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Deep learning on image denoising: An overview
Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …
However, there are substantial differences in the various types of deep learning methods …
[HTML][HTML] A deep fusion matching network semantic reasoning model
As the vital technology of natural language understanding, sentence representation
reasoning technology mainly focuses on sentence representation methods and reasoning …
reasoning technology mainly focuses on sentence representation methods and reasoning …
Characterization inference based on joint-optimization of multi-layer semantics and deep fusion matching network
The whole sentence representation reasoning process simultaneously comprises a
sentence representation module and a semantic reasoning module. This paper combines …
sentence representation module and a semantic reasoning module. This paper combines …
Coarse-to-fine CNN for image super-resolution
Deep convolutional neural networks (CNNs) have been popularly adopted in image super-
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …
resolution (SR). However, deep CNNs for SR often suffer from the instability of training …
Designing and training of a dual CNN for image denoising
Deep convolutional neural networks (CNNs) for image denoising have recently attracted
increasing research interest. However, plain networks cannot recover fine details for a …
increasing research interest. However, plain networks cannot recover fine details for a …
What do programmers discuss about deep learning frameworks
Deep learning has gained tremendous traction from the developer and researcher
communities. It plays an increasingly significant role in a number of application domains …
communities. It plays an increasingly significant role in a number of application domains …
Don't take the premise for granted: Mitigating artifacts in natural language inference
Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts
that allow models to achieve non-trivial performance without learning whether a premise …
that allow models to achieve non-trivial performance without learning whether a premise …
CAIL2019-SCM: a dataset of similar case matching in legal domain
In this paper, we introduce CAIL2019-SCM, Chinese AI and Law 2019 Similar Case
Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the …
Matching dataset. CAIL2019-SCM contains 8,964 triplets of cases published by the …
Deep hierarchical encoding model for sentence semantic matching
Sentence semantic matching (SSM) always plays a critical role in natural language
processing. Measuring the intrinsic semantic similarity among sentences is very challenging …
processing. Measuring the intrinsic semantic similarity among sentences is very challenging …
Multiple relational attention network for multi-task learning
Multi-task learning is a successful machine learning framework which improves the
performance of prediction models by leveraging knowledge among tasks, eg, the …
performance of prediction models by leveraging knowledge among tasks, eg, the …