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Found 14 papers, 2 papers with code
Cascaded Classification Models: Combining Models for Holistic Scene Understanding
We demonstrate the effectiveness of our method on a large set of natural images by combining the subtasks of scene categorization, object detection, multiclass image segmentation, and 3d scene reconstruction.
Region-based Segmentation and Object Detection
Object detection and multi-class image segmentation are two closely related tasks that can be greatly improved when solved jointly by feeding information from one task to the other.
Learning a Small Mixture of Trees
The problem of approximating a given probability distribution using a simpler distribution plays an important role in several areas of machine learning, e. g. variational inference and classification.
Self-Paced Learning for Latent Variable Models
Latent variable models are a powerful tool for addressing several tasks in machine learning.
Active Classification based on Value of Classifier
Many of these tasks are tackled by constructing a set of classifiers, which are then applied at test time and then pieced together in a fixed procedure determined in advance or at training time.
Expectation Maximization and Complex Duration Distributions for Continuous Time Bayesian Networks
A CTBN is a directed (possibly cyclic) dependency graph over a set of variables, each of which represents a finite state continuous time Markov process whose transition model is a function of its parents.
Expectation Propagation for Continuous Time Bayesian Networks
Continuous time Bayesian networks (CTBNs) describe structured stochastic processes with finitely many states that evolve over continuous time.
Shifting Weights: Adapting Object Detectors from Image to Video
In this paper, we tackle the problem of adapting object detectors learned from images to work well on videos.
Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (2001)
This is the Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001
Context-Specific Independence in Bayesian Networks
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution.
Tuned Models of Peer Assessment in MOOCs
In massive open online courses (MOOCs), peer grading serves as a critical tool for scaling the grading of complex, open-ended assignments to courses with tens or hundreds of thousands of students.
Inferring Multidimensional Rates of Aging from Cross-Sectional Data
Motivated by the study of human aging, we present an interpretable latent-variable model that learns temporal dynamics from cross-sectional data.
Peptide-Spectra Matching from Weak Supervision
As in many other scientific domains, we face a fundamental problem when using machine learning to identify proteins from mass spectrometry data: large ground truth datasets mapping inputs to correct outputs are extremely difficult to obtain.
Regression modeling on DNA encoded libraries
DNA encoded libraries (DELs) are pooled, combinatorial compound collections where each member is tagged with its own unique DNA barcode.
