{"id":1382,"date":"2019-07-16T13:25:30","date_gmt":"2019-07-16T04:25:30","guid":{"rendered":"http:\/\/www.phys.s.u-tokyo.ac.jp\/en\/?p=1382"},"modified":"2025-07-16T15:08:13","modified_gmt":"2025-07-16T06:08:13","slug":"past-seminars","status":"publish","type":"post","link":"https:\/\/www.phys.s.u-tokyo.ac.jp\/en\/about\/1382\/","title":{"rendered":"Past Seminars"},"content":{"rendered":"\n

 Institute for Physics of Intelligence <\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\u30fbipi seminar July 7, 2025<\/a><\/td>\n\n

Kai Nakaishi
(RIKEN)
\u300cTemperature-induced critical phase transition in large language models\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar June 25, 2025<\/a><\/td>\nYuji Hirono
(University of Tsukuba)
\u300cDiffusion Models and Path Integrals: Interpreting Generative Models in the Language of Physics\u300d\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar May 19, 2025<\/a><\/td>\nSatoru Tokuda
(Kyushu University)
\u300c\u73fe\u8c61\u8ad6\u7684\u30e2\u30c7\u30ea\u30f3\u30b0\u306e\u305f\u3081\u306e\u30d9\u30a4\u30ba\u7d71\u8a08\u5b66\uff1a\u5b9f\u9a13\u3068\u7406\u8ad6\u306e\u6a4b\u6e21\u3057\u300d\/JP\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Apr. 21, 2025<\/a><\/td>\n\n

Taro Toyoizumi
(RIKEN)
\u300cModeling how the brain learns to represent the world\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 20, 2024<\/a><\/td>\nTakeru Yokota
(RIKEN)
\u300cPhysics-informed neural networks for solving functional renormalization group\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar Dec. 12, 2024<\/a><\/td>\nLingxiao Wang
(RIKEN-iTHEMS\uff09
\u300cPhysics-Driven Deep Learning for Solving Inverse Problems\u300d\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Nov.14, 2024<\/a><\/td>\nKenji Tanabe
(Toyota Technological Institute)
\u300c\u6a5f\u68b0\u5b66\u7fd2\u3092\u7528\u3044\u305f\u78c1\u6027\u8584\u819c\u306e\u65b0\u3057\u3044\u8a55\u4fa1\u6cd5\u306e\u958b\u767a\u300d\/JP\n

\u00a0<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Oct.7, 2024<\/a><\/td>\n\n

Takahiro Nishimichi
(Kyoto Sangyo University)
\u300cEmulators in Cosmological Inference Problem\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar July 19, 2024<\/a><\/td>\n\n

Yoh-ichi Mototake
(Hitotsubashi University)
\u300cInterpretable AI to assist scientists' insight into large pattern dynamics\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar June 5, 2024<\/a><\/td>\n\n

Takuya Isomura
(RIKEN)
\u300cBayesian mechanics of self-organizing and evolutionary systems\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar May 8, 2024<\/a><\/td>\n\n

Atsushi Yamamura
(Stanford University\uff09
\u300cStochastic Collapse: A Noise-induced Implicit Bias of Stochastic Gradient Descent\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Apr. 10, 2024<\/a><\/td>\n\n

Jun Takahashi
(The University of New Mexico)
\u300cA Semidefinite Programming approach for Quantum Heisenberg Ground States\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 27, 2023<\/a><\/td>\n\n

Tomoki Fukai
(OIST)
\u300cGraphical information representations in associative memory models\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 12, 2023<\/a><\/td>\n\n

Makoto Uemura
(Hiroshima University)
\u300cData Science in Astronomy and Observation of Galactic Transients\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 5, 2023<\/a><\/td>\n\n

Toshihiro Ota
(Cyber Agent AI Lab)
\u300cHopfield\/Mixer correspondence: towards a better understanding of MetaFormers architecture design\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Nov. 21, 2023<\/a><\/td>\n\n

Liu Ziyin
(The University of Tokyo)
\u300cUnderstand and analyze deep learning through the lens of symmetry\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar July 21, 2023<\/a><\/td>\n\n

Hayata Yamasaki\u00a0
(The University of Tokyo)
\u300cQuantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar June 9, 2023<\/a><\/td>\n\n

Akifumi Okuno\u00a0
(The Institute of Statistical Mathematics)
\u300cA Greedy and Optimistic Approach to Clustering with a Specified Uncertainty of Covariates\u300d<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar May 29, 2023<\/a><\/td>\n\n

Keiichi Tamai
(The University of Tokyo)
\u300cNon-equilibrium phase transitions in artificial deep neural networks\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Jan. 17, 2023<\/a><\/td>\n\n

Taro HITOSUGI
(The University of Tokyo)
\u300cMachine learning and robots change the way of our research\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 20, 2022<\/a><\/td>\n\n

Akio TOMIYA
(IPUT)
\u300cLattice QCD and Machine learning\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec. 8, 2022<\/a><\/td>\n\n

Tomer GALANTI
(Massachusetts Institute of Technology)
\u300cOn the Role of Neural Collapse in Transfer Learning\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Nov. 24, 2022<\/a><\/td>\n\n

Kenji HARADA
(Kyoto University)
\u300cCompressing neural networks by tensor networks\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Nov. 10, 2022<\/a><\/td>\n\n

Koji HASHIMOTO
(Kyoto University)
\u300cDeep learning and emergent spacetime\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Oct. 19, 2022<\/a><\/td>\n\n

Kana MORIWAKI
(The University of Tokyo)
\u300cDeep Learning Application for Reconstruction of Large-Scale Structure of the Universe\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar July 7, 2022<\/a><\/td>\n\n

Kentaro MINAMI
(Preferred Networks)
\u300cDeep Learning for Financial Applications\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar May 26, 2022<\/a><\/td>\n\n

Shinichiro AKIYAMA
(The University of Tokyo)
\u300cTensor renormalization group approach to quantum fields on a lattice\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Jan. 26, 2022<\/a><\/td>\n\n

Quoc Hoan TRAN
(The University of Tokyo)
\u300cQuantum Reservoir Computing - from Classical to Quantum Time Series Processing\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Jan. 11, 2022<\/a><\/td>\n\n

Ayaka SAKATA
(Institute of Statistical Mathematics)
<\/span>\u300cStatistical physics for the Bayesian statistical decision: an application to Group Testing\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Dec.16, 2021<\/a><\/td>\n\n

Syo YAIDA
(Meta AI)
<\/span>\u300cEffective Theory of Deep Neural Networks\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Nov.30. 2021<\/a><\/td>\n\n

Shirley HO
(Princeton University)
\u300cInterpreting (some) neural networks with symbolic regression\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar Oct. 28, 2021<\/a><\/td>\n\n

Hajime YOSHINO
(Osaka University\uff09
\u300cDesign space of a deep neural network - its spatial evolution and robustness\u300d<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar July 13, 2021<\/a><\/td>\n\n

Masaaki IMAIZUMI
(The University of Tokyo\uff09
\u300cGeneralization Analysis of Deep Learning: Implicit Regularization and Over-parameterization\u300d
<\/span><\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar June 9, 2021<\/a><\/td>\n\n

Lenka Zdeborova
(EPFL<\/span>)
\u300cUnderstanding machine learning via exactly solvable models\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar May 27, 2021<\/a><\/td>\n\n

Nobuyuki Yoshioka
(The University of Tokyo)
\u300cEncoding many-body quantum physics into neural networks\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar April 22, 2021<\/a><\/td>\n\n

Masayuki Ohzeki
(Tohoku University)
\u300cQuantum annealing and its application for practical problems\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar January 9, 2020<\/a><\/td>\n\n

Themis Palpanas
(Paris Descartes University\uff09
\u300cScalable Machine Learning on Large Sequence Collections\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar December 5, 2019<\/a><\/td>\n\n

Gentiane Venture
(Tokyo University of Agriculture and Technology)
Vincent Hernandez (Tokyo University of Agriculture and Technology & Surfclean)
\u300cDeep learning for human activity recognition\u300d<\/p>\n<\/td>\n<\/tr>\n

\u30fbipi seminar\u00a0October\u300017, 2019\u00a0<\/a><\/td>\nZiyin Liu
(The University of Tokyo, Department of Physics\uff09
\u300cCareful Deep Learning: Learning to Abstain by Training on A Simple Loss Function<\/span>\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar\u00a0October\u300011, 2019\u00a0<\/a><\/td>\nHidenori\u00a0Tanaka\u00a0
(Stanford University\uff09
\u300cFrom deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction<\/span>\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar\u00a0September\u30005, 2019\u00a0<\/a><\/td>\nShreya Sharma
(Data Science Research Laboratories, NEC Corporation\uff09
\u300cDeep learning in remote sensing\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar\u00a0August 8, 2019\u00a0<\/a><\/td>\nDenny Wu
(University of Toronto & Vector Institute for Artificial Intelligence \uff09
\u300cGeneralization of two-layer neural networks: an asymptotic view\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar\u00a0July 8, 2019\u00a0<\/a><\/td>\nkensuke Kobayashi
(Institute for Physics of Intelligence )
\u300cFluctuations in Mesoscopic Systems\u300d<\/td>\n<\/tr>\n
\u00a0\u30fbipi seminar June 6, 2019<\/a>\u00a0<\/td>\nMasato Shirasaki
(National Astronomical Observatory Japan (NAOJ))
\u300cDe-noising image of cosmic large-scale structure with deep learning\u300d\u00a0<\/td>\n<\/tr>\n
\u30fbipi seminar April 24, 2019<\/a><\/td>\nShiro Ikeda\u00a0
(The Institute of Statistical Mathematics)
\u300c\u30b9\u30d1\u30fc\u30b9\u30e2\u30c7\u30ea\u30f3\u30b0\u3068 Event Horizon Telescope \u306b\u3088\u308b\u30d6\u30e9\u30c3\u30af\u30db\u30fc\u30eb\u64ae\u50cf\u300d<\/td>\n<\/tr>\n
\u00a0\u30fbipi seminar April 4, 2019<\/a><\/td>\nTarin Clanuwat\u00a0
(Center for Open Data in the Humanities, National Institute of Informatics)
\u300cPre-modern Japanese Kuzushiji Character Recognition with Deep Learning\u300d<\/td>\n<\/tr>\n
\u30fbipi seminar March 7, 2019<\/a><\/td>\nShotaro (Shiba) Funai
(Okinawa Institute of Science and Technology (OIST))
\u300cRenomalization, Thermodynamics, and Feature Extraction of Machine Learning\u300d<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u21e6Top page<\/a><\/p>\n\n\n

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