In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

Ex Input:
Domain transfer is a exciting and challenging branch of machine learning because models must learn to smoothly transfer between domains, preserving local variations and capturing many aspects of variation without labels.
However, most successful applications to date require the two domains to be closely related (ex. image-to-image, video-video),
utilizing similar or shared networks to transform domain specific properties like texture, coloring, and line shapes.
Here, we demonstrate that it is possible to transfer across modalities (ex. image-to-audio) by first abstracting the data with latent generative models and then learning transformations between latent spaces.
We find that a simple variational autoencoder is able to learn a shared latent space to bridge between two generative models in an unsupervised fashion, and even between different types of models (ex. variational autoencoder and a generative adversarial network).
We can further impose desired semantic alignment of attributes with a linear classifier in the shared latent space.
The proposed variation autoencoder enables preserving both locality and semantic alignment through the transfer process, as shown in the qualitative and quantitative evaluations.
Finally, the hierarchical structure decouples the cost of training the base generative models and semantic alignments, enabling computationally efficient and data efficient retraining of personalized mapping functions.

Ex Output:
Conditional VAE on top of latent spaces of pre-trained generative models that enables transfer between drastically different domains while preserving locality and semantic alignment.

Ex Input:
Convolutional Neural Networks (CNN) are very popular in many fields including computer vision, speech recognition, natural language processing, to name a few. Though deep learning leads to groundbreaking performance in these domains, the networks used are very demanding computationally and are far from real-time even on a GPU, which is not power efficient and therefore does not suit low power systems such as mobile devices. To overcome this challenge, some solutions have been proposed for quantizing the weights and activations of these networks, which accelerate the runtime significantly. Yet, this acceleration comes at the cost of a larger error. The NICE method proposed in this work trains quantized neural networks by noise injection and a learned clamping, which improve the accuracy. This leads to state-of-the-art results on various regression and classification tasks, e.g., ImageNet classification with architectures such as ResNet-18/34/50 with low as 3-bit weights and 3 -bit activations. We implement the proposed solution on an FPGA to demonstrate its applicability for low power real-time applications.

Ex Output:
Combine noise injection, gradual quantization and activation clamping learning to achieve state-of-the-art 3,4 and 5 bit quantization

Ex Input:
We propose a novel yet simple neural network architecture for topic modelling. The method is based on training an autoencoder structure where the bottleneck represents the space of the topics distribution and the decoder outputs represent the space of the words distributions over the topics. We exploit an auxiliary decoder to prevent mode collapsing in our model. A key feature for an effective topic modelling method is having sparse topics and words distributions, where there is a trade-off between the sparsity level of topics and words. This feature is implemented in our model by L-2 regularization and the model hyperparameters take care of the trade-off. We show in our experiments that our model achieves competitive results compared to the state-of-the-art deep models for topic modelling, despite its simple architecture and training procedure. The "New York Times" and "20 Newsgroups" datasets are used in the experiments.

Ex Output:
A deep model for topic modelling