1,692 research outputs found
Expert Selection in High-Dimensional Markov Decision Processes
In this work we present a multi-armed bandit framework for online expert
selection in Markov decision processes and demonstrate its use in
high-dimensional settings. Our method takes a set of candidate expert policies
and switches between them to rapidly identify the best performing expert using
a variant of the classical upper confidence bound algorithm, thus ensuring low
regret in the overall performance of the system. This is useful in applications
where several expert policies may be available, and one needs to be selected at
run-time for the underlying environment.Comment: In proceedings of the 59th IEEE Conference on Decision and Control
2020. arXiv admin note: text overlap with arXiv:1707.0571
Study on evaluation of International Science and Technology Cooperation Project (ISTCP) in China
This paper presents an overview of evaluation of ISTCP in China. We discuss briefly the history of evaluation and the strengths and weaknesses of different assessment systems. On this basis, with Analytical Hierarchy Process (AHP), we establish evaluation indicator system for ISTCP that includes research project establishment evaluation, mid-period evaluation system, effect evaluation system, and confirm the value of each indicator. At the same time, we established expert database, project database, research organization database, researcher database etc. We therefore establish an evaluation platform for international science and technology cooperation project. We use it to realize full process supervision from evaluation expert selection to project management
Ensemble prediction model with expert selection for electricity price forecasting
Forecasting of electricity prices is important in deregulated electricity markets for all of the stakeholders: energy wholesalers, traders, retailers and consumers. Electricity price forecasting is an inherently difficult problem due to its special characteristic of dynamicity and non-stationarity. In this paper, we present a robust price forecasting mechanism that shows resilience towards the aggregate demand response effect and provides highly accurate forecasted electricity prices to the stakeholders in a dynamic environment. We employ an ensemble prediction model in which a group of different algorithms participates in forecasting 1-h ahead the price for each hour of a day. We propose two different strategies, namely, the Fixed Weight Method (FWM) and the Varying Weight Method (VWM), for selecting each hour’s expert algorithm from the set of participating algorithms. In addition, we utilize a carefully engineered set of features selected from a pool of features extracted from the past electricity price data, weather data and calendar data. The proposed ensemble model offers better results than the Autoregressive Integrated Moving Average (ARIMA) method, the Pattern Sequence-based Forecasting (PSF) method and our previous work using Artificial Neural Networks (ANN) alone on the datasets for New York, Australian and Spanish electricity markets
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Knowledge distillation (KD) is an effective tool for compressing deep
classification models for edge devices. However, the performance of KD is
affected by the large capacity gap between the teacher and student networks.
Recent methods have resorted to a multiple teacher assistant (TA) setting for
KD, which sequentially decreases the size of the teacher model to relatively
bridge the size gap between these models. This paper proposes a new technique
called Curriculum Expert Selection for Knowledge Distillation (CES-KD) to
efficiently enhance the learning of a compact student under the capacity gap
problem. This technique is built upon the hypothesis that a student network
should be guided gradually using stratified teaching curriculum as it learns
easy (hard) data samples better and faster from a lower (higher) capacity
teacher network. Specifically, our method is a gradual TA-based KD technique
that selects a single teacher per input image based on a curriculum driven by
the difficulty in classifying the image. In this work, we empirically verify
our hypothesis and rigorously experiment with CIFAR-10, CIFAR-100, CINIC-10,
and ImageNet datasets and show improved accuracy on VGG-like models, ResNets,
and WideResNets architectures.Comment: ICPR202
Communication cost of consensus for nodes with limited memory
Motivated by applications in blockchains and sensor networks, we consider a
model of nodes trying to reach consensus on their majority bit. Each node
is assigned a bit at time zero, and is a finite automaton with bits of
memory (i.e., states) and a Poisson clock. When the clock of rings,
can choose to communicate, and is then matched to a uniformly chosen node
. The nodes and may update their states based on the state of the
other node. Previous work has focused on minimizing the time to consensus and
the probability of error, while our goal is minimizing the number of
communications. We show that when , consensus can be
reached at linear communication cost, but this is impossible if
. We also study a synchronous variant of the model, where
our upper and lower bounds on for achieving linear communication cost are
and , respectively. A key step is to
distinguish when nodes can become aware of knowing the majority bit and stop
communicating. We show that this is impossible if their memory is too low.Comment: 62 pages, 5 figure
Approval plans, discipline change, and the importance of human mediated book selection
This study examines holdings of 21 members of the Association of Research Libraries for books reviewed in American Historical Review. The study asserts that approval plans are inadequate for collecting from small publishers or from scholarship that crosses disciplinary boundaries. Although approval plans increase efficiency in collection development, the need for expert selection cannot be overstated. Results indicated that small publisher’s books were less likely to be in libraries than university press publisher’s books, and that history monographs are frequently classified outside disciplinary boundaries, and are therefore invisible to approval plans that define disciplines based on classification systems
Unsupervised ensemble of experts (EoE) framework for automatic binarization of document images
In recent years, a large number of binarization methods have been developed,
with varying performance generalization and strength against different
benchmarks. In this work, to leverage on these methods, an ensemble of experts
(EoE) framework is introduced, to efficiently combine the outputs of various
methods. The proposed framework offers a new selection process of the
binarization methods, which are actually the experts in the ensemble, by
introducing three concepts: confidentness, endorsement and schools of experts.
The framework, which is highly objective, is built based on two general
principles: (i) consolidation of saturated opinions and (ii) identification of
schools of experts. After building the endorsement graph of the ensemble for an
input document image based on the confidentness of the experts, the saturated
opinions are consolidated, and then the schools of experts are identified by
thresholding the consolidated endorsement graph. A variation of the framework,
in which no selection is made, is also introduced that combines the outputs of
all experts using endorsement-dependent weights. The EoE framework is evaluated
on the set of participating methods in the H-DIBCO'12 contest and also on an
ensemble generated from various instances of grid-based Sauvola method with
promising performance.Comment: 6-page version, Accepted to be presented in ICDAR'1
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