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Generative Adversarial Text to Image Synthesis
TLDR
We develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image modeling, translating visual concepts from characters to pixels. Expand
An efficient framework for learning sentence representations
TLDR
In this work we propose a simple and efficient framework for learning sentence representations from unlabelled data, and we show that the model learns high-quality sentence representations. Expand
Sentence Ordering and Coherence Modeling using Recurrent Neural Networks
TLDR
We propose an end-to-end unsupervised deep learning approach based on the set- to-sequence framework to address this problem. Expand
Zero-Shot Entity Linking by Reading Entity Descriptions
TLDR
We present the zero-shot entity linking task, where mentions must be linked to unseen entities without in-domain labeled data. Expand
Content preserving text generation with attribute controls
TLDR
We propose an adversarial loss to enforce generated samples to be attribute compatible and realistic, and evaluate models using these metrics. Expand
Sentence Ordering using Recurrent Neural Networks
TLDR
We propose an end-to-end neural approach based on the recently proposed set to sequence mapping framework to address the sentence ordering problem. Expand
Solving Jigsaw Puzzles using Paths and Cycles
TLDR
We introduce the concept of paths and cycles in jigsaw compatibility puzzles and show that they provide a means of identifying correct and incorrect matches. Expand
Few-shot Sequence Learning with Transformers
TLDR
We investigate few-shot learning in the setting where the data points are sequences of tokens and propose an efficient learning algorithm based on Transformers. Expand
Performance, Resource, and Cost Aware Resource Provisioning in the Cloud
TLDR
We propose a dynamic and computationally efficient reconfiguration scheme which comprises an Application Performance Model, a Cost Model, and a Reconfiguration algorithm. Expand
Performance, resource and cost aware virtual machine adaptation
TLDR
We propose a dynamic VM recon guration scheme which comprises an Application Performance Model, a Cost Model and a Recon guration algorithm. Expand
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