271 research outputs found
Optimising for Interpretability: Convolutional Dynamic Alignment Networks
We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent interpretability. Their core building blocks are Dynamic Alignment Units (DAUs), which are optimised to transform their inputs with dynamically computed weight vectors that align with task-relevant patterns. As a result, CoDA Nets model the classification prediction through a series of input-dependent linear transformations, allowing for linear decomposition of the output into individual input contributions. Given the alignment of the DAUs, the resulting contribution maps align with discriminative input patterns. These model-inherent decompositions are of high visual quality and outperform existing attribution methods under quantitative metrics. Further, CoDA Nets constitute performant classifiers, achieving on par results to ResNet and VGG models on e.g. CIFAR-10 and TinyImagenet. Lastly, CoDA Nets can be combined with conventional neural network models to yield powerful classifiers that more easily scale to complex datasets such as Imagenet whilst exhibiting an increased interpretable depth, i.e., the output can be explained well in terms of contributions from intermediate layers within the network
Intelligente Artefakte als Herausforderung fĂŒr Soziologie und TA: EinfĂŒhrung in den Schwerpunkt
Intelligente Artefakte als Herausforderung fĂŒr Soziologie und TA: EinfĂŒhrung in den Schwerpunkt
Spatial-domain interferometer for measuring plasma mirror expansion
International audienceWe present a practical spatial-domain interferometer for characterizing the electronic density gradient of laser- induced plasma mirrors with sub-30-femtosecond temporal resolution. Time-resolved spatial imaging of an intensity- shaped pulse reflecting off an expanding plasma mirror in- duced by a time-delayed pre-pulse allows us to measure characteristic plasma gradients of 10â100 nm with an ex- pansion velocity of 10.8 nm/ps. Spatial-domain interferom- etry (SDI) can be generalized to the ultrafast imaging of nm to ÎŒm size laser-induced phenomena at surfaces
Testing the robustness of attribution methods for convolutional neural networks in MRI-based Alzheimer's disease classification
Attribution methods are an easy to use tool for investigating and validating
machine learning models. Multiple methods have been suggested in the literature
and it is not yet clear which method is most suitable for a given task. In this
study, we tested the robustness of four attribution methods, namely
gradient*input, guided backpropagation, layer-wise relevance propagation and
occlusion, for the task of Alzheimer's disease classification. We have
repeatedly trained a convolutional neural network (CNN) with identical training
settings in order to separate structural MRI data of patients with Alzheimer's
disease and healthy controls. Afterwards, we produced attribution maps for each
subject in the test data and quantitatively compared them across models and
attribution methods. We show that visual comparison is not sufficient and that
some widely used attribution methods produce highly inconsistent outcomes
Service robotics: do you know your new companion? Framing an interdisciplinary technology assessment
Service-Roboticâmainly defined as ânon-industrial roboticsââis identified as the next economical success story to be expected after robots have been ubiquitously implemented into industrial production lines. Under the heading of service-robotic, we found a widespread area of applications reaching from robotics in agriculture and in the public transportation system to service robots applied in private homes. We propose for our interdisciplinary perspective of technology assessment to take the human user/worker as common focus. In some cases, the user/worker is the effective subject acting by means of and in cooperation with a service robot; in other cases, the user/worker might become a pure object of the respective robotic system, for example, as a patient in a hospital. In this paper, we present a comprehensive interdisciplinary framework, which allows us to scrutinize some of the most relevant applications of service robotics; we propose to combine technical, economical, legal, philosophical/ethical, and psychological perspectives in order to design a thorough and comprehensive expert-based technology assessment. This allows us to understand the potentials as well as the limits and even the threats connected with the ongoing and the planned implementation of service robots into human lifeworldâparticularly of those technical systems displaying increasing grades of autonomy
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