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Found 9 papers, 3 papers with code
Deep Transferring Quantization
Specifically, we propose a method named deep transferring quantization (DTQ) to effectively exploit the knowledge in a pre-trained full-precision model.
Rhetorically Controlled Encoder-Decoder for Modern Chinese Poetry Generation
Rhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics.
An Adaptive Remote Stochastic Gradient Method for Training Neural Networks
RSG is further combined with adaptive methods to construct ARSG for acceleration.
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space
There have been a lot of researches based on the time series of the wind power or speed, but In fact, these time series cannot express the temporal and spatial changes of wind, which fundamentally hinders the advance of wind power prediction.
Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
We propose an heterogeneous multi-task learning framework for human pose estimation from monocular image with deep convolutional neural network.
ESSP: An Efficient Approach to Minimizing Dense and Nonsubmodular Energy Functions
From our study, we make some reasonable recommendations of combining existing methods that perform the best in different situations for this challenging problem.
Towards Big Topic Modeling
Although parallel LDA algorithms on the multi-processor architecture have low time and space complexities, their communication costs among processors often scale linearly with the vocabulary size and the number of topics, leading to a serious scalability problem.
Fast Online EM for Big Topic Modeling
The expectation-maximization (EM) algorithm can compute the maximum-likelihood (ML) or maximum a posterior (MAP) point estimate of the mixture models or latent variable models such as latent Dirichlet allocation (LDA), which has been one of the most popular probabilistic topic modeling methods in the past decade.
A New Approach to Speeding Up Topic Modeling
To accelerate the training speed, ABP actively scans the subset of corpus and searches the subset of topic space for topic modeling, therefore saves enormous training time in each iteration.
