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Language Models for Image Captioning: The Quirks and What Works
TLDR
By combining key aspects of the ME and RNN methods, this paper achieves a new record performance over previously published results on the benchmark COCO dataset, however, the gains the authors see in BLEU do not translate to human judgments. Expand
Permutation Meets Parallel Compressed Sensing: How to Relax Restricted Isometry Property for 2D Sparse Signals
TLDR
A zigzag-scan-based permutation is shown to be particularly useful for signals satisfying the newly introduced layer model and increases the peak signal-to-noise ratio of reconstructed images and video frames. Expand
Sounding Board: A User-Centric and Content-Driven Social Chatbot
TLDR
The system architecture consists of several components including spoken language processing, dialogue management, language generation, and content management, with emphasis on user-centric and content-driven design. Expand
Talking to the crowd: What do people react to in online discussions?
TLDR
A new comment ranking task is proposed based on community annotated karma in Reddit discussions, which controls for topic and timing of comments, and the relative importance of the message vs. the messenger. Expand
Open-Domain Name Error Detection using a Multi-Task RNN
TLDR
A multi-task recurrent neural network language model for sentence-level name detection is proposed for use in combination with out-of-vocabulary word detection, which shows a 26% improvement in name-error detection F-score over a system using n-gram lexical features. Expand
A Factored Neural Network Model for Characterizing Online Discussions in Vector Space
TLDR
A novel factored neural model that learns comment embeddings in an unsupervised way leveraging the structure of distributional context in online discussion forums and captures community style and topic, as well as response trigger patterns. Expand
Permutation enhanced parallel reconstruction for compressive sampling
TLDR
A simple but efficient permutation enhanced parallel reconstruction architecture for compressive sampling (CS) is proposed that can achieve comparable results to the centralized reconstruction methods, while requiring much less reconstruction time. Expand
Exponential Language Modeling Using Morphological Features and Multi-Task Learning
TLDR
Different uses of unsupervised morphological features in both the history and prediction space for three word-based exponential models (maximum entropy, logbilinear, and recurrent neural net (RNN) are explored. Expand
Learning Latent Local Conversation Modes for Predicting Community Endorsement in Online Discussions
TLDR
This paper addresses the problem of predicting community endorsement in online discussions, leveraging both the participant response structure and the text of the comment, using a novel architecture to learn latent modes of discussion structure that perform as well as deep neural networks but are more interpretable. Expand
Comparing density forecasts in a risk management context
We compare multivariate and univariate approaches to assessing the accuracy of competing density forecasts of a portfolio return in the downside part of the support. We argue that the common practiceExpand
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