Target scope of Conference
Deep learning plays a central role in recent developments in research in artiﬁcial intelligence (AI).
Various ideas based on physics are found in the research of deep learning, and consequently, deeplearning and physics are related intimately. This international conference is dedicated to (1)applications of deep learning to physics, (2) discovering similarities among deep learning andphysics, and (3) leading to new paradigm in physics motivated by deep learning. Researchers in
related ﬁelds are welcome to attending discussions at the conference.

Organizers
Koji Hashimoto (Osaka U), Masatoshi Imada (Toyota RIKEN / Waseda U), Kouji Kashiwa (Fukuoka
Institute of Technology), Yuki Nagai (JAEA), Masayuki Ohzeki (Tohoku U), Enrico Rinaldi (Riken &Arithmer Inc.), Akinori Tanaka (RIKEN AIP), Akio Tomiya (Riken BNL)

Date and Place
31 Oct - 2 Nov 2019
Panasonic Auditorium, Yukawa Hall, Yukawa institute for theoretical physics, Kyoto university

Schedule
Schedule
First day: Oct 31(Thu)

Second day: Nov 1 (Fri)Third day: Nov 2 (Sat)9:30-10:20 H.Y. Hu

9:30-10:20 F. Ruhle

10:20-11:10 G. Kanwar

10:20-11:10 J. Liu
BreakLunch break
11:40-12:30 Y. Yamaji"Physics, AI, Company" session
13:10 Opening Welcome

13:00-13:35 R.K. Seong

13:20-14:10 J. Halverson

13:35-14:10 Y. Miyake

14:10-15:00 S. Krippendorf

14:10-14:45 M. Ohzeki

14:00-14:50 Z.Y. Meng

15:00-15:50 G. Yang

14:45-15:00 Discussion

14:50-15:40 H. Yoshino

Break
16:20-17:10 L. Wang
17:10-18:00 S. Amari

Break
15:30-18:00 Poster session

Lunch break

Break
16:10-17:00 G. Shiu
17:00 ClosingAbstracts
First day: Oct 31 (Thu)
1. 13:20-14:10 James Halverson (Northeastern University)
Generative Models and Statistical Predictions in String Theory
String theory is a theory of quantum gravity and is the leading candidate for unifying observed
particle physics and cosmology. If it is correct, its many solutions means that "fundamental
physics" is an intrinsically complex system with many metastable states, motivating the use oftechniques from deep learning and raising questions regarding the dynamics of complex
systems. In this talk I will review aspects of the string landscape, how computationally complex
problems arise in it, and how generative models could be used to overcome some of thediﬃculties. Speciﬁcally, a variety of generative adversarial networks will be used to approximate
Kahler metric data on Kahler moduli space, which are relevant for axion physics in cosmology,
including interactions of axion-like-particles with the photon. From another perspective, theutilized data techniques provide methods for learning random matrix approximations to matrix
ensembles, in this case those that arise in string eﬀective Lagrangians.
2. 14:10-15:00 Sven Krippendorf (Arnold Sommerfeld Center)
Dualities in and from Machine Learning
Dualities play a key role in our understanding of dynamical systems appearing in eﬀective ﬁeld
theories from string theory and in general in fundamental physics. Dualities in ﬁeld theory andstring theory are a tool to obtain models describing data at a precision beyond a level standard
eﬀective ﬁeld theory techniques allow. At this stage these techniques are not used explicitly inmachine learning.
Based on examples, we identify data questions which are easily answerable in one duality frame
but not in the other. Simple neural networks confronted with data in this favoured duality frame
can answer data questions, in the other frame they cannot identify the correct answer.
I then discuss the question whether a deeper neural network can learn this favourable dual data
representation? I present two strategies, how a dual representation can be learned without
knowing about its existence.
3. 15:00-15:50 Greg Yang (Microsoft Research)
Tensor Programs: The Feynman Diagrams of Deep Learning
Feynman diagrams are used in physics to compute correlation functions in an interacting theory.
In analogy to Feynman diagrams, we introduce tensor programs in this talk as a mechanism tocompute the “correlation functions” of deep neural networks, which can be thought of as having
an “interacting theory” due to their nonlinearities. We start with the motivating example of the
Semicircle Law, which describes the eigenvalue distribution of a large matrix with random

Gaussian entries. We show a classic proof using Feynman diagrams, and then compare itagainst a new proof using Tensor Programs. We discuss similarities and diﬀerences in the“interactivity” handled by Feynman Diagrams and Tensor Programs. Next, we describe several
consequences the Tensor Programs framework in deep learning: 1) wide neural networks of any
architecture are Gaussian processes when randomly initialized; 2) when trained with small
learning rate, wide neural neural networks of any architecture evolve via functional gradientdescent with a kernel, called the “Neural Tangent Kernel”; 3) when trained with large learning
rate, wide neural networks learn an embedding function of inputs into a space of randomvariables.
4. 16:20-17:10 Lei Wang (Chinese Academy of Sciences)
Neural Canonical Transformation
Canonical transformation plays a fundamental role in simplifying and solving classicalHamiltonian systems. We construct ﬂexible and powerful canonical transformations as generativemodels using symplectic neural networks. The model transforms physical variables towards alatent representation with an independent harmonic oscillator Hamiltonian. Correspondingly, thephase space density of the physical system ﬂows towards a factorized Gaussian distribution in
the latent space. Since the canonical transformation preserves the Hamiltonian evolution, themodel captures nonlinear collective modes in the learned latent representation. We present aneﬃcient implementation of symplectic neural coordinate transformations and two ways to train
the model. The variational free energy calculation is based on the analytical form of physicalHamiltonian. While the phase space density estimation only requires samples in the coordinate
space for separable Hamiltonians. We demonstrate appealing features of neural canonicaltransformation using toy problems including two-dimensional ring potential and harmonic chain.
Finally, we apply the approach to real-world problems such as identifying slow collective modes
in alanine dipeptide and conceptual compression of the MNIST dataset.
Ref: Shuo-Hui Li, Chen-Xiao Dong, Linfeng Zhang, Lei Wang, arXiv:1910.00024
5. 17:10-18:00 Shun-Ichi Amari (RIKEN)
Deep Random Neural Field
We begin with a short history of AI and neural networks starting with multilayer perceptrons andtheir learning. We then review recent trends of deep learning with extremely wide layers, showingthat random connected networks play a fundamental role. We thirdly review a short history ofstatistical neurodynamics of random networks. We show how signals are transformed throughrandom deep networks. We also show the Fisher information of random networks. We ﬁnallyconsider randomly connected neural ﬁelds having inﬁnitely many neurons.

Second day: Nov 1 (Fri)
6. 9:30-10:20 Hong-Ye Hu (UC San Diego)
Machine Learning Holography
During this talk, I am going to review the recent progresses made in the ﬁeld of machine learning
holography. Holographic duality is a beautiful relation between certain quantum ﬁeld theories on
a d-dimensional boundary and classical gravity theories on a (d+1)-dimensional bulk. In general,ﬁnding this explicit duality map is challenging.
In the ﬁrst part of this talk, I am going to discuss designing the optimal holographic mapping by
introducing the neural network renormalization group as a universal approach. The mapping isconstructed using a ﬂow-based hierarchical deep generative model. And I am also going todiscuss the uniﬁcation of renormalization group and generative model as the forward andbackward ﬂow of a holographic mapping. We applied this approach to the complex phi-4 theory
in two dimensional Euclidian spacetime in its critical phase, and show that the emergent bulk
geometry matches the three-dimensional hyperbolic geometry in saddle point approximation.
In the second part of this talk, I am going to discuss how to use neural network to solve theholographic QCD problem. Suppose we have the lattice QCD data, can we ﬁnd a gravity theory
that can explain the experimental data? Moreover, can the gravity theory of the bulk predict newexperimental phenomenon of the system? We used both feedforward neural network and newly
developed neuralODE technique to solve this problem. By providing the lattice QCD chiralcondensate data, the machine can learn a metric for the bulk ﬁeld. We observed the coexistenceof conﬁnement and deconﬁnement of the bulk ﬁeld. Moreover, the machine could predict a QQbar potential that matches the experimental result.
7. 10:20-11:10 Gurtej Kanwar (MIT)Flow-based generative models for lattice theories
Monte Carlo sampling is a powerful approach to computing partition functions, thermodynamic
observables, and operator expectation values in quantum and statistical ﬁeld theories. As criticalpoints are approached in parameter space however, the cost of drawing independent samples
typically diverges due to critical slowing down. Typically, solving or reducing this slow-down
must be done on a case-by-case basis by exploiting features of the problem at hand, and inmany cases no good approaches are known. I discuss our recent work on applying generative
machine learning techniques to the sampling problem, including a case study of scalar phi^4
theory in which critical slowing down of sampling is removed by employing trained generativemodels.

"Physics, AI, Company" session
8. 13:00-13:35 Rak-Kyeong Seong (Samsung SDS)
An Overview of AI at Samsung SDS
At a time when advances in AI are shaping the world around us, Samsung is making key
contributions in the endeavour of improving lives and businesses. The talk will give a broad
overview of what recent contributions Samsung has made with the help of AI and what we can
expect in the future.
9. 13:35-14:10 Youichiro Miyake (Square Enix)
AI Technologies in Digital Games
There are two ﬂows in AI technologies in 60 years such as Symbolism and Connectionism.
Digital Game AI uses both two technologies. In the lecture, many cases of AI in digital game titles
including FINAL FANTASY XV (SQUARE ENIX, 2016) are introduced. One of purposes of digital
game is to make an autonomous AI such as to recognize a game world, make a decision, andgenerate a body motion by itself. Understanding an environment around itself for AI is similar tophysics for a human to understand the universe. And it includes also a philosophical problem
such as "What is a world? ","What is a self ?" and "What is a life" ? Making an interactive AI in
real-time with body is a science, engineering. and.philosophy. In the lecture, a whole ﬁgure ofdigital game AI is showed.
10. 14:10-14:45 Masayuki Ohzeki (Tohoku Univ.)
Quantum annealing and its application to deep learning- new direction of quantum annealing Quantum annealing is a generic solver of combinatorial optimization problem and is implemented
by a hardware known as the D-Wave quantum annealer.
On the other hand, the neural network, which is a big success in developing the artiﬁcial
intelligence and data science, is also attained via solving optimization problem.
In this talk, by taking the quantum annealer as an optimizer, we introduce several directions of its
application.
One of the main topic would be an application of the quantum annealer to the deep neuralnetwork although the standard one only deal with the binary variables.

Third day: Nov 2 (Sat)
11. 9:30-10:20 Fabian Ruhle (CERN)
Machine learning for Calabi-Yau metrics
I will discuss how neural networks can be used to study metrics on Calabi-Yau manifolds. These
manifolds feature prominently in string theory, and their metrics determine physical properties ofstring theory models.
12. 10:20-11:10 Junwei Liu (Hong Kong University of Science and Technology)
Self-learning Monte Carlo method and all optical neural network
Self-learning Monte Carlo (SLMC) method is a general-purpose numerical method to simulate
many-body systems. SLMC can eﬃciently cure the critical slowing down in both bosonic andfermionic systems. Moreover, for fermionic systems, SLMC can generally reduce thecomputational complexity and speed up simulations even away from the critical points. For
example, SLMC is more than 1000 times faster than the conventional method for the double
exchange model in 888 cubic lattice. In addition, SLMC also provides a general framework tonaturally integrate the advanced machine learning techniques into Monte Carlo. In this talk, I will
give an introduction about the background, basic idea and the design principle of SLMC. Later, I
will explicitly show how to use SLMC and its great accelerations in classic systems, free fermionscoupled with classical spins systems, and interacting fermion systems. At the end, I will talk ourrecent developed all optical neural networks, which can realize the intrinsic parallel calculations
at the speed of light and are expected outperform the electronic neural networks with large
system size.
References:[1] Self-Learning Monte Carlo Method, PRB 95, 041101(R) (2017)[2] Accelerated Monte Carlo simulations with restricted Boltzmann machines, PRB 95, 035105(2017)[3] Self-Learning Monte Carlo Method in Fermion Systems, PRB 95, 241104(R) (2017)[4] Self-learning Monte Carlo with Deep Neural Networks, PRB 97, 205140 (2018)[5] All optical neural network with nonlinear activation functions, Optica 6, 1132-1137 (2019)13. 11:40:12:30 Youhei Yamaji (Department of Applied Physics, the University of Tokyo)
Origin of High-Temperature Superconductivity Revealed by Boltzmann Machine Learning
Recently machine-learning approaches are widely utilized as tools to analyze accumulated data
across various research domains. Machine learning also has innovated ways of exposing
physical observables, which is invisible in direct scientiﬁc measurements. Solving theunderdetermined inverse problem is a crucial step towards the machine learning innovation of
the scientiﬁc measurements, which enables us to extract fundamental physical quantities
entangled with each other in existing experimental data. A combination of properly chosen priorknowledge and machine learning holds the key to ﬁnd a solution to the inverse problem.

An enigmatic inverse problem in condensed matter physics is found in open issues of hightemperature superconductors, which has long been a major challenge in physics. How electrons
are mutually interacting is the key to identify the origin of the superconductivity. We are, however,able to observe the motion of electrons only after projection onto experimentally accessible
degrees of freedom.
In this study, we utilized the Boltzmann machine, combined with physically sound priorknowledge, to solve the inverse problem and extract physical quantities hidden in experimental
data. The method is applied to the angle-resolved photoemission spectroscopy spectra [1] ofcopper-oxide high-temperature superconductors. From a spectrum [2], we extracted metallic(normal) and superconducting (anomalous) components of the self-energy separately, in which
mutual interactions among electrons are encoded. We found prominent peak structures
emerging both in the normal and anomalous self-energies, respectively, which are canceled in
the total self-energy and hence invisible in experiments, as the origin of high-temperature
superconductivity [3]. The present achievement may open avenues for innovative machinelearning spectroscopy.
[1] A. Damascelli, Z. Hussain, and Z.-X. Shen, Rev. Mod. Phys. 75,
473-541 (2003).
[2] T. Kondo, et al., Nature 457, 296-300 (2009).
[3] Y. Yamaji, T. Yoshida, A. Fujimori and M. Imada, arXiv:1903.08060.
14. 14:00-14:50 Zi Yang Meng (Chinese Academy of Sciences)
What we talk about When we talk about learning in many-body physics
In this talk, I will review recent developments in a priori and a posteriori learning strategies indealing with classical and quantum many-body systems. Thanks to these philosophical andnumerical advancements, new paradigms in condensed matter and high energy physics such as
non-Fermi-liquid, quantum criticality and emergent gauge-ﬁeld coupled with matter ﬁeld can beaccessed with large-scale numerical simulations. These results in turn inspire further analyticaland numerical progress towards the complete form of few important many-body physics
problems.
Reference:[1] TOPICAL REVIEW, J. Phys.: Condens. Matter 31, 463001 (2019)https://dx.doi.org/10.1088/1361-648X/ab3295[2] PNAS August 20, 2019 116 (34) 16760-16767[3] Phys. Rev. X 9 , 021022 (2019)15. 14:50-15:40 Hajime Yoshino (Osaka Univ.)
From complex to simple : hierarchical free-energy landscape renormalized in deep neral
networks
We develop a statistical mechanical approach based on the replica method to study the phasespace structure of deep neural networks. Speciﬁcally we analyze the conﬁguration space of thesynaptic weights in a simple feed-forward perceptron network within a Gaussian approximation.

By increasing the strength of constraints, i. e. increasing the number of imposed random
input/output patterns, successive glass transitions take place layer-by-layer starting next to the
input/output boundaries going deeper into the bulk. For deep enough network the central part of
the network remains in the liquid phase. The successive glass transitions bring about ahierarchical free-energy landscape which evolves in space: it is most complex close to theboundaries but becomes renormalized into progressively simpler one in deeper layers. Similarly,
in an idealized teacher-student setting, the student machine reproduces the teacher machinearound the input/output boundaries but leaves behind the central part as very diﬀerent from theteacher. These observations provide clues to understand why deep neural networks operateeﬃciently and also insights for related problems in glass physics and biology. Finally we present
some results of a set of simple numerical simulations to examine the theoretical predictions.
16. 16:10-17:00 Gary Shiu (Univ. Wisconsin)
Decoding the Shape of the String Landscape with Data Science
What gave string theory its appeal is that it provides a consistent framework for unifying
quantum mechanics with gravity. However, the richness of string theory also suggests anenormous number of solutions, often known as the string landscape. In some corner of string
theory, the number of solutions was estimated to be 10500 and recent work suggested that there
may even be more (~10272,000 ). These solutions constitute a huge set of "theoretical data”,
possibly far beyond what commonly referred to as "big data” in other data intense ﬁelds. Whenthe dimension of the data space is huge, we cannot visualize the "shape” of data and decodethe underlying physics. Searching for solutions with desired properties would also be extremely
challenging. In this talk, I’ll discuss how topological data analysis and genetic algorithms canhelp uncover the structure of the string landscape.