16,008 research outputs found
Migration, Marginalization, and Institutional Injustice in the Rural South
At the new beginning of the next 50 years of the Southern Rural Sociological Association (SRSA), the SRSA Presidential Address calls for attention to the issues that rural immigrants have faced – the everyday experiences of international migrants, their marginalization, and institutional injustice in rural America, particularly in the rural South. These issues have often been ignored or downplayed in the larger dialogue on rural issues and in the public debates about immigration policy, even though these social problems have been a perennial issue. Rural social scientists are challenged to be organic intellectuals who do not hide in the ivory tower of the academy, but rather use our intellect to diagnose the ills of society and help exploited rural migrants better understand their situation and the most fruitful strategies available to them to improve their lives and achieve a more just and humane society
Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
Many recent works on knowledge distillation have provided ways to transfer
the knowledge of a trained network for improving the learning process of a new
one, but finding a good technique for knowledge distillation is still an open
problem. In this paper, we provide a new perspective based on a decision
boundary, which is one of the most important component of a classifier. The
generalization performance of a classifier is closely related to the adequacy
of its decision boundary, so a good classifier bears a good decision boundary.
Therefore, transferring information closely related to the decision boundary
can be a good attempt for knowledge distillation. To realize this goal, we
utilize an adversarial attack to discover samples supporting a decision
boundary. Based on this idea, to transfer more accurate information about the
decision boundary, the proposed algorithm trains a student classifier based on
the adversarial samples supporting the decision boundary. Experiments show that
the proposed method indeed improves knowledge distillation and achieves the
state-of-the-arts performance.Comment: Accepted to AAAI 201
Jenseits des culturul turn
Auslands-DaF leidet stets unter der Diskrepanz zwischen Theorie und Praxis. ‚Exportierte‘ Konzepte gelten in der Regel als Diktat und sind schwer zu integrieren. Mit der Tendenz der Kulturkundisierung der Germanistik wird in Korea Schritt gehalten. Der DaF-Unterricht wird aber nach wie vor dem Zufall überlassen. Davor warnt die Autorin und appelliert an die DaFler in Deutschland, die Bedürfnisse des Auslands wahrzunehmen
Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons
An activation boundary for a neuron refers to a separating hyperplane that
determines whether the neuron is activated or deactivated. It has been long
considered in neural networks that the activations of neurons, rather than
their exact output values, play the most important role in forming
classification friendly partitions of the hidden feature space. However, as far
as we know, this aspect of neural networks has not been considered in the
literature of knowledge transfer. In this paper, we propose a knowledge
transfer method via distillation of activation boundaries formed by hidden
neurons. For the distillation, we propose an activation transfer loss that has
the minimum value when the boundaries generated by the student coincide with
those by the teacher. Since the activation transfer loss is not differentiable,
we design a piecewise differentiable loss approximating the activation transfer
loss. By the proposed method, the student learns a separating boundary between
activation region and deactivation region formed by each neuron in the teacher.
Through the experiments in various aspects of knowledge transfer, it is
verified that the proposed method outperforms the current state-of-the-art.Comment: Accepted to AAAI 201
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