Добірка наукової літератури з теми "Discriminative Encoding"
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Статті в журналах з теми "Discriminative Encoding"
Feng, Lin, Yang Liu, Zan Li, Meng Zhang, Feilong Wang, and Shenglan Liu. "Discriminative bit selection hashing in RGB-D based object recognition for robot vision." Assembly Automation 39, no. 1 (February 4, 2019): 17–25. http://dx.doi.org/10.1108/aa-03-2018-037.
Повний текст джерелаZhao, Yuehua, Jie Ma, Qian Wang, Mao Ye, and Lin Wu. "Encoding discriminative representation for point cloud semantic segmentation." Electronics Letters 57, no. 6 (February 23, 2021): 258–60. http://dx.doi.org/10.1049/ell2.12118.
Повний текст джерелаBansal, Vipul, Himanshu Buckchash, and Balasubramanian Raman. "Discriminative Auto-Encoding for Classification and Representation Learning Problems." IEEE Signal Processing Letters 28 (2021): 987–91. http://dx.doi.org/10.1109/lsp.2021.3077853.
Повний текст джерелаSivaraman, Deepa, Jeneetha Jebanazer, and Bhuvaneswari Balasubramanian. "Discriminative analysis of wavelets for efficient medical image compression." Indonesian Journal of Electrical Engineering and Computer Science 30, no. 1 (April 1, 2023): 510. http://dx.doi.org/10.11591/ijeecs.v30.i1.pp510-517.
Повний текст джерелаLiu, Peixin, Xiaofeng Li, Han Liu, and Zhizhong Fu. "Online Learned Siamese Network with Auto-Encoding Constraints for Robust Multi-Object Tracking." Electronics 8, no. 6 (May 28, 2019): 595. http://dx.doi.org/10.3390/electronics8060595.
Повний текст джерелаLi, Fuqiang, Tongzhuang Zhang, Yong Liu, and Feiqi Long. "Deep Residual Vector Encoding for Vein Recognition." Electronics 11, no. 20 (October 13, 2022): 3300. http://dx.doi.org/10.3390/electronics11203300.
Повний текст джерелаTavares, Gabriel, and Sylvio Barbon. "Matching business process behavior with encoding techniques via meta-learning: An anomaly detection study." Computer Science and Information Systems, no. 00 (2023): 5. http://dx.doi.org/10.2298/csis220110005t.
Повний текст джерелаShakeel, M. Saad, and Kin-Man Lam. "Deep-feature encoding-based discriminative model for age-invariant face recognition." Pattern Recognition 93 (September 2019): 442–57. http://dx.doi.org/10.1016/j.patcog.2019.04.028.
Повний текст джерелаKim, Yeongbin, Joongchol Shin, Hasil Park, and Joonki Paik. "Real-Time Visual Tracking with Variational Structure Attention Network." Sensors 19, no. 22 (November 9, 2019): 4904. http://dx.doi.org/10.3390/s19224904.
Повний текст джерелаRescorla, Robert A. "Elemental and Configural Encoding of the Conditioned Stimulus." Quarterly Journal of Experimental Psychology Section B 56, no. 2b (May 2003): 161–76. http://dx.doi.org/10.1080/02724990244000089.
Повний текст джерелаДисертації з теми "Discriminative Encoding"
Aime, Mattia. "Circuit mechanisms for encoding discriminative learning in the dorsal prefrontal cortex of behaving mice." Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0805/document.
Повний текст джерелаThe ability of an organism to predict forthcoming events is crucial for survival, and depends on the repeated contingency and contiguity between sensory cues and the events (i.e. danger) they must predict. The resulting learned association provides an accurate representation of the environment by increasing discriminative skills between threat and safety signals, most likely as a result of the interaction between the prefrontal cortex (PFC) and the basolateral amygdala (BLA). Although it suggests that local neuronal networks in the PFC might encode opposing memories that are preferentially selected during recall by recruiting specific cortical or subcortical structures, whether such a discriminative representation is wired within discrete prefrontal circuits during learning and by which synaptic mechanisms remain unclear. Here, the work at issue demonstrates that discrimination learning of both safe and fear-conditioned stimuli depends on full activity of the frontal association cortex, and is associated with the formation of cue-specific neuronal assemblies therein. During learning, prefrontal pyramidal neurons were potentiated through sensory-driven dendritic non-linearities supported by the activation of long-range inputs from the basolateral amygdala (BLA). Taken together, these data provide evidence for a new synaptic level mechanism that coincidently link (or meta-associate) during learning features of perceived experience with BLA mediated emotional state into prefrontal memory assemblies
Saal, Hannes. "Information theoretic approach to tactile encoding and discrimination." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5737.
Повний текст джерелаPidgeon, Laura Marie. "Encoding contributions to mnemonic discrimination and its age-related decline." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/15739.
Повний текст джерелаMcLean, Jennifer E. "Processing capacity of visual perception and memory encoding /." Thesis, Connect to this title online; UW restricted, 1999. http://hdl.handle.net/1773/9019.
Повний текст джерелаWang, Xun. "Enhanced colour encoding of materials discrimination information for multiple view dual-energy X-ray imaging." Thesis, Nottingham Trent University, 2009. http://irep.ntu.ac.uk/id/eprint/300/.
Повний текст джерелаBillard, Pauline. "Cοmparative study οf episοdic memοry in cοmmοn cuttlefish (Sepia οfficinalis) and Εurasian jay (Garrulus glandarius) Cuttlefish retrieve whether they smelt or saw a previously encountered item A new paradigm for assessing discriminative learning and incidental encoding of task-irrelevant contextual cues in Eurasian jays Cuttlefish show flexible and future-dependent foraging cognition Exploration of future-planning in the common cuttlefish Neuronal substrates of episodic-like memory in cuttlefish". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMC227.
Повний текст джерелаSome authors support that mental time travel is unique to humans. To their point of view, animals are not able to project themselves into the past of the future because they are bound into the present. Nevertheless, during the last 30 years, researchers have brought considerable knowledge on animals’ capacities to travel mentally through time. Even though opinions have evolved, the debate concerning the unicity of mental time travel is still on. My PhD thesis aimed at bringing further knowledge on this matter by focusing on an innovative aspect of episodic cognition in common cuttlefish, Sepia officinalis and Eurasian jay, Garrulus glandarius, namely, source-memory. Source-memory is the capacity to retrieve the origin of an episodic memory. Results showed that cuttlefish were able to perform a source-discrimination study, revealing that they were able to discriminate and retrieve their own perceptions after 3-hours delay. A study on jays’ capacity to encode incidentally a contextual information (contextual source) revealed unexpected differences between males and females. Investigation of future-oriented behaviour in cuttlefish showed that they were able to take a decision in the present according to previous encoded knowledge and according to future experimental conditions. A preliminary study also revealed promising results on cuttlefish capacity to anticipate their future needs. To finish, we explored and revealed for the first time the neuronal substrates of episodic-like memory in cuttlefish. Alltogether, these results provide new knowledge on mental time travel in cuttlefish and in jays, suggesting that this capacity would have evolved under different environmental contraints
Pauzin, François Philippe [Verfasser], Patrick [Gutachter] Krieger, and Stephan [Gutachter] Herlitze. "A corticothalamic circuit for refining tactile encoding : a switch between feature detection and discrimination / François Philippe Pauzin ; Gutachter: Patrick Krieger, Stephan Herlitze ; International Graduate School of Neuroscience." Bochum : Ruhr-Universität Bochum, 2018. http://d-nb.info/117520496X/34.
Повний текст джерелаMuriithi, Paul Mutuanyingi. "A case for memory enhancement : ethical, social, legal, and policy implications for enhancing the memory." Thesis, University of Manchester, 2014. https://www.research.manchester.ac.uk/portal/en/theses/a-case-for-memory-enhancement-ethical-social-legal-and-policy-implications-for-enhancing-the-memory(bf11d09d-6326-49d2-8ef3-a40340471acf).html.
Повний текст джерелаBharmauria, Vishal. "Investigating the encoding of visual stimuli by forming neural circuits in the cat primary visual cortex." Thèse, 2016. http://hdl.handle.net/1866/14129.
Повний текст джерелаBackground ‘Connectomics’— the mapping of neural connections, is a rapidly advancing field in neurosciences and it promises significant insights into the brain functioning. The formation of neuronal circuits in response to the sensory environment is an emergent property of the brain; however, the knowledge about the precise nature of these sub-networks is still limited. Even at the level of the visual cortex, which is the most studied area in the brain, how sensory inputs are processed between its neurons, is a question yet to be completely explored. Heuristically, this invites an investigation into the emergence of micro-circuits in response to a visual input — that is, how the intriguing interplay between a stimulus and a cell assembly is engineered and modulated? Methods Neuronal assemblies were recorded in response to randomly presented drifting sine-wave gratings in the layer II/III (area 17) of the primary visual cortex (V1) in anaesthetized cats using tungsten multi-electrodes. Cross-correlograms (CCGs) between simultaneously recorded neural activities were computed to reveal the functional links between neurons that were indicative of putative synaptic connections between them. Further, the peristimulus time histograms (PSTH) of neurons were compared to divulge the epochal synergistic collaboration in the revealed functional networks. Thereafter, perievent spectrograms were computed to observe the gamma oscillations in emergent microcircuits. Noise correlation (Rsc) was calculated for the connected and unconnected neurons within these microcircuits. Results The functionally linked neurons collaborate synergistically with augmented activity in a 50-ms window of opportunity compared with the functionally unconnected neurons suggesting that the connectivity between neurons leads to the added excitability between them. Further, the perievent spectrogram analysis revealed that the connected neurons had an augmented power of gamma activity compared with the unconnected neurons in the emergent 50-ms window of opportunity. The low-band (20-40 Hz) gamma activity was linked to the regular-spiking (RS) neurons, whereas the high-band (60-80 Hz) activity was related to the fast-spiking (FS) neurons. The functionally connected neurons systematically displayed higher Rsc compared with the unconnected neurons in emergent microcircuits. Finally, the CCG analysis revealed that there is an activation of a salient functional network in an assembly in relation to the presented orientation. Closely tuned neurons exhibited more connections than the distantly tuned neurons. Untuned assemblies did not display functional linkage. In short, a ‘signature’ functional network was formed between neurons comprising an assembly that was strictly related to the presented orientation. Conclusion Indeed, this study points to the fact that a cell-assembly is the fundamental functional unit of information processing in the brain, rather than the individual neurons. This dilutes the importance of a neuron working in isolation, that is, the classical firing rate paradigm that has been traditionally used to study the encoding of a stimulus. This study also helps to reconcile the debate on gamma oscillations in that they systematically originate between the connected neurons in assemblies. Though the size of the recorded assemblies in the current investigation was relatively small, nevertheless, this study shows the intriguing functional specificity of interacting neurons in an assembly in response to a visual input. One may form this study as a premise to computationally infer the functional connectomes on a larger scale.
Частини книг з теми "Discriminative Encoding"
Sargent, S. "The Listening Styles Profile." In Handbook of Research on Electronic Surveys and Measurements, 334–38. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59140-792-8.ch045.
Повний текст джерелаТези доповідей конференцій з теми "Discriminative Encoding"
Wang, Zongji, and Feng Lu. "Single Image Intrinsic Decomposition with Discriminative Feature Encoding." In 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW). IEEE, 2019. http://dx.doi.org/10.1109/iccvw.2019.00531.
Повний текст джерелаMemmesheimer, Raphael, Nick Theisen, and Dietrich Paulus. "Gimme Signals: Discriminative signal encoding for multimodal activity recognition." In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020. http://dx.doi.org/10.1109/iros45743.2020.9341699.
Повний текст джерелаXie, Yurui, and Ling Guan. "A Semi-Handcrafted Keypoint Detector with Discriminative Feature Encoding." In ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2022. http://dx.doi.org/10.1109/icassp43922.2022.9747017.
Повний текст джерелаStober, Sebastian. "Learning discriminative features from electroencephalography recordings by encoding similarity constraints." In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2017. http://dx.doi.org/10.1109/icassp.2017.7953343.
Повний текст джерелаMa, Ailong, Yanfei Zhong, Bei Zhao, Hongzan Jiao, and Liangpei Zhang. "Spectral-spatial DNA encoding discriminative classifier for hyperspectral remote sensing imagery." In IGARSS 2015 - 2015 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2015. http://dx.doi.org/10.1109/igarss.2015.7326117.
Повний текст джерелаPedrosa, Glauco V., and Agma J. M. Traina. "Compact and discriminative approach for encoding spatial-relationship of visual words." In SAC 2015: Symposium on Applied Computing. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2695664.2695951.
Повний текст джерелаRao, Haocong, Siqi Wang, Xiping Hu, Mingkui Tan, Huang Da, Jun Cheng, and Bin Hu. "Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/125.
Повний текст джерелаWang, Kuikui, Lu Yang, Gongping Yang, and Yilong Yin. "Integration of discriminative features and similarity-preserving encoding for finger vein image retrieval." In 2017 IEEE International Conference on Image Processing (ICIP). IEEE, 2017. http://dx.doi.org/10.1109/icip.2017.8296938.
Повний текст джерелаRao, Haocong, Shihao Xu, Xiping Hu, Jun Cheng, and Bin Hu. "Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/135.
Повний текст джерелаXu, Suping, Lin Shang, and Furao Shen. "Latent Semantics Encoding for Label Distribution Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/553.
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