Search Results for author: Xinshi Chen

Found 15 papers, 9 papers with code

uGLAD: Sparse graph recovery by optimizing deep unrolled networks

1 code implementation23 May 2022 Harsh Shrivastava, Urszula Chajewska, Robin Abraham, Xinshi Chen

Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting.

Multi-Task Learning

Multi-task Learning of Order-Consistent Causal Graphs

no code implementations NeurIPS 2021 Xinshi Chen, Haoran Sun, Caleb Ellington, Eric Xing, Le Song

We consider the problem of discovering $K$ related Gaussian directed acyclic graphs (DAGs), where the involved graph structures share a consistent causal order and sparse unions of supports.

Multi-Task Learning

Provable Learning-based Algorithm For Sparse Recovery

no code implementations ICLR 2022 Xinshi Chen, Haoran Sun, Le Song

In this work, we propose PLISA (Provable Learning-based Iterative Sparse recovery Algorithm) to learn algorithms automatically from data.

Learning Two-Time-Scale Representations For Large Scale Recommendations

no code implementations1 Jan 2021 Xinshi Chen, Yan Zhu, Haowen Xu, Muhan Zhang, Liang Xiong, Le Song

We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.

A Framework For Differentiable Discovery Of Graph Algorithms

no code implementations NeurIPS Workshop LMCA 2020 Hanjun Dai, Xinshi Chen, Yu Li, Xin Gao, Le Song

Recently there is a surge of interests in using graph neural networks (GNNs) to learn algorithms.

Understanding Deep Architecture with Reasoning Layer

1 code implementation NeurIPS 2020 Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Recently, there is a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.

Understanding Deep Architectures with Reasoning Layer

1 code implementation24 Jun 2020 Xinshi Chen, Yufei Zhang, Christoph Reisinger, Le Song

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks.

Learning to Stop While Learning to Predict

1 code implementation ICML 2020 Xinshi Chen, Hanjun Dai, Yu Li, Xin Gao, Le Song

Similar to algorithms, the optimal depth of a deep architecture may be different for different input instances, either to avoid ``over-thinking'', or because we want to compute less for operations converged already.

Meta-Learning

RNA Secondary Structure Prediction By Learning Unrolled Algorithms

1 code implementation ICLR 2020 Xinshi Chen, Yu Li, Ramzan Umarov, Xin Gao, Le Song

The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints.

Efficient Probabilistic Logic Reasoning with Graph Neural Networks

1 code implementation ICLR 2020 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

In this paper, we explore the combination of MLNs and GNNs, and use graph neural networks for variational inference in MLN.

Variational Inference

Review: Ordinary Differential Equations For Deep Learning

no code implementations1 Nov 2019 Xinshi Chen

Finally, two applications of using a continuous model will be shown in Section 5 and 6 to demonstrate some of its advantages over traditional neural networks.

Can Graph Neural Networks Help Logic Reasoning?

no code implementations5 Jun 2019 Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, Le Song

Effectively combining logic reasoning and probabilistic inference has been a long-standing goal of machine learning: the former has the ability to generalize with small training data, while the latter provides a principled framework for dealing with noisy data.

GLAD: Learning Sparse Graph Recovery

1 code implementation ICLR 2020 Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinvas Aluru, Han Liu, Le Song

Recently, there is a surge of interest to learn algorithms directly based on data, and in this case, learn to map empirical covariance to the sparse precision matrix.

Particle Flow Bayes' Rule

2 code implementations2 Feb 2019 Xinshi Chen, Hanjun Dai, Le Song

We present a particle flow realization of Bayes' rule, where an ODE-based neural operator is used to transport particles from a prior to its posterior after a new observation.

Bayesian Inference Meta-Learning +1

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