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All Technical Committee Conferences (Searched in: All Years)
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Search Results: Conference Papers |
Conference Papers (Available on Advance Programs) (Sort by: Date Descending) |
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Committee |
Date Time |
Place |
Paper Title / Authors |
Abstract |
Paper # |
NC, IBISML, IPSJ-BIO, IPSJ-MPS [detail] |
2022-06-28 15:50 |
Okinawa |
(Primary: On-site, Secondary: Online) |
Causal Discovery in Discrete Data Using NML Code Length Based on MDL Principle Masatoshi Kobayashi, Nishimoto Hiroki, Shin Mastushima (Todai) NC2022-21 IBISML2022-21 |
Inference on the causal structure among random variables from only a finite number of observed data is one of the most i... [more] |
NC2022-21 IBISML2022-21 pp.149-155 |
NLP |
2019-05-10 13:50 |
Oita |
J:COM HoltoHALL OITA |
Structure estimation of a neural network using Inter-spike-interval Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Ikeguchi Tohru (TUS) NLP2019-3 |
In this paper, we apply the causal estimation method of Convergent Cross Mapping to a mathematical model of neural netwo... [more] |
NLP2019-3 pp.13-18 |
IA, IN (Joint) |
2018-12-13 14:45 |
Hiroshima |
Hiroshima Univ. |
Towards application of network topology information to network log causal anlaysis Satoru Kobayashi (NII), Kazuki Otomo (Univ. Tokyo), Kensuke Fukuda (NII) IA2018-40 |
To detect root causes of failures in large-scale networks, we need to extract contextual information from operational da... [more] |
IA2018-40 pp.1-8 |
CAS, NLP |
2018-10-18 14:15 |
Miyagi |
Tohoku Univ. |
Estimation accuracy of causal relation on difference in network structures Kazuya Sawada (TUS), Yutaka Shimada (Saitama Univ.), Tohru Ikeguchi (TUS) CAS2018-43 NLP2018-78 |
In this paper, we applied Convergent Cross Mapping to estimate causal relations between multiple timeseries. We used a c... [more] |
CAS2018-43 NLP2018-78 pp.33-38 |
IBISML |
2016-11-16 15:00 |
Kyoto |
Kyoto Univ. |
Additive Model Decomposition with Global Sparse Structure for Multi-task Granger Causal Estimation Hitoshi Abe, Jun Sakuma (Univ. Tsukuba) IBISML2016-56 |
Causality estimation is one of the key issues in time-series data analysis.
Granger causality is widely known as a form... [more] |
IBISML2016-56 pp.73-79 |
IBISML |
2011-11-10 15:45 |
Nara |
Nara Womens Univ. |
A Method for Estimating Binary Data Generating Process Takanori Inazumi, Takashi Washio, Shohei Shimizu, Joe Suzuki (Osaka Univ.), Akihiro Yamamoto (Kyoto Univ.), Yoshinobu Kawahara (Osaka Univ.) IBISML2011-65 |
In our previous study, we proposed a method to identify a data generation process governing its given binary data set. H... [more] |
IBISML2011-65 pp.155-162 |
NC, MBE (Joint) |
2011-03-08 14:35 |
Tokyo |
Tamagawa University |
Improved Granger causality tests for network structure estimation from time-series data Hikaru Harima, Shigeyuki Oba, Shin Ishii (Kyoto Univ.) NC2010-178 |
Granger causality and its variants have been proposed for estimating network structure as a causality graph based on cor... [more] |
NC2010-178 pp.301-306 |
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