49 research outputs found
OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework
Recent developments in Artificial Intelligence techniques have enabled their
successful application across a spectrum of commercial and industrial settings.
However, these techniques require large volumes of data to be aggregated in a
centralized manner, forestalling their applicability to scenarios wherein the
data is sensitive or the cost of data transmission is prohibitive. Federated
Learning alleviates these problems by decentralizing model training, thereby
removing the need for data transfer and aggregation. To advance the adoption of
Federated Learning, more research and development needs to be conducted to
address some important open questions. In this work, we propose OpenFed, an
open-source software framework for end-to-end Federated Learning. OpenFed
reduces the barrier to entry for both researchers and downstream users of
Federated Learning by the targeted removal of existing pain points. For
researchers, OpenFed provides a framework wherein new methods can be easily
implemented and fairly evaluated against an extensive suite of benchmarks. For
downstream users, OpenFed allows Federated Learning to be plug and play within
different subject-matter contexts, removing the need for deep expertise in
Federated Learning.Comment: 18 pages, 3 figures, 1 tabl
Rethinking skip connection model as a learnable Markov chain
Over past few years afterward the birth of ResNet, skip connection has become
the defacto standard for the design of modern architectures due to its
widespread adoption, easy optimization and proven performance. Prior work has
explained the effectiveness of the skip connection mechanism from different
perspectives. In this work, we deep dive into the model's behaviors with skip
connections which can be formulated as a learnable Markov chain. An efficient
Markov chain is preferred as it always maps the input data to the target domain
in a better way. However, while a model is explained as a Markov chain, it is
not guaranteed to be optimized following an efficient Markov chain by existing
SGD-based optimizers which are prone to get trapped in local optimal points. In
order to towards a more efficient Markov chain, we propose a simple routine of
penal connection to make any residual-like model become a learnable Markov
chain. Aside from that, the penal connection can also be viewed as a particular
model regularization and can be easily implemented with one line of code in the
most popular deep learning frameworks~\footnote{Source code:
\url{https://github.com/densechen/penal-connection}}. The encouraging
experimental results in multi-modal translation and image recognition
empirically confirm our conjecture of the learnable Markov chain view and
demonstrate the superiority of the proposed penal connection.Comment: 12 pages, 4 figure
Deformable Object Tracking with Gated Fusion
The tracking-by-detection framework receives growing attentions through the
integration with the Convolutional Neural Networks (CNNs). Existing
tracking-by-detection based methods, however, fail to track objects with severe
appearance variations. This is because the traditional convolutional operation
is performed on fixed grids, and thus may not be able to find the correct
response while the object is changing pose or under varying environmental
conditions. In this paper, we propose a deformable convolution layer to enrich
the target appearance representations in the tracking-by-detection framework.
We aim to capture the target appearance variations via deformable convolution,
which adaptively enhances its original features. In addition, we also propose a
gated fusion scheme to control how the variations captured by the deformable
convolution affect the original appearance. The enriched feature representation
through deformable convolution facilitates the discrimination of the CNN
classifier on the target object and background. Extensive experiments on the
standard benchmarks show that the proposed tracker performs favorably against
state-of-the-art methods
Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates
Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data
Operational Risk Aggregation across Business Lines Based on Frequency Dependence and Loss Dependence
In loss distribution approach (LDA), the most popular approach in operational risk modeling, frequency dependence and loss distribution dependence across business lines are two dependences which banks should consider. In practice, mainly for simplicity, many banks only model frequency dependence although they think that the impact of frequency dependence is insignificant. In this study, two approaches, respectively, models frequency dependence and loss distribution dependence, are introduced. Both approaches are modeled by copula function, which is capable of capturing nonlinear correlation. Based on the most comprehensive operational risk dataset of Chinese banking as far as we know, the operational risk capital charge of the overall Chinese banking is calculated by the two approaches. The results show that there is an obvious distinction between the capital calculated by modeling frequency dependence and the capital calculated by modeling loss dependence. The approach with very limited attention exactly yields a much larger capital result. So it is advised in this paper that banks should not just rely on the approach to modeling frequency dependence for it is natural and easy to deal with. A safer and more effective way for banks is to comprehensively take the results of the two kinds of approach into consideration
Operational Risk Aggregation across Business Lines Based on Frequency Dependence and Loss Dependence
In loss distribution approach (LDA), the most popular approach in operational risk modeling, frequency dependence and loss distribution dependence across business lines are two dependences which banks should consider. In practice, mainly for simplicity, many banks only model frequency dependence although they think that the impact of frequency dependence is insignificant. In this study, two approaches, respectively, models frequency dependence and loss distribution dependence, are introduced. Both approaches are modeled by copula function, which is capable of capturing nonlinear correlation. Based on the most comprehensive operational risk dataset of Chinese banking as far as we know, the operational risk capital charge of the overall Chinese banking is calculated by the two approaches. The results show that there is an obvious distinction between the capital calculated by modeling frequency dependence and the capital calculated by modeling loss dependence. The approach with very limited attention exactly yields a much larger capital result. So it is advised in this paper that banks should not just rely on the approach to modeling frequency dependence for it is natural and easy to deal with. A safer and more effective way for banks is to comprehensively take the results of the two kinds of approach into consideration