Search Results for author: Anqi Wu

Found 13 papers, 5 papers with code

SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework

no code implementations14 Apr 2022 Ari Blau, Christoph Gebhardt, Andres Bendesky, Liam Paninski, Anqi Wu

Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology.

Animal Pose Estimation

Domain Generalization via Domain-based Covariance Minimization

no code implementations12 Oct 2021 Anqi Wu

Researchers have been facing a difficult problem that data generation mechanisms could be influenced by internal or external factors leading to the training and test data with quite different distributions, consequently traditional classification or regression from the training set is unable to achieve satisfying results on test data.

Dimensionality Reduction Domain Generalization

Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis

no code implementations11 May 2020 Ming Bo Cai, Michael Shvartsman, Anqi Wu, Hejia Zhang, Xia Zhu

With the wide adoption of functional magnetic resonance imaging (fMRI) by cognitive neuroscience researchers, large volumes of brain imaging data have been accumulated in recent years.

Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks

1 code implementation1 Jul 2019 Qi She, Anqi Wu

In the experiment, we show that our model outperforms other state-of-the-art methods in reconstructing insightful latent dynamics from both simulated and experimental neural datasets with either Gaussian or Poisson observations, especially in the low-sample scenario.

Dimensionality Reduction Time Series +1

Learning a latent manifold of odor representations from neural responses in piriform cortex

no code implementations NeurIPS 2018 Anqi Wu, Stan Pashkovski, Sandeep R. Datta, Jonathan W. Pillow

Our approach is based on the Gaussian process latent variable model, and seeks to map odorants to points in a low-dimensional embedding space, where distances between points in the embedding space relate to the similarity of population responses they elicit.

Deterministic Variational Inference for Robust Bayesian Neural Networks

2 code implementations ICLR 2019 Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

We provide two innovations that aim to turn VB into a robust inference tool for Bayesian neural networks: first, we introduce a novel deterministic method to approximate moments in neural networks, eliminating gradient variance; second, we introduce a hierarchical prior for parameters and a novel Empirical Bayes procedure for automatically selecting prior variances.

Variational Inference

Gaussian process based nonlinear latent structure discovery in multivariate spike train data

no code implementations NeurIPS 2017 Anqi Wu, Nicholas G. Roy, Stephen Keeley, Jonathan W. Pillow

We apply the model to spike trains recorded from hippocampal place cells and show that it compares favorably to a variety of previous methods for latent structure discovery, including variational auto-encoder (VAE) based methods that parametrize the nonlinear mapping from latent space to spike rates with a deep neural network.

Gaussian Processes

Dependent relevance determination for smooth and structured sparse regression

1 code implementation28 Nov 2017 Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow

Our approach represents a hierarchical extension of the relevance determination framework, where we add a transformed Gaussian process to model the dependencies between the prior variances of regression weights.

Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature

no code implementations31 Mar 2017 Anqi Wu, Mikio C. Aoi, Jonathan W. Pillow

An exciting branch of machine learning research focuses on methods for learning, optimizing, and integrating unknown functions that are difficult or costly to evaluate.

Gaussian Processes

Convolutional spike-triggered covariance analysis for neural subunit models

no code implementations NeurIPS 2015 Anqi Wu, Il Memming Park, Jonathan W. Pillow

Subunit models provide a powerful yet parsimonious description of neural spike responses to complex stimuli.

Sparse Bayesian structure learning with “dependent relevance determination” priors

no code implementations NeurIPS 2014 Anqi Wu, Mijung Park, Oluwasanmi O. Koyejo, Jonathan W. Pillow

Classical sparse regression methods, such as the lasso and automatic relevance determination (ARD), model parameters as independent a priori, and therefore do not exploit such dependencies.

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