
Learning and Evaluating Representations for Deep Oneclass Classification
We present a twostage framework for deep oneclass classification. We f...
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PseudoSeg: Designing Pseudo Labels for Semantic Segmentation
Recent advances in semisupervised learning (SSL) demonstrate that a com...
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iMix: A Strategy for Regularizing Contrastive Representation Learning
Contrastive representation learning has shown to be an effective way of ...
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Interpretable Sequence Learning for COVID19 Forecasting
We propose a novel approach that integrates machine learning into compar...
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Kernel Stein Generative Modeling
We are interested in gradientbased Explicit Generative Modeling where s...
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A Simple SemiSupervised Learning Framework for Object Detection
Semisupervised learning (SSL) has promising potential for improving the...
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Unsupervised Program Synthesis for Images using TreeStructured LSTM
Program synthesis has recently emerged as a promising approach to the im...
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FixMatch: Simplifying SemiSupervised Learning with Consistency and Confidence
Semisupervised learning (SSL) provides an effective means of leveraging...
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Learned Interpolation for 3D Generation
In order to generate novel 3D shapes with machine learning, one must all...
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Getting Topology and Point Cloud Generation to Mesh
In this work, we explore the idea that effective generative models for p...
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On ConceptBased Explanations in Deep Neural Networks
Deep neural networks (DNNs) build highlevel intelligence on lowlevel r...
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Developing Creative AI to Generate Sculptural Objects
We explore the intersection of human and machine creativity by generatin...
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LBS Autoencoder: Selfsupervised Fitting of Articulated Meshes to Point Clouds
We present LBSAE; a selfsupervised autoencoding algorithm for fitting ...
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Implicit Kernel Learning
Kernels are powerful and versatile tools in machine learning and statist...
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Kernel Changepoint Detection with Auxiliary Deep Generative Models
Detecting the emergence of abrupt property changes in time series is a c...
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Hallucinating Point Cloud into 3D Sculptural Object
Our team of artists and machine learning researchers designed a creative...
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Point Cloud GAN
Generative Adversarial Networks (GAN) can achieve promising performance ...
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Adversarial Geometry and Lighting using a Differentiable Renderer
Many machine learning classifiers are vulnerable to adversarial attacks,...
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Beyond Pixel NormBalls: Parametric Adversaries using an Analytically Differentiable Renderer
Many machine learning image classifiers are vulnerable to adversarial at...
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Nonparametric Density Estimation under Adversarial Losses
We study minimax convergence rates of nonparametric density estimation u...
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PedestrianSynthesisGAN: Generating Pedestrian Data in Real Scene and Beyond
Stateoftheart pedestrian detection models have achieved great success...
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Sobolev GAN
We propose a new Integral Probability Metric (IPM) between distributions...
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MMD GAN: Towards Deeper Understanding of Moment Matching Network
Generative moment matching network (GMMN) is a deep generative model tha...
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Datadriven Random Fourier Features using Stein Effect
Largescale kernel approximation is an important problem in machine lear...
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One Network to Solve Them All  Solving Linear Inverse Problems using Deep Projection Models
While deep learning methods have achieved stateoftheart performance i...
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Annealing Gaussian into ReLU: a New Sampling Strategy for LeakyReLU RBM
Restricted Boltzmann Machine (RBM) is a bipartite graphical model that i...
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Rivalry of Two Families of Algorithms for MemoryRestricted Streaming PCA
We study the problem of recovering the subspace spanned by the first k p...
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ChunLiang Li
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