Search Results for author:
Found 21 papers, 11 papers with code
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
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.
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 performance in modern machine learning.
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.
Learning Sinkhorn divergences for supervised change point detection
We present a novel change point detection framework that uses true change point instances as supervision for learning a ground metric such that Sinkhorn divergences can be then used in two-sample tests on sliding windows to detect change points in an online manner.
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).
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
We curate four datasets of neural spiking activity from cognitive, sensory, and motor areas to promote models that apply to the wide variety of activity seen across these areas.
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.
Bayesian optimization for modular black-box systems with switching costs
In this work, we propose a new algorithm for switch cost-aware optimization called Lazy Modular Bayesian Optimization (LaMBO).
Hierarchical Optimal Transport for Multimodal Distribution Alignment
Our results demonstrate that when clustered structure exists in datasets, and is consistent across trials or time points, a hierarchical alignment strategy that leverages such structure can provide significant improvements in cross-domain alignment.
Quantifying mesoscale neuroanatomy using X-ray microtomography
Methods for resolving the 3D microstructure of the brain typically start by thinly slicing and staining the brain, and then imaging each individual section with visible light photons or electrons.
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation
Kernel matrices (e. g. Gram or similarity matrices) are essential for many state-of-the-art approaches to classification, clustering, and dimensionality reduction.
Self-Expressive Decompositions for Matrix Approximation and Clustering
Classical approaches discover such structure by learning a basis that can efficiently express the collection.
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets.
Greedy Feature Selection for Subspace Clustering
To learn a union of subspaces from a collection of data, sets of signals in the collection that belong to the same subspace must be identified in order to obtain accurate estimates of the subspace structures present in the data.
