1,334 research outputs found
Research on Sustainable Development Issue in the Vision of Game Theory
Sustainable development issues belong to external non-economical common land based concerns. From an historical viewpoint, the main responsibility for the current series of global environment problem should be taken on by developed countries. Therefore, on sustainable development issue, international society has formed a principle of “common but different” responsibility-sharing rule. By game theory model, it is found that the reason why the negotiation between developed countries and developing ones reaches a deadlock again and again is short of proper conditions or mechanism to co-operate. There are at least three prerequisites to solve sustainable development issues, none of which has been met so far. Key words: Game theory; Sustainable development; Prerequisite; Innovation Mechanis
Private Model Compression via Knowledge Distillation
The soaring demand for intelligent mobile applications calls for deploying
powerful deep neural networks (DNNs) on mobile devices. However, the
outstanding performance of DNNs notoriously relies on increasingly complex
models, which in turn is associated with an increase in computational expense
far surpassing mobile devices' capacity. What is worse, app service providers
need to collect and utilize a large volume of users' data, which contain
sensitive information, to build the sophisticated DNN models. Directly
deploying these models on public mobile devices presents prohibitive privacy
risk. To benefit from the on-device deep learning without the capacity and
privacy concerns, we design a private model compression framework RONA.
Following the knowledge distillation paradigm, we jointly use hint learning,
distillation learning, and self learning to train a compact and fast neural
network. The knowledge distilled from the cumbersome model is adaptively
bounded and carefully perturbed to enforce differential privacy. We further
propose an elegant query sample selection method to reduce the number of
queries and control the privacy loss. A series of empirical evaluations as well
as the implementation on an Android mobile device show that RONA can not only
compress cumbersome models efficiently but also provide a strong privacy
guarantee. For example, on SVHN, when a meaningful
-differential privacy is guaranteed, the compact model trained
by RONA can obtain 20 compression ratio and 19 speed-up with
merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1
Pakistan-China Relations: CPEC and Beyond
This contribution is based on the proceedings of a session on the same title organized by the Institute of Policy Studies (IPS), Islamabad on August 10, 2017. The highlight of the event, as is recorded in this article, was the exclusive talk of H.E. Sun Weidong, Chinese ambassador to Pakistan while the opening and concluding remarks by Khalid Rahman, Executive President of IPS and Senator Raja Zafar-ul-Haq, Leader of the House in the Senate of Pakistan, respectively, are also part of the article. Pleasantries have been edited for the purpose of brevity and consistency
Pattern Classification Using A Fuzzy Immune Network Model
It is generally believed that one major function of immune system is helping to protect multicellular organisms from foreign pathogens, especially replicating pathogens such as viruses, bacteria and parasites. The relevant events in immune system are not only the molecules, but also their interactions. The immune cells can respond either positively or negatively to the recognition signal. A positive response would result in cell proliferation, activation and antibody secretion, while a negative response would lead to tolerance and suppression. Depending upon these immune mechanisms, an immune network model(here, we call it the binary model) based on biological immune response network was proposed in our previous work. However, there are some problems like input and memory in the binary model. In order to improve the binary model, in this paper we propose a fuzzy immune network model. In the proposed fuzzy immune model, we add a normalization B cell layer for normalizing the large-scale antigen information on the base of the binary model. Meanwhile, a fuzzy AND operator(.AND.) and a normalization procedure called complement coding were employed in the proposed fuzzy immune model. Compute simulations illustrate that the proposed fuzzy model not only can improve the problems existing in the binary model but also is capable of clustering arbitrary sequences of large-scale analog input patterns into stable recognition categories. (author abst.
Diversity is Strength: Mastering Football Full Game with Interactive Reinforcement Learning of Multiple AIs
Training AI with strong and rich strategies in multi-agent environments
remains an important research topic in Deep Reinforcement Learning (DRL). The
AI's strength is closely related to its diversity of strategies, and this
relationship can guide us to train AI with both strong and rich strategies. To
prove this point, we propose Diversity is Strength (DIS), a novel DRL training
framework that can simultaneously train multiple kinds of AIs. These AIs are
linked through an interconnected history model pool structure, which enhances
their capabilities and strategy diversities. We also design a model evaluation
and screening scheme to select the best models to enrich the model pool and
obtain the final AI. The proposed training method provides diverse,
generalizable, and strong AI strategies without using human data. We tested our
method in an AI competition based on Google Research Football (GRF) and won the
5v5 and 11v11 tracks. The method enables a GRF AI to have a high level on both
5v5 and 11v11 tracks for the first time, which are under complex multi-agent
environments. The behavior analysis shows that the trained AI has rich
strategies, and the ablation experiments proved that the designed modules
benefit the training process
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