Search Results for author: Ben Taskar

Found 18 papers, 1 papers with code

Expectation-Maximization for Learning Determinantal Point Processes

no code implementations NeurIPS 2014 Jennifer Gillenwater, Alex Kulesza, Emily Fox, Ben Taskar

However, log-likelihood is non-convex in the entries of the kernel matrix, and this learning problem is conjectured to be NP-hard.

Point Processes Product Recommendation

Understanding Objects in Detail with Fine-Grained Attributes

no code implementations CVPR 2014 Andrea Vedaldi, Siddharth Mahendran, Stavros Tsogkas, Subhransu Maji, Ross Girshick, Juho Kannala, Esa Rahtu, Iasonas Kokkinos, Matthew B. Blaschko, David Weiss, Ben Taskar, Karen Simonyan, Naomi Saphra, Sammy Mohamed

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.

object-detection Object Detection

Learning the Parameters of Determinantal Point Process Kernels

no code implementations20 Feb 2014 Raja Hafiz Affandi, Emily B. Fox, Ryan P. Adams, Ben Taskar

Determinantal point processes (DPPs) are well-suited for modeling repulsion and have proven useful in many applications where diversity is desired.

Point Processes

Controlling Complexity in Part-of-Speech Induction

no code implementations16 Jan 2014 João V. Graça, Kuzman Ganchev, Luisa Coheur, Fernando Pereira, Ben Taskar

We consider the problem of fully unsupervised learning of grammatical (part-of-speech) categories from unlabeled text.

Inductive Bias

Approximate Inference in Continuous Determinantal Processes

no code implementations NeurIPS 2013 Raja Hafiz Affandi, Emily Fox, Ben Taskar

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.

Point Processes

Learning Adaptive Value of Information for Structured Prediction

no code implementations NeurIPS 2013 David J. Weiss, Ben Taskar

Such feature selection methods control computation statically and miss the opportunity to fine-tune feature extraction to each input at run-time.

feature selection Model Selection +3

Approximate Inference in Continuous Determinantal Point Processes

no code implementations12 Nov 2013 Raja Hafiz Affandi, Emily B. Fox, Ben Taskar

Determinantal point processes (DPPs) are random point processes well-suited for modeling repulsion.

Point Processes

MODEC: Multimodal Decomposable Models for Human Pose Estimation

no code implementations CVPR 2013 Ben Sapp, Ben Taskar

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.

Pose Estimation

SCALPEL: Segmentation Cascades with Localized Priors and Efficient Learning

no code implementations CVPR 2013 David Weiss, Ben Taskar

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.

Re-Ranking Semantic Segmentation

Near-Optimal MAP Inference for Determinantal Point Processes

no code implementations NeurIPS 2012 Jennifer Gillenwater, Alex Kulesza, Ben Taskar

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.

Document Summarization Image Retrieval +2

Determinantal point processes for machine learning

5 code implementations25 Jul 2012 Alex Kulesza, Ben Taskar

Determinantal point processes (DPPs) are elegant probabilistic models of repulsion that arise in quantum physics and random matrix theory.

BIG-bench Machine Learning Point Processes

Sidestepping Intractable Inference with Structured Ensemble Cascades

no code implementations NeurIPS 2010 David Weiss, Benjamin Sapp, Ben Taskar

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.

Pose Estimation Structured Prediction

Structured Determinantal Point Processes

no code implementations NeurIPS 2010 Alex Kulesza, Ben Taskar

We present a novel probabilistic model for distributions over sets of structures -- for example, sets of sequences, trees, or graphs.

Point Processes Pose Estimation

Posterior vs Parameter Sparsity in Latent Variable Models

no code implementations NeurIPS 2009 Kuzman Ganchev, Ben Taskar, Fernando Pereira, João Gama

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.

Part-Of-Speech Tagging POS

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