The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
The interplay between data-driven and theory-driven methods for chemical scie...Ichigaku Takigawa
The 1st International Symposium on Human InformatiX
X-Dimensional Human Informatics and Biology
ATR, Kyoto, February 27-28, 2020
https://human-informatix.atr.jp
Hokkaido University (HU) - Seoul National University (SNU) Joint Symposium
2018 International Workshop on
New Frontiers in Convergence Science and Technology
How to use data to design and optimize reaction? A quick introduction to work...Ichigaku Takigawa
(Journal Club) ICReDD Seminar, Apr 27 2020
Institute for Chemical Reaction Design and Discovery (ICReDD)
Hokkaido University
Sapporo, JAPAN
https://www.icredd.hokudai.ac.jp
Friday, October 15th, 2021, Sapporo, Hokkaido, Japan.
Hokkaido University ICReDD - Faculty of Medicine Joint Symposium
https://www.icredd.hokudai.ac.jp/event/5993
ICReDD (Institute for Chemical Reaction Design and Discovery)
https://www.icredd.hokudai.ac.jp
Machine Learning for Chemistry: Representing and InterveningIchigaku Takigawa
Joint Symposium of Engineering & Information Science & WPI-ICReDD in Hokkaido University
Apr. 26 (Mon), 2021
https://www.icredd.hokudai.ac.jp/event/5430
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Mobile devices are now mainstream handheld computers providing access to computational power and storage that a decade ago was available only on desktop computers. In terms of chemistry informatics the majority of capabilities that were previously found only on desktop computers is fast migrating to mobile devices making use of the combination of powerful visualization capabilities, fast cloud-based calculations, websites optimized for the mobile platforms, and delivering “apps”. This presentation will provide an overview of how access to chemistry continues to be made increasingly mobile and specifically on how the Royal Society of Chemistry is contributing to this computing environment.
Listing of Intellectual work of patanjali kashyap , mainly contains , name, details and reference of papers , presentations , patents , public speaking
In this deck from the 2014 HPC User Forum in Seattle, Jack Collins from the National Cancer Institute presents: Genomes to Structures to Function: The Role of HPC.
Watch the video presentation: http://wp.me/p3RLHQ-d28
Future Directions in Chemical Engineering and BioengineeringIlya Klabukov
"Future Directions in Chemical Engineering and Bioengineering"
January 16-18, 2013
Austin, Texas
Chair: John G. Ekerdt, The University of Texas at Austin
Sponsored by Department of Defense,
Office of the Assistant Secretary of Defense for Research and Engineering
Chemical and biological engineers use math, physics, chemistry, and biology to develop chemical transformations and processes, creating useful products and materials that improve society. In recent years, the boundaries between chemical engineering and bioengineering have blurred as biology has become molecular science, more seamlessly connecting with the historic focus of chemical engineering on molecular interactions and transformations.
This disappearing boundary creates new opportunities for the next generation of engineered systems – hybrid systems that integrate the specificity of biology with chemical and material systems to enable novel applications in catalysis, biomaterials, electronic materials, and energy conversion materials.
Basic research for the U.S. Department of Defense covers a wide range of topics such as metamaterials and plasmonics, quantum information science, cognitive neuroscience, understanding human behavior, synthetic biology, and nanoscience and nanotechnology. Future Directions workshops such as this one identify opportunities
for continuing and future DOD investment. The intent is to create conditions for discovery and transformation, maximize the discovery potential, bring balance and coherence, and foster connections. Basic research stretches the limits of today’s technologies and discovers new phenomena and know-how that ultimately lead to future technologies and enable military and societal progress.
Machine Learning for Molecules: Lessons and Challenges of Data-Centric ChemistryIchigaku Takigawa
Perspectives on Artificial Intelligence and Machine Learning in Materials Science
February 4, 2022. – February 6, 2022.
https://joint.imi.kyushu-u.ac.jp/post-2698/
Over the past decade, unprecedented progress in the development of neural networks influenced dozens of different industries, including weed recognition in the agro-industrial sector. The use of neural networks in agro-industrial activity in the task of recognizing cultivated crops is a new direction. The absence of any standards significantly complicates the understanding of the real situation of the use of the neural network in the agricultural sector. The manuscript presents the complete analysis of researches over the past 10 years on the use of neural networks for the classification and tracking of weeds due to neural networks. In particular, the analysis of the results of using various neural network algorithms for the task of classification and tracking was presented. As a result, we presented the recommendation for the use of neural networks in the tasks of recognizing a cultivated object and weeds. Using this standard can significantly improve the quality of research on this topic and simplify the analysis and understanding of any paper.
Mobile devices are now mainstream handheld computers providing access to computational power and storage that a decade ago was available only on desktop computers. In terms of chemistry informatics the majority of capabilities that were previously found only on desktop computers is fast migrating to mobile devices making use of the combination of powerful visualization capabilities, fast cloud-based calculations, websites optimized for the mobile platforms, and delivering “apps”. This presentation will provide an overview of how access to chemistry continues to be made increasingly mobile and specifically on how the Royal Society of Chemistry is contributing to this computing environment.
Listing of Intellectual work of patanjali kashyap , mainly contains , name, details and reference of papers , presentations , patents , public speaking
In this deck from the 2014 HPC User Forum in Seattle, Jack Collins from the National Cancer Institute presents: Genomes to Structures to Function: The Role of HPC.
Watch the video presentation: http://wp.me/p3RLHQ-d28
Future Directions in Chemical Engineering and BioengineeringIlya Klabukov
"Future Directions in Chemical Engineering and Bioengineering"
January 16-18, 2013
Austin, Texas
Chair: John G. Ekerdt, The University of Texas at Austin
Sponsored by Department of Defense,
Office of the Assistant Secretary of Defense for Research and Engineering
Chemical and biological engineers use math, physics, chemistry, and biology to develop chemical transformations and processes, creating useful products and materials that improve society. In recent years, the boundaries between chemical engineering and bioengineering have blurred as biology has become molecular science, more seamlessly connecting with the historic focus of chemical engineering on molecular interactions and transformations.
This disappearing boundary creates new opportunities for the next generation of engineered systems – hybrid systems that integrate the specificity of biology with chemical and material systems to enable novel applications in catalysis, biomaterials, electronic materials, and energy conversion materials.
Basic research for the U.S. Department of Defense covers a wide range of topics such as metamaterials and plasmonics, quantum information science, cognitive neuroscience, understanding human behavior, synthetic biology, and nanoscience and nanotechnology. Future Directions workshops such as this one identify opportunities
for continuing and future DOD investment. The intent is to create conditions for discovery and transformation, maximize the discovery potential, bring balance and coherence, and foster connections. Basic research stretches the limits of today’s technologies and discovers new phenomena and know-how that ultimately lead to future technologies and enable military and societal progress.
Analysis of Existing Models in Relation to the Problems of Mass Exchange betw...YogeshIJTSRD
The main recommendations of this article mainly analyzing the rate of harmful elements the period of exploitation of the automobile implements and its services to develop activity of automobile implements of the exploitation period. Shavkat Giyazov "Analysis of Existing Models in Relation to the Problems of Mass Exchange between Autotransport Complex and the Environment" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-3 , April 2021, URL: https://www.ijtsrd.com/papers/ijtsrd38681.pdf Paper URL: https://www.ijtsrd.com/engineering/automotive-engineering/38681/analysis-of-existing-models-in-relation-to-the-problems-of-mass-exchange-between-autotransport-complex-and-the-environment/shavkat-giyazov
This review considers the application of CASE systems to a series of examples in which the original structures were later revised. We demonstrate how the chemical structure could be correctly elucidated if 2D NMR data were available and the expert system Structure Elucidator was employed. We will also demonstrate that if only 1D NMR spectra from the published articles were used then simply the empirical calculation of 13C chemical shifts for the hypothetical structures frequently enables a researcher to realize that the structural hypothesis is likely incorrect. We also analyze a number of erroneous structural suggestions made by highly qualified and skilled chemists. The investigation of these mistakes is very instructive and has facilitated a deeper understanding of the complicated logical-combinatorial process for deducing chemical structures.
NG2S: A Study of Pro-Environmental Tipping Point via ABMsKan Yuenyong
A study of tipping point: much less is known about the most efficient ways to reach such transitions or how self-reinforcing systemic transformations might be instigated through policy. We employ an agent-based model to study the emergence of social tipping points through various feedback loops that have been previously identified to constitute an ecological approach to human behavior. Our model suggests that even a linear introduction of pro-environmental affordances (action opportunities) to a social system can have non-linear positive effects on the emergence of collective pro-environmental behavior patterns.
AN OPTIMIZATION ALGORITHM BASED ON BACTERIA BEHAVIORijaia
Paradigms based on competition have shown to be useful for solving difficult problems. In this paper we present a new approach for solving hard problems using a collaborative philosophy. A collaborative philosophy can produce paradigms as interesting as the ones found in algorithms based on a competitive philosophy. Furthermore, we show that the performance - in problems associated to explosive combinatorial - is comparable to the performance obtained using a classic evolutive approach.
Nature-inspired Solutions for Engineering: A Transformative Methodology for I...KTN
Nature- Inspired Engineering (NIE) is the application of fundamental scientific mechanisms, underpinning desirable properties observed in nature (e.g., resilience, scalability, efficiency), to inform the design of advanced technological solutions. As illustrated by the many applications, from energy technology, catalysis and reactor engineering, to functional materials for the built environment, electronic or optical devices, biomedical and healthcare engineering, NIE has the opportunity to inform transformative solutions to tackle some of our most pressing challenges, as well as to be a pathway to innovation.
The webcast recording is now available. Click here to watch it: https://www.youtube.com/watch?v=gPyTb_-qhgo
Find out more about the Nature Inspired Solutions special interest group at https://ktn-uk.co.uk/interests/nature-inspired-solutions
Join the Nature Inspired Solutions LinkedIn group at https://www.linkedin.com/groups/13701855/
NanoAgents: Molecular Docking Using Multi-Agent TechnologyCSCJournals
Traditional computer-based simulators for manual molecular docking for rational drug discovery have been very time consuming. In this research, a multi agent-based solution, named as NanoAgent, has been developed to automate the drug discovery process with little human intervention. In this solution, ligands and proteins are implemented as agents who pose the knowledge of permitted connections with other agents to form new molecules. The system also includes several other agents for surface determination, cavity finding and energy calculation. These agents autonomously activate and communicate with each other to come up with a most probable structure over the ligands and proteins, which are participating in deliberation. Domain ontology is maintained to store the common knowledge of molecular bindings, whereas specific rules pertaining to the behaviour of ligands and proteins are stored in their personal ontologies. Existing, Protein Data Bank (PDB) has also been used to calculate the space required by ligand to bond with the receptor. The drug discovery process of NanoAgent has exemplified exciting features of multi agent technology, including communication, coordination, negotiation, butterfly effect, self-organizing and emergent behaviour. Since agents consume fewer computing resources, NanoAgent has recorded optimal performance during the drug discovery process. NanoAgent has been tested for the discovery of the known drugs for the known protein targets. It has 80% accuracy by considering the prediction of the correct actual existence of the docked molecules using energy calculations. By comparing the time taken for the manual docking process with the time taken for the molecular docking by NanoAgent, there has been 95% efficiency.
Exploring Practices in Machine Learning and Machine Discovery for Heterogeneo...Ichigaku Takigawa
Video https://youtu.be/P4QogT8bdqY
ACS Spring 2023 Symposium on AI-Accelerated Scientific Workflow
https://acs.digitellinc.com/acs/sessions/526630/view
ACS SPRING 2023 ———— Crossroads of Chemistry
Indianapolis, IN & Hybrid, March 26-30
https://www.acs.org/meetings/acs-meetings/spring-2023.html
Slide PDF
https://itakigawa.page.link/acs2023spring
Our Paper
Accelerated discovery of multi-elemental reverse water-gas shift catalysts using extrapolative machine learning approach (2022, ChemRxiv)
https://doi.org/10.26434/chemrxiv-2022-695rj
Ichi Takigawa
https://itakigawa.github.io/
Machine Learning for Molecular Graph Representations and GeometriesIchigaku Takigawa
Dec 1, 2021, Pacifico Yokohama, Japan.
Symposium 1AS-17 "Data science and machine learning: Tackling the Noise and Heterogeneity of the Real World"
The 44th Annual Meetingn of the Molecular Biology Society of Japan
https://www2.aeplan.co.jp/mbsj2021/english/designation/index.html
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Cancer cell metabolism: special Reference to Lactate PathwayAADYARAJPANDEY1
Normal Cell Metabolism:
Cellular respiration describes the series of steps that cells use to break down sugar and other chemicals to get the energy we need to function.
Energy is stored in the bonds of glucose and when glucose is broken down, much of that energy is released.
Cell utilize energy in the form of ATP.
The first step of respiration is called glycolysis. In a series of steps, glycolysis breaks glucose into two smaller molecules - a chemical called pyruvate. A small amount of ATP is formed during this process.
Most healthy cells continue the breakdown in a second process, called the Kreb's cycle. The Kreb's cycle allows cells to “burn” the pyruvates made in glycolysis to get more ATP.
The last step in the breakdown of glucose is called oxidative phosphorylation (Ox-Phos).
It takes place in specialized cell structures called mitochondria. This process produces a large amount of ATP. Importantly, cells need oxygen to complete oxidative phosphorylation.
If a cell completes only glycolysis, only 2 molecules of ATP are made per glucose. However, if the cell completes the entire respiration process (glycolysis - Kreb's - oxidative phosphorylation), about 36 molecules of ATP are created, giving it much more energy to use.
IN CANCER CELL:
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
Unlike healthy cells that "burn" the entire molecule of sugar to capture a large amount of energy as ATP, cancer cells are wasteful.
Cancer cells only partially break down sugar molecules. They overuse the first step of respiration, glycolysis. They frequently do not complete the second step, oxidative phosphorylation.
This results in only 2 molecules of ATP per each glucose molecule instead of the 36 or so ATPs healthy cells gain. As a result, cancer cells need to use a lot more sugar molecules to get enough energy to survive.
introduction to WARBERG PHENOMENA:
WARBURG EFFECT Usually, cancer cells are highly glycolytic (glucose addiction) and take up more glucose than do normal cells from outside.
Otto Heinrich Warburg (; 8 October 1883 – 1 August 1970) In 1931 was awarded the Nobel Prize in Physiology for his "discovery of the nature and mode of action of the respiratory enzyme.
WARNBURG EFFECT : cancer cells under aerobic (well-oxygenated) conditions to metabolize glucose to lactate (aerobic glycolysis) is known as the Warburg effect. Warburg made the observation that tumor slices consume glucose and secrete lactate at a higher rate than normal tissues.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
6. !4
All models are wrong, but some are useful
(George Box)
David Hand
Theory-driven models can be wrong
But data-driven models cannot be wrong
7. !4
All models are wrong, but some are useful
(George Box)
David Hand
Theory-driven models can be wrong
But data-driven models cannot be wrong
or right
8. !4
All models are wrong, but some are useful
(George Box)
David Hand
Theory-driven models can be wrong
But data-driven models cannot be wrong
or right
Data-driven are not trying to describe an underlying reality.
so they could be poor or useless, but not wrong
But are merely intended to be useful
35. !31
• the number of immediate neighbors who are
“heavy” (non-hydrogen) atoms
• the valence minus the number of hydrogens
• the atomic number
• the atomic mass
• the atomic charge
• the number of attached hydrogens
• whether the atom is contained in at least one ring
• hydrogen-bond acceptor or not?
• hydrogen-bond donor or not?
• negatively ionizable or not?
• positively ionizable or not?
• aromatic or not?
• halogen or not?
Rogers+, Extended-Connectivity Fingerprints. J. Chem. Inf. Model., 2010, 50 (5), pp 742–754
Faber+, Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error. J. Chem. Theory Comput., 2017, 13 (11), pp 5255–5264
38. !34
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hG<latexit sha1_base64="peoO2C4CZtQgbVCeJaYQ32lHrFU=">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</latexit><latexit sha1_base64="peoO2C4CZtQgbVCeJaYQ32lHrFU=">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</latexit><latexit sha1_base64="peoO2C4CZtQgbVCeJaYQ32lHrFU=">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</latexit><latexit sha1_base64="peoO2C4CZtQgbVCeJaYQ32lHrFU=">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</latexit>
v
h(1)
v =
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h(t 1)
v
a(t)
v
h(t)
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h
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v
xv
tanh
X
v2V
(yv) tanh(zv)
!hG =<latexit sha1_base64="ARMGXVwsPnaLefmPlcb2NUBMhVE=">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</latexit><latexit sha1_base64="ARMGXVwsPnaLefmPlcb2NUBMhVE=">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</latexit><latexit sha1_base64="ARMGXVwsPnaLefmPlcb2NUBMhVE=">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</latexit><latexit sha1_base64="ARMGXVwsPnaLefmPlcb2NUBMhVE=">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</latexit>
40. !36
↑ “part position paper, part review, and part unification” (DeepMind)
11 Jun 2018 arXiv:1806.01261 [cs.LG]
41. !37
We argue that combinatorial generalization must be a top priority for AI to achieve human-like
abilities, and that structured representations and computations are key to realizing this objective.
•Combinatorial optimization
(Bello+ 2016; Nowak+ 2017; Dai+ 2017)
•Boolean satisfiability
(Selsam+ 2018)
•Program representation and verification
(Allamanis+ 2018; Li+ 2016)
•Modeling cellular automata and Turing
machines (Johnson, 2017)
•Performing inference in graphical
models (Yoon+ 2018)