44 citations · 111 across the 10 of their papers we have counts for
11 papers
JGAT: a joint spatio-temporal graph attention model for brain decoding
Han Yi Chiu, Liang Zhao, Anqi Wu
The decoding of brain neural networks has been an intriguing topic in neuroscience for a well-rounded understanding of different types of brain disorders and cognitive stimuli. Int…
Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
Yule Wang, Zijing Wu, Chengrui Li +1
In the field of behavior-related brain computation, it is necessary to align raw neural signals against the drastic domain shift among them. A foundational framework within neurosc…
Inverse Reinforcement Learning with the Average Reward Criterion
Feiyang Wu, Jingyang Ke, Anqi Wu
We study the problem of Inverse Reinforcement Learning (IRL) with an average-reward criterion. The goal is to recover an unknown policy and a reward function when the agent only ha…
Inverse Kernel Decomposition
Chengrui Li, Anqi Wu
The state-of-the-art dimensionality reduction approaches largely rely on complicated optimization procedures. On the other hand, closed-form approaches requiring merely eigen-decom…
Domain Generalization via Domain-based Covariance Minimization
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 quit…
Neural Latents Benchmark '21: Evaluating latent variable models of neural population activity
Felix Pei, Joel Ye, David Zoltowski +13
Advances in neural recording present increasing opportunities to study neural activity in unprecedented detail. Latent variable models (LVMs) are promising tools for analyzing this…
Incorporating structured assumptions with probabilistic graphical models in fMRI data analysis
Ming Bo Cai, Michael Shvartsman, Anqi Wu +2
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 ye…
Neural Dynamics Discovery via Gaussian Process Recurrent Neural Networks
Qi She, Anqi Wu
Latent dynamics discovery is challenging in extracting complex dynamics from high-dimensional noisy neural data. Many dimensionality reduction methods have been widely adopted to e…
Deterministic Variational Inference for Robust Bayesian Neural Networks
Anqi Wu, Sebastian Nowozin, Edward Meeds +3
Bayesian neural networks (BNNs) hold great promise as a flexible and principled solution to deal with uncertainty when learning from finite data. Among approaches to realize probab…
Dependent relevance determination for smooth and structured sparse regression
Anqi Wu, Oluwasanmi Koyejo, Jonathan W. Pillow
In many problem settings, parameter vectors are not merely sparse but dependent in such a way that non-zero coefficients tend to cluster together. We refer to this form of dependen…
Exploiting gradients and Hessians in Bayesian optimization and Bayesian quadrature
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. A popular B…