PPT-TL1 Team Approach to Investigate Intracranial Networks of Attention and Learning: Relevance

Sarah Long and Catherine Tocci Reaction Time Accuracy Intracranial AND Surface EEG Implicit Learning Contextual Cuing Task Refractory Epilepsy Single timepoint Traumatic Brain Injury TBI. Clickthroughs. for News Search. Hongning. Wang. +. , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. +. CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. Ex. 1. Determine whether each function is continuous at the given . x. value(s). Justify using the continuity test. If discontinuous, identify the type of discontinuity as . infinite, jump, . or. Does Visual Attention Modulate Visual Evoked Potentials?. The theory is that Visual Attention modulates visual information at the level of visual cortex. How would you design an experiment to test this theory?. 2011-11709. Seo. . Seok. . Jun. Abstract. Video information retrieval. Finding info. relevant to query. Approach. Pseudo-relevance feedback. Negative PRF. Questions. How this paper approach to content-based video retrieval. Deep Learning @ . UvA. UVA Deep Learning COURSE - Efstratios Gavves & Max Welling. LEARNING WITH NEURAL NETWORKS . - . PAGE . 1. Machine Learning Paradigm for Neural Networks. The Backpropagation algorithm for learning with a neural network. vs.. Weak Induction. Homework. Study Fallacies 1-18. Review pp. 103-132. Fallacies (definition § 4.1). § 4.2 Fallacies of Relevance (1 – 8). § 4.3 Fallacies of Weak Induction (9 – 14). For Next Class: pp. 139-152. Hillyard. et al. (1960s) showed attention effects in human auditory pathway using ERP. Selective listening task using headphones. Every few minutes the attended side was reversed. Thus they could measure the brain response to identical stimuli when attended or unattended. Early Selection. Early Selection . model postulated that attention acted as a strict gate at the lowest levels of sensory processing. Based on concept of a limited capacity . bottleneck. Late Selection. David Collings (ECU) and Bruce Guthrie (GCA. ). In this session: . Supplementing the UES. Why workplace relevance?. WRS Development. Source, versions, items. Workplace Relevance Scale. Dennis . Trewen. Cranborne. Primary School- A collaborative learning approach. Alan Cocker. Cranborne Primary. Learning Together. What is good learning?. Explore what enables good learning?. Top down or bottom up?. How much of your own CPD is top down?. Clickthroughs. for News Search. Hongning. Wang. . , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. . CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. What’s new in ANNs in the last 5-10 years?. Deeper networks, . m. ore data, and faster training. Scalability and use of GPUs . ✔. Symbolic differentiation. ✔. reverse-mode automatic differentiation. Tae Jun Ham. , Sung Jun Jung, . Seonghak. Kim, Young H. Oh, . Yeonhong. Park, . Yoonho. Song, Jung-Hun Park, . Sanghee. Lee, . Kyoung. Park, Jae W. Lee, . Deog-Kyoon. . Jeong. SEOUL NATIONAL. Models and applications. Outline. Sequence Data. Recurrent Neural Networks Variants. Handling Long Term Dependencies. Attention Mechanisms. Properties of RNNs. Applications of RNNs. Hands-on LSTM-supported timeseries prediction.