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: Two important topics in deep learning both involve incorporating humans into the modeling process: Model priors transfer information from humans to a model by regularizing the model's parameters; Model attributions transfer information from a model to humans by explaining the model's behavior. Previous work has taken important steps to connect these topics through various forms of gradient regularization. We find, however, that existing methods that use attributions to align a model's behavior with human intuition are ineffective. We develop an efficient and theoretically grounded feature attribution method, expected gradients, and a novel framework, attribution priors, to enforce prior expectations about a model's behavior during training. We demonstrate that attribution priors are broadly applicable by instantiating them on three different types of data: image data, gene expression data, and health care data. Our experiments show that models trained with attribution priors are more intuitive and achieve better generalization performance than both equivalent baselines and existing methods to regularize model behavior.

SOLUTION: A method for encouraging axiomatic feature attributions of a deep model to match human intuition.

PROBLEM: Flow-based generative models are powerful exact likelihood models with efficient sampling and inference. 
 Despite their computational efficiency, flow-based models generally have much worse density modeling performance compared to state-of-the-art autoregressive models. In this paper, we investigate and improve upon three limiting design choices employed by flow-based models in prior work: the use of uniform noise for dequantization, the use of inexpressive affine flows, and the use of purely convolutional conditioning networks in coupling layers. Based on our findings, we propose Flow++, a new flow-based model that is now the state-of-the-art non-autoregressive model for unconditional density estimation on standard image benchmarks. Our work has begun to close the significant performance gap that has so far existed between autoregressive models and flow-based models.

SOLUTION: Improved training of current flow-based generative models (Glow and RealNVP) on density estimation benchmarks

PROBLEM: Cloud Migration transforms customer’s data, application and services from original IT platform to one or more cloud en- vironment, with the goal of improving the performance of the IT system while reducing the IT management cost. The enterprise level Cloud Migration projects are generally com- plex, involves dynamically planning and replanning various types of transformations for up to 10k endpoints. Currently the planning and replanning in Cloud Migration are generally done manually or semi-manually with heavy dependency on the migration expert’s domain knowledge, which takes days to even weeks for each round of planning or replanning. As a result, automated planning engine that is capable of gener- ating high quality migration plan in a short time is particu- larly desirable for the migration industry. In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the challenges the requires attention from the planning and scheduling society.

SOLUTION:
In this short paper, we briefly introduce the advantages of using AI planning in Cloud Migration, a preliminary prototype, as well as the chal- lenges the requires attention from the planning and schedul- ing society.