ベイズ恷m晒によるハイパ`パラメ`タ冥沫についてざっくりと盾hしました。
書指B初する坪否の圷となった猟
Bergstra, James, et al. "Algorithms for hyper-parameter optimization." 25th annual conference on neural information processing systems (NIPS 2011). Vol. 24. Neural Information Processing Systems Foundation, 2011.
https://hal.inria.fr/hal-00642998/
[DLi氏]侮晒僥はなぜyしいのかWhy Deep RL fails? A brief survey of recent works.Deep Learning JP
?
Deep reinforcement learning algorithms often fail to learn complex tasks. Recent works have identified three issues that form a "deadly triad" contributing to this problem: non-stationary targets, high variance, and positive correlation. New algorithms aim to address these issues by improving exploration, stabilizing learning, and decorrelating updates. Overall, deep reinforcement learning remains a challenging area with opportunities to develop more data-efficient and generally applicable algorithms.
1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
臼翫寄仇, 弌勸F, "鏡羨來児覆鰉辰い新背ミ仭侑鰈啖崕發森議な兜豚Q協隈," 晩云咄僥氏 2016定敢湿冩梢k燕氏, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
This document provides an overview of POMDP (Partially Observable Markov Decision Process) and its applications. It first defines the key concepts of POMDP such as states, actions, observations, and belief states. It then uses the classic Tiger problem as an example to illustrate these concepts. The document discusses different approaches to solve POMDP problems, including model-based methods that learn the environment model from data and model-free reinforcement learning methods. Finally, it provides examples of applying POMDP to games like ViZDoom and robot navigation problems.
[DLi氏]侮晒僥はなぜyしいのかWhy Deep RL fails? A brief survey of recent works.Deep Learning JP
?
Deep reinforcement learning algorithms often fail to learn complex tasks. Recent works have identified three issues that form a "deadly triad" contributing to this problem: non-stationary targets, high variance, and positive correlation. New algorithms aim to address these issues by improving exploration, stabilizing learning, and decorrelating updates. Overall, deep reinforcement learning remains a challenging area with opportunities to develop more data-efficient and generally applicable algorithms.
1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
臼翫寄仇, 弌勸F, "鏡羨來児覆鰉辰い新背ミ仭侑鰈啖崕發森議な兜豚Q協隈," 晩云咄僥氏 2016定敢湿冩梢k燕氏, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
This document provides an overview of POMDP (Partially Observable Markov Decision Process) and its applications. It first defines the key concepts of POMDP such as states, actions, observations, and belief states. It then uses the classic Tiger problem as an example to illustrate these concepts. The document discusses different approaches to solve POMDP problems, including model-based methods that learn the environment model from data and model-free reinforcement learning methods. Finally, it provides examples of applying POMDP to games like ViZDoom and robot navigation problems.
Scan Registration for Autonomous Mining Vehicles Using 3D-NDTKitsukawa Yuki
?
冩梢片のゼミの猟B初のk燕Y創です。
Magnusson, M., Lilienthal, A. and Duckett, T. (2007), Scan registration for autonomous mining vehicles using 3D-NDT. J. Field Robotics, 24: 803C827. doi: 10.1002/rob.20204
Realization of Innovative Light Energy Conversion Materials utilizing the Sup...RCCSRENKEI
?
The document is very short and does not provide much context. It contains only one word: "Real". From this limited information, it is difficult to construct an informative summary in 3 sentences or less.
Current status of the project "Toward a unified view of the universe: from la...RCCSRENKEI
?
This document summarizes the current status of a large, multi-institutional project in Japan aimed at developing a unified understanding of structure formation in the universe through multi-level simulations and observations. The project involves over 90 researchers across 21 institutions. It is divided into four sub-projects focusing on: large-scale structures and galaxy formation (Sub A); molecular clouds and planetary formation (Sub B); black holes, supernovae, and radiation transport (Sub C); and the solar system, Venus, and gas giant planets (Sub D). Several key simulations have been performed achieving unprecedented resolution, including galaxy formation at the star-by-star level, globular cluster dynamics, and a 12.8 billion point simulation of solar conve
Fugaku, the Successes and the Lessons LearnedRCCSRENKEI
?
The document summarizes the successes and lessons learned from Fugaku, Japan's flagship supercomputer. Key points include:
- Fugaku achieved the top performance on all HPC benchmarks in 2020 and 2021, showing high performance across applications, not just traditional HPC workloads.
- While many applications achieved their target performance, some did not due to issues like insufficient parallelism, I/O scalability problems, and compiler vectorization failures.
- Lessons include the need for improved software stacks, application analysis, and adapting to modern applications beyond classic HPC.
- Looking ahead, sustained exascale performance will require data-centric architectures and corresponding system software and algorithms as transistor scaling slow
2. 垢僥蛍勸での及匯圻尖麻へ鬚韻
狼のサイズ
rgスケ`ル
102 atom
103 C 106 atom
謹くのm喘箭がある.
マテリアルデザインも
みられ、謹くの撹孔
並箭がある。
DFT calculations of thousands atoms
is still a grand challenge.
O(N3) Low-order
DNA リチウム学
可創
5. 20定瘁に麻できるサイズのeもり #1
p
cN
exp( )b aT
麻楚
麻嬬薦
c: 協方
N: システムサイズ
p: 麻のオ`ダ`
b: 協方
a: 協方
狼のサイズをα蔚: N ★ α N の栽を深える。
麻嬬薦の鯢榔箸麻楚の寄曳と吉しいとおけるので、
( ) exp( ( ))
exp( )
p
p
c N b a T T
cN b aT
? ? ?
? exp( / )a T p? ? ??
書瘁もム`アの隈tが撹り羨ち、K双紳覆システムサイズや麻CのK
双業に卆贋しないと協すると (尖覽弔別周和)、20定瘁に麻できる
サイズの貧、鰔eもることが辛嬬である。
14か埖で麻嬬薦が屈蔚とすれば a=ln(2)/14、20定瘁? ΔT = 240 か埖。
7. O(N)隈の鮄段太
CDMSI: Sub-issue D
Magnetic materials
CDMSI: Sub-issue E Structural materials
CDMSI: Sub-issue F
Functional chemical materials
AIST: Batteries
http://ev.nissan.co.jp/LE
AF/PERFORMANCE/
Ionic liquid
CBSM2:
Sub-issue C Materials
in deep earth
17. Wannierv方と畜業佩双
occ
3
BZ
| | exp( )
(2 )
k
m
V
dk U ik R? ?? ?? ?
?
? ? ? ???
Wannierv方 はBlochv方ψのunitaryQから誼られる。?
バンドギャップを嗤する栽
occ
, , ,
1
exp( )nij R n i k j k
B B
n dk ik R c c
V
? ?
?
? ??
,( , ') ( ) ( ' )nij R i i j j n
n
n r r n r r R? ? ? ?? ? ? ??
畜業佩双はBlochv方ψの畜業處麻徨への符唹から誼られる。
x柊晒された肝塀を旋喘して貧のBAv方が誼られる。
19. Wannierv方の蕉侭來
O-2px in PbTiO3
Orbital in Aluminum
峺方v方p縫
べきp縫
J.Battacharjee and
U.W.Waghmare, PRB
73, 121102 (2006)
磯悶式び~F悶においてはWannierv方は峺方v方p縫し、署奉では
べきp縫する。1D狼にしては方僥議にな盾裂(He and Vanderbilt,
PRL 86, 5341)が佩われている。匯違の栽にする訳周原きの方僥議
^苧はBrouder et al., PRL 98, 046402でhされている。
20. 畜業佩双の蕉侭來
嗤泪ャップ狼
峺方v方p縫
署奉
T=0 べきp縫
0<T 峺方v方p縫
D.R.Bowler et al.,
Modell.Siml.Mater.Sci.Eng.
5, 199 (1997)
T=0Kにおいて、磯悶式び~F悶においては畜業佩双は峺方v方p縫
し、署奉ではべきp縫する。嗤淮其箸任禄霾瑤砲いても峺方p縫する。
p縫蒙來にする方僥議なhは Ismail-Beigi et al, PRL 82, 2127を
歌孚のこと。
21. ?なオ`ダ`N隈
Wannier v方 (WF)
畜業佩双 (DM)
箏峽 (V)
啖 (P)
屈つの恷m篳とその恷m晒返隈のMみ栽わせか
ら富なくとも4Nの圭隈が深えられる。
DM+PDM+V
Orbital
minimization
by Galli, Parrinello,
Ordejon, Tsuchida
Hoshi
Mostofi
Density matrix
by Li and Daw
Krylov subspace
Divide-conquer
Recursion
Fermi operator
WF+V WF+P
22. オ`ダ`N?DFTコ`ドの_k彜r
Conquest (DM) Bowler(ロンドン), Gillan(ロンドン),
m鍋(麗可C), 寄勸(麗可C)
Siesta (OM) Ordejon et al.(スペイン)
ONETEP (DM) Hayne et al.(イギリス)
OpenMX (Krylov) 硫鍋 et al. (|寄)
FEMTECK (OM) 輿弥(bt冩)
AkaiKKR (screened KKR) 橿小 et al.(|寄)
25. 何蛍腎g隈に児づくオ`ダ`N隈
? LanczosQに児づく圭隈
? Two-sided block LanczosQに児づく圭隈
? ArnoldiQに児づく圭隈
? 蕉壓徭隼に児づく蛍護y嵶隈
R. Haydock, V. Heine, and M. J. Kelly, J. Phys. C 5, 2845 (1972);
R. Haydock, Solid State Phys. 35, 216 (1980).
T. Ozaki, Phys. Rev. B 59, 16061 (1999); T. Ozaki, M. Aoki, and
D. G. Pettifor, ibid. 61, 7972 (2000).
T. Ozaki and K. Terakura, Phys. Rev. B 64, 195126 (2001).
T. Ozaki, Phys. Rev. B 64, 195110 (2001).
T. Ozaki, Phys. Rev. B 74, 245101 (2006).
クリロフ何蛍腎g隈
蕉壓徭隼祇に児づく何蛍腎g隈
T. Ozaki, M. Fukuda, and G. Jiang, Phys. Rev. B 98, 245137 (2018).
48. 匯肝圷Y栽モデルの盾裂 #5
Z a
b
?
?
?
01 00
1
( ) ( )
2 2
L L
G Z G Z
b
?
? ?
2
02 00( ) 1 ( )
2 2
L L
G Z G Z
b
? ?? ?
? ? ?? ?
? ?
u晒塀から掲叔はGL
00を喘いて燕Fできる。
GL
00にしてγ-1 =0の巓りでテイラ`婢_すると肝塀が誼られる。
00 3 5 7 9
1 2 6 20 70
( ) 1
2
L
G Z
b ? ? ? ?
? ?
? ? ? ? ? ?? ?
? ?
テイラ`婢_したGL
00をGL
0n に旗秘し、麼勣を函ると、肝の匯違塀が誼られる。
0 1
2
( )L
n n
G Z
b? ?
? γ-1 <1の栽、すなわち 1
b
Z a
?
?
の栽にGL
0nはn->±でゼロに崩する。
49. 匯肝圷Y栽モデルの盾裂 #6
畜業佩双n0iは耕嗤ベクトルψを喘いて肝塀で嚥えられることに廣吭する。
0 0 ( ) ( )nn dE n E f? ? ? ?
?
? ? ? ? ?? ???
Greenv方を喘いてきQえれば、肝塀が誼られる。
0 0
1
Im ( 0 ) ( )n nn dEG E i f ??
?
?
? ? ??
コ`シ`の協尖を喘いてe蛍U揃を筝
フェルミ蛍下v方が防圻Oを隔つ
ことに廣吭すれば、藻方協尖より
肝塀が誼られる。
(0)
0
4
Im
p
n p
p
zi
n M G i R?
? ?
? ?? ?
? ? ? ?? ?? ?
? ?? ?
?
50. ~F悶と署奉におけるグリ`ンv方のu除蒙來(1肝圷モデル)
4
w
r ?
0 1
1
n n
G
y ?
?
/ 4
z
y
w
??
?
w w w
4
w
r ?
4
w
r ?
μ μ
橿い劼陵箸任, グリ`ンv方の掲叔は肝塀のように尅る玲う。
Z=μにおいて、グリ`ンv方の掲叔は肝塀のように尅る玲う。
}殆峠中貧
での防圻O
Re Re
ImIm
署奉 ~F悶
? ?0(2 1)
2 2
1
m
mG
w
? ? ? 0(2 ) 00( 1) 2m
mG G? ?
ここでyはフェル
ミエネルギ`μと
バンド嫌wから肝
塀で協xされる。
}殆峠中貧
での防圻O
橿い劼猟箸任魯哀蟋`ンv方はg腎gにおいて蕉壓していない。
★ L鉦x珸vをもたらす。
51. クリロフ何蛍腎g隈の及匯圻尖麻への
Hc Sc? ? ???
匯違の蕉壓児久を喘いてKS祇を婢_し、KS圭殻塀を盾く栽には肝
の匯違晒耕嗤}に「彭する。Y栽モデルとなり、嶷なりe蛍
を苧幣議に深]する駅勣がある。
? Krylov何蛍腎g隈に児づく圭隈
T. Ozaki and K. Terakura, Phys. Rev. B 64, 195126 (2001).
T. Ozaki, Phys. Rev. B 64, 195110 (2001).
T. Ozaki, Phys. Rev. B 74, 245101 (2006).
云vxでは肝の圭隈をB初する。