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.

Example Input: Sequential data often originates from diverse environments. Across them exist both shared regularities and environment specifics. To learn robust cross-environment descriptions of sequences we introduce disentangled state space models (DSSM). In the latent space of DSSM environment-invariant state dynamics is explicitly disentangled from environment-specific information governing that dynamics. We empirically show that such separation enables robust prediction, sequence manipulation and environment characterization. We also propose an unsupervised VAE-based training procedure to learn DSSM as Bayesian filters. In our experiments, we demonstrate state-of-the-art performance in controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.
Example Output: DISENTANGLED STATE SPACE MODELS

Example Input: We propose a new, multi-component energy function for energy-based Generative Adversarial Networks (GANs) based on methods from the image quality assessment literature. Our approach expands on the Boundary Equilibrium Generative Adversarial Network (BEGAN) by outlining some of the short-comings of the original energy and loss functions. We address these short-comings by incorporating an l1 score, the Gradient Magnitude Similarity score, and a chrominance score into the new energy function. We then provide a set of systematic experiments that explore its hyper-parameters. We show that each of the energy function's components is able to represent a slightly different set of features, which require their own evaluation criteria to assess whether they have been adequately learned. We show that models using the new energy function are able to produce better image representations than the BEGAN model in predicted ways.
Example Output: Image Quality Assessment Techniques Improve Training and Evaluation of Energy-Based Generative Adversarial Networks

Example Input: Deep neural networks (DNNs) dominate current research in machine learning. Due to massive GPU parallelization DNN training is no longer a bottleneck, and large models with many parameters and high computational effort lead common benchmark tables. In contrast, embedded devices have a very limited capability. As a result, both model size and inference time must be significantly reduced if DNNs are to achieve suitable performance on embedded devices.
We propose a soft quantization approach to train DNNs that can be evaluated using pure fixed-point arithmetic. By exploiting the bit-shift mechanism, we derive fixed-point quantization constraints for all important components, including batch normalization and ReLU. Compared to floating-point arithmetic, fixed-point calculations significantly reduce computational effort whereas low-bit representations immediately decrease memory costs. We evaluate our approach with different architectures on common benchmark data sets and compare with recent quantization approaches. We achieve new state of the art performance using 4-bit fixed-point models with an error rate of 4.98% on CIFAR-10.
Example Output:
Soft quantization approach to learn pure fixed-point representations of deep neural networks