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Found 32 papers, 5 papers with code
Unbiased Learning of Deep Generative Models with Structured Discrete Representations
By composing graphical models with deep learning architectures, we learn generative models with the strengths of both frameworks.
Learning Consistent Deep Generative Models from Sparsely Labeled Data
We consider training deep generative models toward two simultaneous goals: discriminative classification and generative modeling using an explicit likelihood.
Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints
We investigate how fairness relaxations scale to flexible classifiers like deep neural networks for images and text.
Learning Consistent Deep Generative Models from Sparse Data via Prediction Constraints
We develop a new framework for learning variational autoencoders and other deep generative models that balances generative and discriminative goals.
Clouds of Oriented Gradients for 3D Detection of Objects, Surfaces, and Indoor Scene Layouts
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #22 on
3D Object Detection
on SUN-RGBD val
3D Scene Reconstruction with Multi-layer Depth and Epipolar Transformers
We tackle the problem of automatically reconstructing a complete 3D model of a scene from a single RGB image.
A Fusion Approach for Multi-Frame Optical Flow Estimation
To date, top-performing optical flow estimation methods only take pairs of consecutive frames into account.
3D Object Detection With Latent Support Surfaces
We develop a 3D object detection algorithm that uses latent support surfaces to capture contextual relationships in indoor scenes.
Ranked #24 on
3D Object Detection
on SUN-RGBD val
Prediction-Constrained Topic Models for Antidepressant Recommendation
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks.
Bayesian Paragraph Vectors
Word2vec (Mikolov et al., 2013) has proven to be successful in natural language processing by capturing the semantic relationships between different words.
From Patches to Images: A Nonparametric Generative Model
Our model is based on a novel, variational interpretation of the popular expected patch log-likelihood (EPLL) method as a model for randomly positioned grids of image patches.
Cascaded Scene Flow Prediction using Semantic Segmentation
Given two consecutive frames from a pair of stereo cameras, 3D scene flow methods simultaneously estimate the 3D geometry and motion of the observed scene.
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful.
Fast Learning of Clusters and Topics via Sparse Posteriors
Mixture models and topic models generate each observation from a single cluster, but standard variational posteriors for each observation assign positive probability to all possible clusters.
Three-Dimensional Object Detection and Layout Prediction Using Clouds of Oriented Gradients
We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes.
Ranked #25 on
3D Object Detection
on SUN-RGBD val
Layered RGBD Scene Flow Estimation
As consumer depth sensors become widely available, estimating scene flow from RGBD sequences has received increasing attention.
Joint modeling of multiple time series via the beta process with application to motion capture segmentation
We propose a Bayesian nonparametric approach to the problem of jointly modeling multiple related time series.
A Fully-Connected Layered Model of Foreground and Background Flow
Layered models allow scene segmentation and motion estimation to be formulated together and to inform one another.
From Deformations to Parts: Motion-based Segmentation of 3D Objects
We develop a method for discovering the parts of an articulated object from aligned meshes capturing various three-dimensional (3D) poses.
Minimization of Continuous Bethe Approximations: A Positive Variation
We develop convergent minimization algorithms for Bethe variational approximations which explicitly constrain marginal estimates to families of valid distributions.
Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data
Applications of Bayesian nonparametric methods require learning and inference algorithms which efficiently explore models of unbounded complexity.
Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes
Variational methods provide a computationally scalable alternative to Monte Carlo methods for large-scale, Bayesian nonparametric learning.
The Doubly Correlated Nonparametric Topic Model
Topic models are learned via a statistical model of variation within document collections, but designed to extract meaningful semantic structure.
Spatial distance dependent Chinese restaurant processes for image segmentation
The distance dependent Chinese restaurant process (ddCRP) was recently introduced to accommodate random partitions of non-exchangeable data.
Global seismic monitoring as probabilistic inference
The International Monitoring System (IMS) is a global network of sensors whose purpose is to identify potential violations of the Comprehensive Nuclear-Test-Ban Treaty (CTBT), primarily through detection and localization of seismic events.
Layered image motion with explicit occlusions, temporal consistency, and depth ordering
We present a new probabilistic model of optical flow in layers that addresses many of the shortcomings of previous approaches.
Bayesian Nonparametric Inference of Switching Linear Dynamical Systems
Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.
Sharing Features among Dynamical Systems with Beta Processes
We propose a Bayesian nonparametric approach to relating multiple time series via a set of latent, dynamical behaviors.
A sticky HDP-HMM with application to speaker diarization
To address this problem, we take a Bayesian nonparametric approach to speaker diarization that builds on the hierarchical Dirichlet process hidden Markov model (HDP-HMM) of Teh et al. [J. Amer.
Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes
We develop a statistical framework for the simultaneous, unsupervised segmentation and discovery of visual object categories from image databases.
Nonparametric Bayesian Learning of Switching Linear Dynamical Systems
Many nonlinear dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes.
Loop Series and Bethe Variational Bounds in Attractive Graphical Models
Variational methods are frequently used to approximate or bound the partition or likelihood function of a Markov random field.
