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Current deep learning methods are regarded as favorable if they empirically perform well on dedicated test sets. This mentality is seamlessly reflected in the resurfacing area of continual learning, where consecutively arriving data is investigated. The core challenge is framed as protecting previously acquired representations from being catastrophically forgotten. However, comparison of individual methods is nevertheless performed in isolation from the real world by monitoring accumulated benchmark test set performance. The closed world assumption remains predominant, i.e. models are evaluated on data that is guaranteed to originate from the same distribution as used for training. This poses a massive challenge as neural networks are well known to provide overconfident false predictions on unknown and corrupted instances. In this work we critically survey the literature and argue that notable lessons from open set recognition, identifying unknown examples outside of the observed set, and the adjacent field of active learning, querying data to maximize the expected performance gain, are frequently overlooked in the deep learning era. Hence, we propose a consolidated view to bridge continual learning, active learning and open set recognition in deep neural networks. Finally, the established synergies are supported empirically, showing joint improvement in alleviating catastrophic forgetting, querying data, selecting task orders, while exhibiting robust open world application.
Event-related potentials (ERPs) are widely used in basic neuroscience and in clinical diagnostic procedures. In contrast, neurophysiological insights from ERPs have been limited, as several different mechanisms lead to ERPs. Apart from stereotypically repeated responses (additive evoked responses), these mechanisms are asymmetric amplitude modulations and phase-resetting of ongoing oscillatory activity. Therefore, a method is needed that differentiates between these mechanisms and moreover quantifies the stability of a response. We propose a constrained subspace independent component analysis that exploits the multivariate information present in the all-to-all relationship of recordings over trials. Our method identifies additive evoked activity and quantifies its stability over trials. We evaluate identification performance for biologically plausible simulation data and two neurophysiological test cases: Local field potential (LFP) recordings from a visuo-motor-integration task in the awake behaving macaque and magnetoencephalography (MEG) recordings of steady-state visual evoked fields (SSVEFs). In the LFPs we find additive evoked response contributions in visual areas V2/4 but not in primary motor cortex A4, although visually triggered ERPs were also observed in area A4. MEG-SSVEFs were mainly created by additive evoked response contributions. Our results demonstrate that the identification of additive evoked response contributions is possible both in invasive and in non-invasive electrophysiological recordings.
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.
Wie eine "Heilslehre" überzieht der Begriff "Digitalisierung" fast alle Lebensbereiche – natürlich auch den Bildungsbereich. Gerade wir Informatiker*innen sind gefordert, diese Wege der Bildungstransformation mitzugestalten. Wir zusammen mit den Erziehungswissenschaftler*innen und Psychologen*innen müssen identifizieren, aufzeigen und vorbildlich umsetzen, was sinnvoll und möglich ist. Wir sind diejenigen, die die Bedingungen des Gelingens und auch die der Irrwege erforschen und aufzeigen müssen. Digitalisierungswahnsinn brauchen wir nicht!
Die 16. Jahrestagung DeLFI 2018 der Fachgruppe eLearning der Gesellschaft für Informatik e. V. findet vom 10. bis 13.September 2018 an der Johann Wolfgang Goethe – Universität, Frankfurt am Main statt, gemeinsam mit der 8. Tagung für Hochschuldidaktik der Informatik HDI 2018. ...
An der Universität Frankfurt entwickelte Online-Self-Assessment-Verfahren für die Studiengänge Psychologie und Informatik sollen Studieninteressierten noch vor Studienbeginn auf der Basis von Selbsterkundungsmaßnahmen und Tests eine Rückmeldung über ihre eigenen Fähigkeiten, Motive, personalen Kompetenzen und Interessen mit Blick auf den jeweiligen Studiengang geben. Sowohl die Befunde zur psychometrischen Güte der Verfahren als auch jene zur prognostischen Validität lassen ihren Einsatz zur Feststellung studienrelevanter Kompetenzen als geeignet erscheinen. Da die erfassten Kompetenzen und Merkmale substanzielle Beziehun-gen zu Studienleistungen aufweisen, könnten die Informationen über individuelle Stärken zur Wahl eines geeigneten Studienganges genutzt werden; Schwächen hingegen könnten frühzeitig Hinweise für geeignete Fördermaßnahmen liefern.