TASK DEFINITION: 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.
PROBLEM: Dramatic advances in generative models have resulted in near photographic quality for artificially rendered faces, animals and other objects in the natural world. In spite of such advances, a higher level understanding of vision and imagery does not arise from exhaustively modeling an object,  but instead identifying higher-level  attributes  that  best  summarize  the  aspects  of  an  object.    In  this  work  we attempt  to  model  the  drawing  process  of  fonts  by  building  sequential generative models of vector graphics.   This model has the benefit of providing a scale-invariant representation for imagery whose latent representation may be systematically manipulated and exploited to perform style propagation. We demonstrate these results on a large dataset of fonts and highlight how such a model captures the statistical dependencies and richness of this dataset. We envision that our model can find use as a tool for designers to facilitate font design.

SOLUTION: We attempt to model the drawing process of fonts by building sequential generative models of vector graphics (SVGs), a highly structured representation of font characters.

PROBLEM: Structured tabular data is the most commonly used form of data in industry according to a Kaggle ML and DS Survey. Gradient Boosting Trees, Support Vector Machine, Random Forest, and Logistic Regression are typically used for classification tasks on tabular data. The recent work of Super Characters method using two-dimensional word embeddings achieved state-of-the-art results in text classification tasks, showcasing the promise of this new approach. In this paper, we propose the SuperTML method, which borrows the idea of Super Characters method and two-dimensional embeddings to address the problem of classification on tabular data. For each input of tabular data, the features are first projected into two-dimensional embeddings like an image, and then this image is fed into fine-tuned ImageNet CNN models for classification. Experimental results have shown that the proposed SuperTML method have achieved state-of-the-art results on both large and small datasets.

SOLUTION: Deep learning on structured tabular data using two-dimensional word embedding with fine-tuned ImageNet pre-trained CNN model.

PROBLEM: As an emerging field, federated learning has recently attracted considerable attention.
 Compared to distributed learning in the datacenter setting, federated learning
 has more strict constraints on computate efficiency of the learned model and communication
 cost during the training process. In this work, we propose an efficient
 federated learning framework based on variational dropout. Our approach is able
 to jointly learn a sparse model while reducing the amount of gradients exchanged
 during the iterative training process. We demonstrate the superior performance
 of our approach on achieving significant model compression and communication
 reduction ratios with no accuracy loss.

SOLUTION:
a joint model and gradient sparsification method for federated learning