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10/13/2022
Searching for Better Database Queries in the Outputs of Semantic Parsers
The task of generating a database query from a question in natural langu...
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09/13/2021
SPARQLing Database Queries from Intermediate Question Decompositions
To translate natural language questions into executable database queries...
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03/15/2020
OS2D: One-Stage One-Shot Object Detection by Matching Anchor Features
In this paper, we consider the task of one-shot object detection, which ...
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12/08/2019
Cost-Sensitive Training for Autoregressive Models
Training autoregressive models to better predict under the test metric, ...
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02/28/2019
Scaling Matters in Deep Structured-Prediction Models
Deep structured-prediction energy-based models combine the expressive po...
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12/06/2018
Tube-CNN: Modeling temporal evolution of appearance for object detection in video
Object detection in video is crucial for many applications. Compared to ...
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11/21/2018
Marginal Weighted Maximum Log-likelihood for Efficient Learning of Perturb-and-Map models
We consider the structured-output prediction problem through probabilist...
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10/26/2018
Quantifying Learning Guarantees for Convex but Inconsistent Surrogates
We study consistency properties of machine learning methods based on min...
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06/28/2018
Modeling Spatio-Temporal Human Track Structure for Action Localization
This paper addresses spatio-temporal localization of human actions in vi...
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08/15/2017
GANs for Biological Image Synthesis
In this paper, we propose a novel application of Generative Adversarial ...
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06/14/2017
SEARNN: Training RNNs with Global-Local Losses
We propose SEARNN, a novel training algorithm for recurrent neural netwo...
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03/07/2017
On Structured Prediction Theory with Calibrated Convex Surrogate Losses
We provide novel theoretical insights on structured prediction in the co...
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05/30/2016
Minding the Gaps for Block Frank-Wolfe Optimization of Structured SVMs
In this paper, we propose several improvements on the block-coordinate F...
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09/22/2015
Tensorizing Neural Networks
Deep neural networks currently demonstrate state-of-the-art performance ...
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02/25/2015
Breaking Sticks and Ambiguities with Adaptive Skip-gram
Recently proposed Skip-gram model is a powerful method for learning high...
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01/15/2015
Submodular relaxation for inference in Markov random fields
In this paper we address the problem of finding the most probable state ...
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06/23/2014
Multi-utility Learning: Structured-output Learning with Multiple Annotation-specific Loss Functions
Structured-output learning is a challenging problem; particularly so bec...
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03/05/2011