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06/16/2023
From Hypergraph Energy Functions to Hypergraph Neural Networks
Hypergraphs are a powerful abstraction for representing higher-order int...
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06/14/2023
NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification
Graph neural networks have been extensively studied for learning with in...
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02/23/2023
Learning Manifold Dimensions with Conditional Variational Autoencoders
Although the variational autoencoder (VAE) and its conditional extension...
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01/23/2023
DIFFormer: Scalable (Graph) Transformers Induced by Energy Constrained Diffusion
Real-world data generation often involves complex inter-dependencies amo...
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01/18/2023
ReFresh: Reducing Memory Access from Exploiting Stable Historical Embeddings for Graph Neural Network Training
A key performance bottleneck when training graph neural network (GNN) mo...
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10/23/2022
Self-supervised Amodal Video Object Segmentation
Amodal perception requires inferring the full shape of an object that is...
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06/22/2022
Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks
Heterogeneous graph neural networks (GNNs) achieve strong performance on...
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06/16/2022
A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features
Graph Neural Networks (GNNs) with numerical node features and graph stru...
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05/27/2022
Transformers from an Optimization Perspective
Deep learning models such as the Transformer are often constructed by he...
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02/01/2022
GNNRank: Learning Global Rankings from Pairwise Comparisons via Directed Graph Neural Networks
Recovering global rankings from pairwise comparisons is an important pro...
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11/12/2021
Implicit vs Unfolded Graph Neural Networks
It has been observed that graph neural networks (GNN) sometimes struggle...
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10/26/2021
Convergent Boosted Smoothing for Modeling Graph Data with Tabular Node Features
For supervised learning with tabular data, decision tree ensembles produ...
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06/23/2021
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks
We introduce a conceptually simple yet effective model for self-supervis...
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03/10/2021
Graph Neural Networks Inspired by Classical Iterative Algorithms
Despite the recent success of graph neural networks (GNN), common archit...
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03/01/2021
A Biased Graph Neural Network Sampler with Near-Optimal Regret
Graph neural networks (GNN) have recently emerged as a vehicle for apply...
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12/14/2020
Fork or Fail: Cycle-Consistent Training with Many-to-One Mappings
Cycle-consistent training is widely used for jointly learning a forward ...
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10/25/2020
Further Analysis of Outlier Detection with Deep Generative Models
The recent, counter-intuitive discovery that deep generative models (DGM...
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06/08/2020
CycleGT: Unsupervised Graph-to-Text and Text-to-Graph Generation via Cycle Training
Two important tasks at the intersection of knowledge graphs and natural ...
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12/23/2019
The Usual Suspects? Reassessing Blame for VAE Posterior Collapse
In narrow asymptotic settings Gaussian VAE models of continuous data hav...
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04/01/2019
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements
Removing undesirable reflections from a single image captured through a ...
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03/14/2019
Diagnosing and Enhancing VAE Models
Although variational autoencoders (VAEs) represent a widely influential ...
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11/07/2018
Image Smoothing via Unsupervised Learning
Image smoothing represents a fundamental component of many disparate com...
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02/28/2018
Compressing Neural Networks using the Variational Information Bottleneck
Neural networks can be compressed to reduce memory and computational req...
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08/11/2017
A Generic Deep Architecture for Single Image Reflection Removal and Image Smoothing
This paper proposes a deep neural network structure that exploits edge i...
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01/11/2017
Revisiting Deep Intrinsic Image Decompositions
While invaluable for many computer vision applications, decomposing a na...
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12/07/2015
Pseudo-Bayesian Robust PCA: Algorithms and Analyses
Commonly used in computer vision and other applications, robust PCA repr...
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10/06/2015
Unsupervised Extraction of Video Highlights Via Robust Recurrent Auto-encoders
With the growing popularity of short-form video sharing platforms such a...
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08/09/2014
Non-Convex Rank Minimization via an Empirical Bayesian Approach
In many applications that require matrix solutions of minimal rank, the ...
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06/17/2013
Non-Uniform Blind Deblurring with a Spatially-Adaptive Sparse Prior
Typical blur from camera shake often deviates from the standard uniform ...
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05/10/2013
Revisiting Bayesian Blind Deconvolution
Blind deconvolution involves the estimation of a sharp signal or image g...
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09/19/2012
Image Super-Resolution via Sparse Bayesian Modeling of Natural Images
Image super-resolution (SR) is one of the long-standing and active topic...
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07/10/2012