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.

Deep latent variable models are powerful tools for representation learning. In this paper, we adopt the deep information bottleneck model, identify its shortcomings and propose a model that circumvents them. To this end, we apply a copula transformation which, by restoring the invariance properties of the information bottleneck method, leads to disentanglement of the features in the latent space. Building on that, we show how this transformation translates to sparsity of the latent space in the new model. We evaluate our method on artificial and real data.
We apply the copula transformation to the Deep Information Bottleneck which leads to restored invariance properties and a disentangled latent space with superior predictive capabilities.

The embedding layers transforming input words into real vectors are the key components of deep neural networks used in natural language processing. However, when the vocabulary is large, the corresponding weight matrices can be enormous, which precludes their deployment in a limited resource setting. We introduce a novel way of parametrizing embedding layers based on the Tensor Train (TT) decomposition, which allows compressing the model significantly at the cost of a negligible drop or even a slight gain in performance. We evaluate our method on a wide range of benchmarks in natural language processing and analyze the trade-off between performance and compression ratios for a wide range of architectures, from MLPs to LSTMs and Transformers.
Embedding layers are factorized with Tensor Train decomposition to reduce their memory footprint.

In distributed training, the communication cost due to the transmission of gradients
or the parameters of the deep model is a major bottleneck in scaling up the number
of processing nodes. To address this issue, we propose dithered quantization for
the transmission of the stochastic gradients and show that training with Dithered
Quantized Stochastic Gradients (DQSG) is similar to the training with unquantized
SGs perturbed by an independent bounded uniform noise, in contrast to the other
quantization methods where the perturbation depends on the gradients and hence,
complicating the convergence analysis. We study the convergence of training
algorithms using DQSG and the trade off between the number of quantization
levels and the training time. Next, we observe that there is a correlation among the
SGs computed by workers that can be utilized to further reduce the communication
overhead without any performance loss. Hence, we develop a simple yet effective
quantization scheme, nested dithered quantized SG (NDQSG), that can reduce the
communication significantly without requiring the workers communicating extra
information to each other. We prove that although NDQSG requires significantly
less bits, it can achieve the same quantization variance bound as DQSG. Our
simulation results confirm the effectiveness of training using DQSG and NDQSG
in reducing the communication bits or the convergence time compared to the
existing methods without sacrificing the accuracy of the trained model.
The paper proposes and analyzes two quantization schemes for communicating Stochastic Gradients in distributed learning which would reduce communication costs compare to the state of the art while maintaining the same accuracy.