2,077 research outputs found
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Timely accurate traffic forecast is crucial for urban traffic control and
guidance. Due to the high nonlinearity and complexity of traffic flow,
traditional methods cannot satisfy the requirements of mid-and-long term
prediction tasks and often neglect spatial and temporal dependencies. In this
paper, we propose a novel deep learning framework, Spatio-Temporal Graph
Convolutional Networks (STGCN), to tackle the time series prediction problem in
traffic domain. Instead of applying regular convolutional and recurrent units,
we formulate the problem on graphs and build the model with complete
convolutional structures, which enable much faster training speed with fewer
parameters. Experiments show that our model STGCN effectively captures
comprehensive spatio-temporal correlations through modeling multi-scale traffic
networks and consistently outperforms state-of-the-art baselines on various
real-world traffic datasets.Comment: Proceedings of the 27th International Joint Conference on Artificial
Intelligenc
The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects
Understanding the behavior of stochastic gradient descent (SGD) in the
context of deep neural networks has raised lots of concerns recently. Along
this line, we study a general form of gradient based optimization dynamics with
unbiased noise, which unifies SGD and standard Langevin dynamics. Through
investigating this general optimization dynamics, we analyze the behavior of
SGD on escaping from minima and its regularization effects. A novel indicator
is derived to characterize the efficiency of escaping from minima through
measuring the alignment of noise covariance and the curvature of loss function.
Based on this indicator, two conditions are established to show which type of
noise structure is superior to isotropic noise in term of escaping efficiency.
We further show that the anisotropic noise in SGD satisfies the two conditions,
and thus helps to escape from sharp and poor minima effectively, towards more
stable and flat minima that typically generalize well. We systematically design
various experiments to verify the benefits of the anisotropic noise, compared
with full gradient descent plus isotropic diffusion (i.e. Langevin dynamics).Comment: ICML 2019 camera read
Gastric lymphoma
Gastrointestinal lymphomas represent 5-20% of extra nodal lymphomas and mainly occur in the stomach and small intestine. Clinical findings are not specific, thus often determining a delay in the diagnosis. Imaging features at conventional and cross-sectional imaging must be known by the radiologist since he/she plays a pivotal role in the diagnosis and disease assessment, thus assisting in the choice of the optimal treatment to patients. This review focuses on the wide variety of imaging presentation of esophageal, gastric, and small and large bowel lymphoma presenting their main imaging appearances at conventional and cross-sectional imaging, mainly focusing on computed tomography and magnetic resonance, helping in the choice of the best imaging technique for the disease characterization and assessment and the recognition of potential complications. Gastrointestinal tract is the most common extra nodal site involved by lymphoma. Although lymphoma can involve any part of the gastrointestinal tract .The most frequent sites in order of its occurrence are the stomach followed by small intestine and ileocecal region. Gastrointestinal tract lymphoma is usually secondary to the widespread nodal diseases and primary gastrointestinal tract lymphoma is relatively rare
Patch-level Neighborhood Interpolation: A General and Effective Graph-based Regularization Strategy
Regularization plays a crucial role in machine learning models, especially
for deep neural networks. The existing regularization techniques mainly reply
on the i.i.d. assumption and only employ the information of the current sample,
without the leverage of neighboring information between samples. In this work,
we propose a general regularizer called Patch-level Neighborhood
Interpolation~(\textbf{Pani}) that fully exploits the relationship between
samples. Furthermore, by explicitly constructing a patch-level graph in the
different network layers and interpolating the neighborhood features to refine
the representation of the current sample, our Patch-level Neighborhood
Interpolation can then be applied to enhance two popular regularization
strategies, namely Virtual Adversarial Training (VAT) and MixUp, yielding their
neighborhood versions. The first derived \textbf{Pani VAT} presents a novel way
to construct non-local adversarial smoothness by incorporating patch-level
interpolated perturbations. In addition, the \textbf{Pani MixUp} method extends
the original MixUp regularization to the patch level and then can be developed
to MixMatch, achieving the state-of-the-art performance. Finally, extensive
experiments are conducted to verify the effectiveness of the Patch-level
Neighborhood Interpolation in both supervised and semi-supervised settings
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