
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06/27/2019
Hierarchical Optimal Transport for Multimodal Distribution Alignment
In many machine learning applications, it is necessary to meaningfully a...
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04/13/2016
Quantifying mesoscale neuroanatomy using X-ray microtomography
Methods for resolving the 3D microstructure of the brain typically start...
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05/19/2015
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e.g. Gram or similarity matrices) are essential for man...
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05/04/2015
Self-Expressive Decompositions for Matrix Approximation and Clustering
Data-aware methods for dimensionality reduction and matrix decomposition...
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03/19/2013
