David P. Wipf

ORCID

Publication Activity (10 Years)

Years Active: 2003-2022
Publications (10 Years): 42

Top Venues

University of California, San Diego, USA (PHD)

Education History

University of California, San Diego, USA

PHD

Publications

Yijia Zheng, Tong He, Yixuan Qiu, David P. Wipf
Learning Manifold Dimensions with Conditional Variational Autoencoders. NeurIPS (2022)
Hongjoon Ahn, Yongyi Yang, Quan Gan, Taesup Moon, David P. Wipf
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks. NeurIPS (2022)
Kounianhua Du, Weinan Zhang, Ruiwen Zhou, Yangkun Wang, Xilong Zhao, Jiarui Jin, Quan Gan, Zheng Zhang, David P. Wipf
Learning Enhanced Representation for Tabular Data via Neighborhood Propagation. NeurIPS (2022)
Jian Yao, Yuxin Hong, Chiyu Wang, Tianjun Xiao, Tong He, Francesco Locatello, David P. Wipf, Yanwei Fu, Zheng Zhang
Self-supervised Amodal Video Object Segmentation. NeurIPS (2022)
Yongyi Yang, Zengfeng Huang, David P. Wipf
Transformers from an Optimization Perspective. NeurIPS (2022)
Qitian Wu, Wentao Zhao, Zenan Li, David P. Wipf, Junchi Yan
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification. NeurIPS (2022)
Bin Dai, Wenliang Li, David P. Wipf
On the Value of Infinite Gradients in Variational Autoencoder Models. NeurIPS (2021)
Bin Dai, Ziyu Wang, David P. Wipf
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. ICML (2020)
Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu
Further Analysis of Outlier Detection with Deep Generative Models. NeurIPS (2020)
Ziyu Wang, Bin Dai, David P. Wipf, Jun Zhu
Further Analysis of Outlier Detection with Deep Generative Models. CoRR (2020)
Bin Dai, David P. Wipf
Diagnosing and Enhancing VAE Models. ICLR (Poster) (2019)
Bin Dai, David P. Wipf
Diagnosing and Enhancing VAE Models. CoRR (2019)
Bin Dai, Ziyu Wang, David P. Wipf
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse. CoRR (2019)
Wenqi Ren, Jiaolong Yang, Senyou Deng, David P. Wipf, Xiaochun Cao, Xin Tong
Face Video Deblurring Using 3D Facial Priors. ICCV (2019)
Kaixuan Wei, Jiaolong Yang, Ying Fu, David P. Wipf, Hua Huang
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements. CVPR (2019)
Kaixuan Wei, Jiaolong Yang, Ying Fu, David P. Wipf, Hua Huang
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements. CoRR (2019)
Yu Wang, Bin Dai, Gang Hua, John Aston, David P. Wipf
Recurrent Variational Autoencoders for Learning Nonlinear Generative Models in the Presence of Outliers. IEEE J. Sel. Top. Signal Process. 12 (6) (2018)
Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf
Revisiting Deep Intrinsic Image Decompositions. CVPR (2018)
Bin Dai, Yu Wang, John Aston, Gang Hua, David P. Wipf
Connections with Robust PCA and the Role of Emergent Sparsity in Variational Autoencoder Models. J. Mach. Learn. Res. 19 (2018)
Qingnan Fan, Jiaolong Yang, David P. Wipf, Baoquan Chen, Xin Tong
Image smoothing via unsupervised learning. ACM Trans. Graph. 37 (6) (2018)
Bin Dai, Chen Zhu, Baining Guo, David P. Wipf
Compressing Neural Networks using the Variational Information Bottleneck. ICML (2018)
Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf
Building Invariances Into Sparse Subspace Clustering. IEEE Trans. Signal Process. 66 (2) (2018)
Bin Dai, Chen Zhu, David P. Wipf
Compressing Neural Networks using the Variational Information Bottleneck. CoRR (2018)
Qingnan Fan, Jiaolong Yang, David P. Wipf, Baoquan Chen, Xin Tong
Image Smoothing via Unsupervised Learning. CoRR (2018)
Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. CoRR (2017)
Yu Wang, Bin Dai, Gang Hua, John Aston, David P. Wipf
Green Generative Modeling: Recycling Dirty Data using Recurrent Variational Autoencoders. UAI (2017)
Bin Dai, Yu Wang, John Aston, Gang Hua, David P. Wipf
Veiled Attributes of the Variational Autoencoder. CoRR (2017)
Dong Chen, Xudong Cao, David P. Wipf, Fang Wen, Jian Sun
An Efficient Joint Formulation for Bayesian Face Verification. IEEE Trans. Pattern Anal. Mach. Intell. 39 (1) (2017)
Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf
Data-Dependent Sparsity for Subspace Clustering. UAI (2017)
Qingnan Fan, Jiaolong Yang, Gang Hua, Baoquan Chen, David P. Wipf
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing. ICCV (2017)
Hao He, Bo Xin, Satoshi Ikehata, David P. Wipf
From Bayesian Sparsity to Gated Recurrent Nets. NIPS (2017)
Hao He, Bo Xin, David P. Wipf
From Bayesian Sparsity to Gated Recurrent Nets. CoRR (2017)
Qingnan Fan, David P. Wipf, Gang Hua, Baoquan Chen
Revisiting Deep Image Smoothing and Intrinsic Image Decomposition. CoRR (2017)
Tianlin Shi, Forest Agostinelli, Matthew Staib, David P. Wipf, Thomas Moscibroda
Improving Survey Aggregation with Sparsely Represented Signals. KDD (2016)
Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf, Baoyuan Wang
Maximal Sparsity with Deep Networks? NIPS (2016)
Tae-Hyun Oh, Yasuyuki Matsushita, In-So Kweon, David P. Wipf
A Pseudo-Bayesian Algorithm for Robust PCA. NIPS (2016)
Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf
Maximal Sparsity with Deep Networks? CoRR (2016)
Bo Xin, Yizhou Wang, Wen Gao, David P. Wipf
Exploring Algorithmic Limits of Matrix Rank Minimization Under Affine Constraints. IEEE Trans. Signal Process. 64 (19) (2016)
David P. Wipf, Yue Dong, Bo Xin
Subspace Clustering with a Twist. UAI (2016)
Zhiming Zhou, Guojun Chen, Yue Dong, David P. Wipf, Yong Yu, John M. Snyder, Xin Tong
Sparse-as-possible SVBRDF acquisition. ACM Trans. Graph. 35 (6) (2016)
Wei Chen, David P. Wipf, Yu Wang, Yang Liu, Ian J. Wassell
Simultaneous Bayesian Sparse Approximation With Structured Sparse Models. IEEE Trans. Signal Process. 64 (23) (2016)
David P. Wipf
Analysis of Variational Bayesian Factorizations for Sparse and Low-Rank Estimation. ICML (2016)
David P. Wipf, Jeong-Min Yun, Qing Ling
Augmented Bayesian Compressive Sensing. DCC (2015)
Bo Xin, David P. Wipf
Pushing the Limits of Affine Rank Minimization by Adapting Probabilistic PCA. ICML (2015)
Huan Yang, Baoyuan Wang, Stephen Lin, David P. Wipf, Minyi Guo, Baining Guo
Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders. CoRR (2015)
Tae Hyun Oh, David P. Wipf, Yasuyuki Matsushita, In-So Kweon
New Design Criteria for Robust PCA and a Compliant Bayesian-Inspired Algorithm. CoRR (2015)
Yu Wang, David P. Wipf, Jeong-Min Yun, Wei Chen, Ian J. Wassell
Clustered Sparse Bayesian Learning. UAI (2015)
Yu Wang, David P. Wipf, Qing Ling, Wei Chen, Ian J. Wassell
Multi-Task Learning for Subspace Segmentation. ICML (2015)
Huan Yang, Baoyuan Wang, Stephen Lin, David P. Wipf, Minyi Guo, Baining Guo
Unsupervised Extraction of Video Highlights via Robust Recurrent Auto-Encoders. ICCV (2015)
Yi Wu, David P. Wipf, Jeong-Min Yun
Understanding and Evaluating Sparse Linear Discriminant Analysis. AISTATS (2015)
Bo Xin, David P. Wipf
Exploring Algorithmic Limits of Matrix Rank Minimization under Affine Constraints. CoRR (2014)
Haichao Zhang, David P. Wipf, Yanning Zhang
Multi-Observation Blind Deconvolution with an Adaptive Sparse Prior. IEEE Trans. Pattern Anal. Mach. Intell. 36 (8) (2014)
David P. Wipf
Non-Convex Rank Minimization via an Empirical Bayesian Approach. CoRR (2014)
Yu Wang, David P. Wipf, Wei Chen, Ian J. Wassell
Exploiting the convex-concave penalty for tracking: A novel dynamic reweighted sparse Bayesian learning algorithm. ICASSP (2014)
Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa
Photometric Stereo Using Sparse Bayesian Regression for General Diffuse Surfaces. IEEE Trans. Pattern Anal. Mach. Intell. 36 (9) (2014)
David P. Wipf, Haichao Zhang
Revisiting Bayesian blind deconvolution. J. Mach. Learn. Res. 15 (1) (2014)
Carsten Stahlhut, Hagai Thomas Attias, Kensuke Sekihara, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan
A hierarchical Bayesian M/EEG imagingmethod correcting for incomplete spatio-temporal priors. ISBI (2013)
Haichao Zhang, David P. Wipf, Yanning Zhang
Multi-image Blind Deblurring Using a Coupled Adaptive Sparse Prior. CVPR (2013)
Quannan Li, Jingdong Wang, David P. Wipf, Zhuowen Tu
Fixed-Point Model For Structured Labeling. ICML (1) (2013)
David P. Wipf, Haichao Zhang
Revisiting Bayesian Blind Deconvolution CoRR (2013)
Haichao Zhang, David P. Wipf
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior. CoRR (2013)
Xudong Cao, David P. Wipf, Fang Wen, Genquan Duan, Jian Sun
A Practical Transfer Learning Algorithm for Face Verification. ICCV (2013)
Haichao Zhang, David P. Wipf
Non-Uniform Camera Shake Removal Using a Spatially-Adaptive Sparse Penalty. NIPS (2013)
David P. Wipf, Haichao Zhang
Analysis of Bayesian Blind Deconvolution. EMMCVPR (2013)
David P. Wipf, Yi Wu
Dual-Space Analysis of the Sparse Linear Model. NIPS (2012)
Liwei Wang, Yin Li, Jiaya Jia, Jian Sun, David P. Wipf, James M. Rehg
Learning sparse covariance patterns for natural scenes. CVPR (2012)
Haichao Zhang, David P. Wipf, Yanning Zhang
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images CoRR (2012)
David P. Wipf, Yi Wu
Dual-Space Analysis of the Sparse Linear Model CoRR (2012)
Satoshi Ikehata, David P. Wipf, Yasuyuki Matsushita, Kiyoharu Aizawa
Robust photometric stereo using sparse regression. CVPR (2012)
David P. Wipf
Non-Convex Rank Minimization via an Empirical Bayesian Approach CoRR (2012)
David P. Wipf
Non-Convex Rank Minimization via an Empirical Bayesian Approach. UAI (2012)
Julia P. Owen, David P. Wipf, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan
Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data. NeuroImage 60 (1) (2012)
David P. Wipf
Sparse Estimation with Structured Dictionaries. NIPS (2011)
David P. Wipf, Bhaskar D. Rao, Srikantan S. Nagarajan
Latent Variable Bayesian Models for Promoting Sparsity. IEEE Trans. Inf. Theory 57 (9) (2011)
David P. Wipf, Julia P. Owen, Hagai Thomas Attias, Kensuke Sekihara, Srikantan S. Nagarajan
Robust Bayesian estimation of the location, orientation, and time course of multiple correlated neural sources using MEG. NeuroImage 49 (1) (2010)
Matthias W. Seeger, David P. Wipf
Variational Bayesian Inference Techniques. IEEE Signal Process. Mag. 27 (6) (2010)
Carsten Stahlhut, Hagai Attias, David P. Wipf, Lars Kai Hansen, Srikantan S. Nagarajan
Sparse Spatio-temporal Inference of Electromagnetic Brain Sources. MLMI (2010)
David P. Wipf, Srikantan S. Nagarajan
Methods for Finding Sparse Solutions. IEEE J. Sel. Top. Signal Process. 4 (2) (2010)
Julia P. Owen, David P. Wipf, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan
Robust Methods for Reconstructing Brain Activity and Functional Connectivity Between Brain Sources with MEG/EEG Data. ISBI (2009)
David P. Wipf, Srikantan S. Nagarajan
Sparse Estimation Using General Likelihoods and Non-Factorial Priors. NIPS (2009)
David P. Wipf, Srikantan S. Nagarajan
A unified Bayesian framework for MEG/EEG source imaging. NeuroImage 44 (3) (2009)
David P. Wipf, Julia P. Owen, Hagai Attias, Kensuke Sekihara, Srikantan S. Nagarajan
Estimating the Location and Orientation of Complex, Correlated Neural Activity using MEG. NIPS (2008)
David P. Wipf, Jason A. Palmer, Bhaskar D. Rao, Kenneth Kreutz-Delgado
Performance Evaluation of Latent Variable Models with Sparse Priors. ICASSP (2) (2007)
David P. Wipf, Bhaskar D. Rao
An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem. IEEE Trans. Signal Process. 55 (7-2) (2007)
David P. Wipf, Srikantan S. Nagarajan
A New View of Automatic Relevance Determination. NIPS (2007)
Joel C. McCall, David P. Wipf, Mohan M. Trivedi, Bhaskar D. Rao
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. IEEE Trans. Intell. Transp. Syst. 8 (3) (2007)
David P. Wipf, Srikantan S. Nagarajan
Beamforming using the relevance vector machine. ICML (2007)
David P. Wipf, Rey Ramírez, Jason A. Palmer, Scott Makeig, Bhaskar D. Rao
Analysis of Empirical Bayesian Methods for Neuroelectromagnetic Source Localization. NIPS (2006)
David P. Wipf, Bhaskar D. Rao
Comparing the Effects of Different Weight Distributions on Finding Sparse Representations. NIPS (2005)
Joel C. McCall, Mohan M. Trivedi, David P. Wipf, Bhaskar D. Rao
Lane Change Intent Analysis Using Robust Operators and Sparse Bayesian Learning. CVPR Workshops (2005)
Jason A. Palmer, David P. Wipf, Kenneth Kreutz-Delgado, Bhaskar D. Rao
Variational EM Algorithms for Non-Gaussian Latent Variable Models. NIPS (2005)
David P. Wipf, Bhaskar D. Rao
Sparse Bayesian learning for basis selection. IEEE Trans. Signal Process. 52 (8) (2004)
David P. Wipf, Bhaskar D. Rao
L_0-norm Minimization for Basis Selection. NIPS (2004)
David P. Wipf, Bhaskar D. Rao
diversity measures. ICASSP (2) (2004)
David P. Wipf, Bhaskar D. Rao
Bayesian learning for sparse signal reconstruction. ICASSP (6) (2003)
David P. Wipf, Jason A. Palmer, Bhaskar D. Rao
Perspectives on Sparse Bayesian Learning. NIPS (2003)

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Publication Activity (10 Years)

Top Topics

Top Venues

David P. Wipf