673 research outputs found
A Response-Function-Based Coordination Method for Transmission-Distribution-Coupled AC OPF
With distributed generation highly integrated into the grid, the
transmission-distribution-coupled AC OPF (TDOPF) becomes increasingly
important. This paper proposes a response-function-based coordination method to
solve the TDOPF. Different from typical decomposition methods, this method
employs approximate response functions of the power injections with respect to
the bus voltage magnitude in the transmission-distribution (T-D) interface to
reflect the "reaction" of the distribution to the transmission system control.
By using the response functions, only one or two iterations between the
transmission system operator (TSO) and the distribution system operator(s)
(DSO(s)) are required to attain a nearly optimal TDOPF solution. Numerical
tests confirm that, relative to a typical decomposition method, the proposed
method does not only enjoy a cheaper computational cost but is workable even
when the objectives of the TSO and the DSO(s) are in distinct scales.Comment: This paper will appear at 2018 IEEE PES Transmission and Distribution
Conference and Expositio
Enhance Diffusion to Improve Robust Generalization
Deep neural networks are susceptible to human imperceptible adversarial
perturbations. One of the strongest defense mechanisms is \emph{Adversarial
Training} (AT). In this paper, we aim to address two predominant problems in
AT. First, there is still little consensus on how to set hyperparameters with a
performance guarantee for AT research, and customized settings impede a fair
comparison between different model designs in AT research. Second, the robustly
trained neural networks struggle to generalize well and suffer from tremendous
overfitting. This paper focuses on the primary AT framework - Projected
Gradient Descent Adversarial Training (PGD-AT). We approximate the dynamic of
PGD-AT by a continuous-time Stochastic Differential Equation (SDE), and show
that the diffusion term of this SDE determines the robust generalization. An
immediate implication of this theoretical finding is that robust generalization
is positively correlated with the ratio between learning rate and batch size.
We further propose a novel approach, \emph{Diffusion Enhanced Adversarial
Training} (DEAT), to manipulate the diffusion term to improve robust
generalization with virtually no extra computational burden. We theoretically
show that DEAT obtains a tighter generalization bound than PGD-AT. Our
empirical investigation is extensive and firmly attests that DEAT universally
outperforms PGD-AT by a significant margin.Comment: Accepted at KDD 202
Augmenting Knowledge Transfer across Graphs
Given a resource-rich source graph and a resource-scarce target graph, how
can we effectively transfer knowledge across graphs and ensure a good
generalization performance? In many high-impact domains (e.g., brain networks
and molecular graphs), collecting and annotating data is prohibitively
expensive and time-consuming, which makes domain adaptation an attractive
option to alleviate the label scarcity issue. In light of this, the
state-of-the-art methods focus on deriving domain-invariant graph
representation that minimizes the domain discrepancy. However, it has recently
been shown that a small domain discrepancy loss may not always guarantee a good
generalization performance, especially in the presence of disparate graph
structures and label distribution shifts. In this paper, we present TRANSNET, a
generic learning framework for augmenting knowledge transfer across graphs. In
particular, we introduce a novel notion named trinity signal that can naturally
formulate various graph signals at different granularity (e.g., node
attributes, edges, and subgraphs). With that, we further propose a domain
unification module together with a trinity-signal mixup scheme to jointly
minimize the domain discrepancy and augment the knowledge transfer across
graphs. Finally, comprehensive empirical results show that TRANSNET outperforms
all existing approaches on seven benchmark datasets by a significant margin
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