Search Results for author:
Found 18 papers, 1 papers with code
Expectation-Maximization for Learning Determinantal Point Processes
However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.
Understanding Objects in Detail with Fine-Grained Attributes
We show that the collected data can be used to study the relation between part detection and attribute prediction by diagnosing the performance of classifiers that pool information from different parts of an object.
Learning the Parameters of Determinantal Point Process Kernels
Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired.
Controlling Complexity in Part-of-Speech Induction
We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.
Approximate Inference in Continuous Determinantal Processes
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.
Learning Adaptive Value of Information for Structured Prediction
Such feature selection methods control computation statically and miss the opportunity to fine-tune feature extraction to each input at run-time.
Approximate Inference in Continuous Determinantal Point Processes
Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.
SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning
We propose SCALPEL, a flexible method for object segmentation that integrates rich region-merging cues with midand high-level information about object layout, class, and scale into the segmentation process.
MODEC: Multimodal Decomposable Models for Human Pose Estimation
Unlike other multimodal models, our approach includes both global and local pose cues and uses a convex objective and joint training for mode selection and pose estimation.
Near-Optimal MAP Inference for Determinantal Point Processes
Determinantal point processes (DPPs) have recently been proposed as computationally efficient probabilistic models of diverse sets for a variety of applications, including document summarization, image search, and pose estimation.
Determinantal point processes for machine learning
Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.
Structured Determinantal Point Processes
We present a novel probabilistic model for distributions over sets of structures -- for example, sets of sequences, trees, or graphs.
Semi-Supervised Learning with Adversarially Missing Label Information
We address the problem of semi-supervised learning in an adversarial setting.
Sidestepping Intractable Inference with Structured Ensemble Cascades
For many structured prediction problems, complex models often require adopting approximate inference techniques such as variational methods or sampling, which generally provide no satisfactory accuracy guarantees.
Posterior vs Parameter Sparsity in Latent Variable Models
We apply this new method to learn first-order HMMs for unsupervised part-of-speech (POS) tagging, and show that HMMs learned this way consistently and significantly out-performs both EM-trained HMMs, and HMMs with a sparsity-inducing Dirichlet prior trained by variational EM.
