Search Results for author: Anqi Wu

Found 22 papers, 8 papers with code

Learn to Teach: Improve Sample Efficiency in Teacher-student Learning for Sim-to-Real Transfer

no code implementations9 Feb 2024 Feiyang Wu, Zhaoyuan Gu, Ye Zhao, Anqi Wu

We implement variants of our methods, conduct experiments on the MuJoCo benchmark, and apply our methods to the Cassie robot locomotion problem.

reinforcement-learning

Multi-Region Markovian Gaussian Process: An Efficient Method to Discover Directional Communications Across Multiple Brain Regions

no code implementations5 Feb 2024 Weihan Li, Chengrui Li, Yule Wang, Anqi Wu

Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.

A Differentiable Partially Observable Generalized Linear Model with Forward-Backward Message Passing

no code implementations2 Feb 2024 Chengrui Li, Weihan Li, Yule Wang, Anqi Wu

For (1), we propose a new differentiable POGLM, which enables the pathwise gradient estimator, better than the score function gradient estimator used in existing works.

Variational Inference

Forward $χ^2$ Divergence Based Variational Importance Sampling

no code implementations4 Nov 2023 Chengrui Li, Yule Wang, Weihan Li, Anqi Wu

Maximizing the log-likelihood is a crucial aspect of learning latent variable models, and variational inference (VI) stands as the commonly adopted method.

Variational Inference

One-hot Generalized Linear Model for Switching Brain State Discovery

no code implementations23 Oct 2023 Chengrui Li, Soon Ho Kim, Chris Rodgers, Hannah Choi, Anqi Wu

We introduce both a Gaussian prior and a one-hot prior over the GLM in each state.

Infer and Adapt: Bipedal Locomotion Reward Learning from Demonstrations via Inverse Reinforcement Learning

no code implementations28 Sep 2023 Feiyang Wu, Zhaoyuan Gu, Hanran Wu, Anqi Wu, Ye Zhao

Enabling bipedal walking robots to learn how to maneuver over highly uneven, dynamically changing terrains is challenging due to the complexity of robot dynamics and interacted environments.

Imitation Learning

Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models

1 code implementation9 Jun 2023 Yule Wang, Zijing Wu, Chengrui Li, Anqi Wu

Specifically, the latent dynamics structures of the source domain are first extracted by a diffusion model.

JGAT: a joint spatio-temporal graph attention model for brain decoding

1 code implementation3 Jun 2023 Han Yi Chiu, Liang Zhao, Anqi Wu

However, traditional approaches for integrating FC and SC overlook the dynamical variations, which stand a great chance to over-generalize the brain neural network.

Brain Decoding Graph Attention

Inverse Kernel Decomposition

1 code implementation11 Nov 2022 Chengrui Li, Anqi Wu

To deal with very noisy data with weak correlations, we propose two solutions -- blockwise and geodesic -- to make use of locally correlated data points and provide better and numerically more stable latent estimations.

Dimensionality Reduction

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 Analysis

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

3 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.

regression

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.

Bayesian Optimization 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.

regression

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