1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Likelihood is sometimes difficult to compute because of the complexity of the model. Approximate Bayesian computation (ABC) makes it easy to sample parameters generating approximation of observed data.
論文紹介:Grad-CAM: Visual explanations from deep networks via gradient-based loca...Kazuki Adachi
Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618-626
1. The document discusses energy-based models (EBMs) and how they can be applied to classifiers. It introduces noise contrastive estimation and flow contrastive estimation as methods to train EBMs.
2. One paper presented trains energy-based models using flow contrastive estimation by passing data through a flow-based generator. This allows implicit modeling with EBMs.
3. Another paper argues that classifiers can be viewed as joint energy-based models over inputs and outputs, and should be treated as such. It introduces a method to train classifiers as EBMs using contrastive divergence.
Likelihood is sometimes difficult to compute because of the complexity of the model. Approximate Bayesian computation (ABC) makes it easy to sample parameters generating approximation of observed data.
論文紹介:Grad-CAM: Visual explanations from deep networks via gradient-based loca...Kazuki Adachi
Selvaraju, Ramprasaath R., et al. "Grad-cam: Visual explanations from deep networks via gradient-based localization." The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 618-626
國立中央大學客家學院
客家研究的回顧與展望
作者:王東
王東,1963年生,安徽人,北京中國上海華東師範大學歷史學博士,現為上海華東師範大學歷史學系教授,研究方向為史學理論與史學史、中國區域文化史、客家研究。著有:《客家學導論》(上海人民出版社,1996年;台北南天書局,1998年)、《社會結構與客家人教育》(湖南教育出版社,2003年)、《那方山水那方人:客家源流新說》(上海華東師範大學出版社,2007年)。在《歷史研究》、《中國社會科學》、“Social Sciences in China ”、《史學理論研究》等北京中國國內外學術其期刊上,發表學術論文、譯文、書評等百餘篇。
The document is a report on expat life from a survey of over 21,000 expats globally. Some key findings from the report include:
- Singapore ranks first overall as the best place for expats to live and work, providing career opportunities and a stable economy. Expats there report an improved quality of life.
- Expats move abroad more for improved quality of life and new challenges rather than just higher salaries. Career progression, learning new skills, and integrating into new cultures are priorities.
- New Zealand ranks first for overall expat experience. Expats there enjoy an improved quality of life and increased physical activity due to the outdoor lifestyle.
- Sweden ranks first for family life, with high quality