Search Results for author: David Wipf

Found 43 papers, 16 papers with code

Handling Distribution Shifts on Graphs: An Invariance Perspective

1 code implementation ICLR 2022 Qitian Wu, Hengrui Zhang, Junchi Yan, David Wipf

There is increasing evidence suggesting neural networks' sensitivity to distribution shifts, so that research on out-of-distribution (OOD) generalization comes into the spotlight.

GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks

no code implementations1 Feb 2022 Yixuan He, Quan Gan, David Wipf, Gesine Reinert, Junchi Yan, Mihai Cucuringu

Recovering global rankings from pairwise comparisons is an important problem with many applications, ranging from time synchronization to sports team ranking.

On the Value of Infinite Gradients in Variational Autoencoder Models

no code implementations NeurIPS 2021 Bin Dai, Li Wenliang, David Wipf

A number of recent studies of continuous variational autoencoder (VAE) models have noted, either directly or indirectly, the tendency of various parameter gradients to drift towards infinity during training.

Implicit vs Unfolded Graph Neural Networks

no code implementations12 Nov 2021 Yongyi Yang, Tang Liu, Yangkun Wang, Zengfeng Huang, David Wipf

It has been observed that graph neural networks (GNN) sometimes struggle to maintain a healthy balance between the efficient modeling long-range dependencies across nodes while avoiding unintended consequences such oversmoothed node representations or sensitivity to spurious edges.

Graph Attention Node Classification

Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features

no code implementations26 Oct 2021 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

For supervised learning with tabular data, decision tree ensembles produced via boosting techniques generally dominate real-world applications involving iid training/test sets.

Why Propagate Alone? Parallel Use of Labels and Features on Graphs

no code implementations ICLR 2022 Yangkun Wang, Jiarui Jin, Weinan Zhang, Yongyi Yang, Jiuhai Chen, Quan Gan, Yong Yu, Zheng Zhang, Zengfeng Huang, David Wipf

In this regard, it has recently been proposed to use a randomly-selected portion of the training labels as GNN inputs, concatenated with the original node features for making predictions on the remaining labels.

Node Property Prediction

Inductive Relation Prediction Using Analogy Subgraph Embeddings

no code implementations ICLR 2022 Jiarui Jin, Yangkun Wang, Kounianhua Du, Weinan Zhang, Zheng Zhang, David Wipf, Yong Yu, Quan Gan

Prevailing methods for relation prediction in heterogeneous graphs aim at learning latent representations (i. e., embeddings) of observed nodes and relations, and thus are limited to the transductive setting where the relation types must be known during training.

Inductive Relation Prediction

ESCo: Towards Provably Effective and Scalable Contrastive Representation Learning

no code implementations29 Sep 2021 Hengrui Zhang, Qitian Wu, Shaofeng Zhang, Junchi Yan, David Wipf, Philip S. Yu

In this paper, we propose ESCo (Effective and Scalable Contrastive), a new contrastive framework which is essentially an instantiation of the Information Bottleneck principle under self-supervised learning settings.

Contrastive Learning Representation Learning +1

Convergent Boosted Smoothing for Modeling GraphData with Tabular Node Features

no code implementations ICLR 2022 Jiuhai Chen, Jonas Mueller, Vassilis N. Ioannidis, Soji Adeshina, Yangkun Wang, Tom Goldstein, David Wipf

Many practical modeling tasks require making predictions using tabular data composed of heterogeneous feature types (e. g., text-based, categorical, continuous, etc.).

A Closer Look at Distribution Shifts and Out-of-Distribution Generalization on Graphs

no code implementations29 Sep 2021 Mucong Ding, Kezhi Kong, Jiuhai Chen, John Kirchenbauer, Micah Goldblum, David Wipf, Furong Huang, Tom Goldstein

We observe that in most cases, we need both a suitable domain generalization algorithm and a strong GNN backbone model to optimize out-of-distribution test performance.

Domain Generalization Graph Classification +1

Learning Hierarchical Graph Neural Networks for Image Clustering

2 code implementations ICCV 2021 Yifan Xing, Tong He, Tianjun Xiao, Yongxin Wang, Yuanjun Xiong, Wei Xia, David Wipf, Zheng Zhang, Stefano Soatto

Our hierarchical GNN uses a novel approach to merge connected components predicted at each level of the hierarchy to form a new graph at the next level.

Face Clustering

Sparse Multi-Path Corrections in Fringe Projection Profilometry

no code implementations CVPR 2021 Yu Zhang, Daniel Lau, David Wipf

Three-dimensional scanning by means of structured light illumination is an active imaging technique involving projecting and capturing a series of striped patterns and then using the observed warping of stripes to reconstruct the target object's surface through triangulating each pixel in the camera to a unique projector coordinate corresponding to a particular feature in the projected patterns.

Bag of Tricks for Node Classification with Graph Neural Networks

1 code implementation24 Mar 2021 Yangkun Wang, Jiarui Jin, Weinan Zhang, Yong Yu, Zheng Zhang, David Wipf

Over the past few years, graph neural networks (GNN) and label propagation-based methods have made significant progress in addressing node classification tasks on graphs.

Classification General Classification +2

Graph Neural Networks Inspired by Classical Iterative Algorithms

1 code implementation10 Mar 2021 Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, David Wipf

Despite the recent success of graph neural networks (GNN), common architectures often exhibit significant limitations, including sensitivity to oversmoothing, long-range dependencies, and spurious edges, e. g., as can occur as a result of graph heterophily or adversarial attacks.

Node Classification

A Biased Graph Neural Network Sampler with Near-Optimal Regret

1 code implementation NeurIPS 2021 Qingru Zhang, David Wipf, Quan Gan, Le Song

Graph neural networks (GNN) have recently emerged as a vehicle for applying deep network architectures to graph and relational data.

Explore with Dynamic Map: Graph Structured Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Sijin Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Tong He, Yong Yu, Zheng Zhang, Alex Smola

In reinforcement learning, a map with states and transitions built based on historical trajectories is often helpful in exploration and exploitation.

reinforcement-learning

Regioned Episodic Reinforcement Learning

no code implementations1 Jan 2021 Jiarui Jin, Cong Chen, Ming Zhou, Weinan Zhang, Rasool Fakoor, David Wipf, Yong Yu, Jun Wang, Alex Smola

Goal-oriented reinforcement learning algorithms are often good at exploration, not exploitation, while episodic algorithms excel at exploitation, not exploration.

reinforcement-learning

Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings

1 code implementation14 Dec 2020 Qipeng Guo, Zhijing Jin, Ziyu Wang, Xipeng Qiu, Weinan Zhang, Jun Zhu, Zheng Zhang, David Wipf

Cycle-consistent training is widely used for jointly learning a forward and inverse mapping between two domains of interest without the cumbersome requirement of collecting matched pairs within each domain.

Knowledge Graphs Text Generation

Further Analysis of Outlier Detection with Deep Generative Models

1 code implementation NeurIPS 2020 Ziyu Wang, Bin Dai, David Wipf, Jun Zhu

The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling.

Outlier Detection

CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training

2 code implementations ACL (WebNLG, INLG) 2020 Qipeng Guo, Zhijing Jin, Xipeng Qiu, Wei-Nan Zhang, David Wipf, Zheng Zhang

Due to the difficulty and high cost of data collection, the supervised data available in the two fields are usually on the magnitude of tens of thousands, for example, 18K in the WebNLG~2017 dataset after preprocessing, which is far fewer than the millions of data for other tasks such as machine translation.

Graph Generation Knowledge Graphs +2

The Usual Suspects? Reassessing Blame for VAE Posterior Collapse

no code implementations ICML 2020 Bin Dai, Ziyu Wang, David Wipf

In narrow asymptotic settings Gaussian VAE models of continuous data have been shown to possess global optima aligned with ground-truth distributions.

Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements

1 code implementation CVPR 2019 Kaixuan Wei, Jiaolong Yang, Ying Fu, David Wipf, Hua Huang

Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems.

Reflection Removal

Diagnosing and Enhancing VAE Models

4 code implementations ICLR 2019 Bin Dai, David Wipf

Although variational autoencoders (VAEs) represent a widely influential deep generative model, many aspects of the underlying energy function remain poorly understood.

Image Smoothing via Unsupervised Learning

1 code implementation7 Nov 2018 Qingnan Fan, Jiaolong Yang, David Wipf, Baoquan Chen, Xin Tong

Image smoothing represents a fundamental component of many disparate computer vision and graphics applications.

Image Manipulation image smoothing

Compressing Neural Networks using the Variational Information Bottelneck

1 code implementation ICML 2018 Bin Dai, Chen Zhu, Baining Guo, David Wipf

Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.

Compressing Neural Networks using the Variational Information Bottleneck

1 code implementation ICML 2018 Bin Dai, Chen Zhu, David Wipf

Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture.

A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing

1 code implementation ICCV 2017 Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

This paper proposes a deep neural network structure that exploits edge information in addressing representative low-level vision tasks such as layer separation and image filtering.

image smoothing Reflection Removal +1

Hidden Talents of the Variational Autoencoder

1 code implementation16 Jun 2017 Bin Dai, Yu Wang, John Aston, Gang Hua, David Wipf

Variational autoencoders (VAE) represent a popular, flexible form of deep generative model that can be stochastically fit to samples from a given random process using an information-theoretic variational bound on the true underlying distribution.

Dimensionality Reduction

From Bayesian Sparsity to Gated Recurrent Nets

no code implementations NeurIPS 2017 Hao He, Bo Xin, David Wipf

The iterations of many first-order algorithms, when applied to minimizing common regularized regression functions, often resemble neural network layers with pre-specified weights.

Revisiting Deep Intrinsic Image Decompositions

no code implementations CVPR 2018 Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David Wipf

While invaluable for many computer vision applications, decomposing a natural image into intrinsic reflectance and shading layers represents a challenging, underdetermined inverse problem.

A Pseudo-Bayesian Algorithm for Robust PCA

no code implementations NeurIPS 2016 Tae-Hyun Oh, Yasuyuki Matsushita, In Kweon, David Wipf

Commonly used in many applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers.

Maximal Sparsity with Deep Networks?

no code implementations NeurIPS 2016 Bo Xin, Yizhou Wang, Wen Gao, David Wipf

The iterations of many sparse estimation algorithms are comprised of a fixed linear filter cascaded with a thresholding nonlinearity, which collectively resemble a typical neural network layer.

Pseudo-Bayesian Robust PCA: Algorithms and Analyses

no code implementations7 Dec 2015 Tae-Hyun Oh, Yasuyuki Matsushita, In So Kweon, David Wipf

Commonly used in computer vision and other applications, robust PCA represents an algorithmic attempt to reduce the sensitivity of classical PCA to outliers.

Matrix Completion

Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders

no code implementations ICCV 2015 Huan Yang, Baoyuan Wang, Stephen Lin, David Wipf, Minyi Guo, Baining Guo

With the growing popularity of short-form video sharing platforms such as \em{Instagram} and \em{Vine}, there has been an increasing need for techniques that automatically extract highlights from video.

Non-Convex Rank Minimization via an Empirical Bayesian Approach

no code implementations9 Aug 2014 David Wipf

In many applications that require matrix solutions of minimal rank, the underlying cost function is non-convex leading to an intractable, NP-hard optimization problem.

Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints

no code implementations10 Jun 2014 Bo Xin, David Wipf

While elegant theoretical conditions elucidate when this replacement is likely to be successful, they are highly restrictive and convex algorithms fail when the ambient rank is too high or when the constraint set is poorly structured.

Collaborative Filtering Matrix Completion

Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty

no code implementations NeurIPS 2013 Haichao Zhang, David Wipf

Typical blur from camera shake often deviates from the standard uniform convolutional assumption, in part because of problematic rotations which create greater blurring away from some unknown center point.

Bayesian Inference Deblurring

Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior

no code implementations17 Jun 2013 Haichao Zhang, David Wipf

Typical blur from camera shake often deviates from the standard uniform convolutional script, in part because of problematic rotations which create greater blurring away from some unknown center point.

Bayesian Inference Deblurring

Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior

no code implementations CVPR 2013 Haichao Zhang, David Wipf, Yanning Zhang

This paper presents a robust algorithm for estimating a single latent sharp image given multiple blurry and/or noisy observations.

Deblurring

Revisiting Bayesian Blind Deconvolution

no code implementations10 May 2013 David Wipf, Haichao Zhang

Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation.

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