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Trending Research (Top 25)

The trending research page have established filters to look at the research of the last month ordered by standard popularity measures like downloads and social media attention.
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2023/04/18 06:36
https://github.com/tensorlakeai/indexify
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A realtime and indexing and structured extraction engine for Unstructured Data to build Generative AI Applications
2024/05/25 16:54
active
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2020/10/27 22:26
https://github.com/Raphire/Win11Debloat
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A simple, easy to use powershell script to remove bloatware apps from windows, disable telemetry, bing in windows search aswell as perform various other changes to declutter and improve your windows experience. This script works for both windows 10 and windows 11.
2024/05/25 16:03
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2023/04/25 00:46
https://github.com/cgohlke/talib-build
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147
Build TA-Lib wheels for Windows
2024/05/25 07:35
active
2
2024/04/25 21:24
https://github.com/rashadphz/farfalle
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1264
AI search engine - self-host with local or cloud LLMs
2024/05/25 16:16
active
0
2024/02/24 22:57
https://github.com/dobriban/stat-ml-edu
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Resources for education in statistics and machine learning: from advanced undergraduate to research level
2024/05/24 15:30
active
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2020/08/24 05:01
https://github.com/binance/binance-public-data
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Details on how to get Binance public data
2024/05/25 16:36
active
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2021/01/19 08:37
https://github.com/Transpile-AI/ivy
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The Unified AI Framework
2024/05/24 16:14
active
1
2022/09/21 14:43
https://github.com/pymc-labs/CausalPy
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813
A Python package for causal inference in quasi-experimental settings
2024/05/24 00:22
active
3
2024/02/20 23:33
https://github.com/quarylabs/quary
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1917
Open-source BI for engineers
2024/05/24 16:49
active
22
2014/08/30 17:07
https://github.com/triton-lang/triton
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Development repository for the Triton language and compiler
2024/05/24 16:16
active
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2024/05/21 02:41
https://github.com/0xDub/figgie-auto
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39
An algorithmic sandbox for Jane Street's game, "Figgie"
2024/05/24 15:29
active
49
2021/05/30 14:47
https://github.com/alan2207/bulletproof-react
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A simple, scalable, and powerful architecture for building production ready React applications.
2024/05/24 16:57
active
1
2024/05/07 22:21
https://github.com/microsoft/Phi-3CookBook
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546
This is a Phi-3 book for getting started with Phi-3. Phi-3, a family of open AI models developed by Microsoft. Phi-3 models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks.
2024/05/24 16:44
active
1
2023/05/01 21:54
https://github.com/peremartra/Large-Language-Model-Notebooks-Course
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712
Practical course about Large Language Models.
2024/05/24 16:55
active
19
2022/01/03 07:35
https://github.com/HigherOrderCO/HVM
368
9798
A massively parallel, optimal functional runtime in Rust
2024/05/24 16:50
active
0
2024/05/13 08:56
https://github.com/kongds/MoRA
2
97
2024/05/24 16:56
active
0
2024/01/31 20:12
https://github.com/gauge-sh/tach
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261
A Python tool to enforce a modular, decoupled package architecture. Open source Installable via pip Able to be adopted incrementally - Implemented with no runtime impact Interoperable with your existing systems
2024/05/24 06:32
active
15
2015/10/30 19:19
https://github.com/drivendataorg/cookiecutter-data-science
2367
7662
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.
2024/05/23 15:52
active
102
2011/02/13 18:38
https://github.com/psf/requests
9219
51484
A simple, yet elegant, HTTP library.
2024/05/23 16:44
active
3
2024/05/18 09:34
https://github.com/Codium-ai/cover-agent
95
1853
CodiumAI Cover-Agent: An AI-Powered Tool for Automated Test Generation and Code Coverage Enhancement!
2024/05/23 16:58
active
4
2022/01/08 08:45
https://github.com/katanaml/sparrow
247
2082
Data processing with ML and LLM
2024/05/23 15:41
active
12
2015/06/18 19:39
https://github.com/igorbarinov/awesome-data-engineering
1114
6131
A curated list of data engineering tools for software developers
2024/05/23 16:58
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16
Vladimir Arnold 10 Selected Papers1. "On Some Problems in the Theory of Ordinary Differential Equations" (1958): In this paper, Arnold introduced the concept of topological equivalence of dynamical systems and laid the foundation for the theory of dynamical systems.2. "Small Denominators and Problems of Stability of Motion in Classical and Celestial Mechanics" (1963): This paper addresses the phenomenon of chaos in dynamical systems and introduces the concept of small divisors in perturbation theory.3. "Proof of A Theorem of A. N. Kolmogorov on the Invariance of Quasi-Periodic Motions under Small Perturbations of the Hamiltonian" (1963): Arnold's proof of Kolmogorov's theorem on the persistence of quasi-periodic motions in Hamiltonian systems under small perturbations is a landmark result in the theory of dynamical systems.4. "Normal Forms of Functions near Degenerate Critical Points, the Weyl Groups A_k, D_k, E_k, and Lagrangian Singularities" (1978): This paper introduces the theory of normal forms for functions near critical points and provides a classification of singularities using the theory of Lie groups.5. "Geometrical Methods in the Theory of Ordinary Differential Equations" (1983): In this paper, Arnold presents geometric methods for studying the qualitative behavior of solutions to ordinary differential equations, including the use of phase space and integral invariants.6. "Topological Classification of Complex Quadratic Polynomials" (1990): Arnold introduces a topological classification of complex quadratic polynomials based on their critical points, laying the groundwork for the study of complex dynamics.7. "Mathematical Methods of Classical Mechanics" (1989): While not a single paper, Arnold's textbook on classical mechanics has had a profound influence on the study of dynamical systems and serves as a comprehensive reference for researchers and students
2024/05/05
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
165
https://www.linkedin.com/feed/update/urn:li:activity:7192639186066096129
18
35
James Harris Simons ( 1938 – May 10, 2024) was an American hedge fund manager, mathematician, and philanthropist. He was the founder of Renaissance Technologies, a quantitative hedge fund based in East Setauket, New York. He and his fund are known to be quantitative investors, using mathematical models and algorithms to make investment gains from market inefficiencies. Due to the long-term aggregate investment returns of Renaissance and its Medallion Fund, Simons was described as the "greatest investor on Wall Street", and more specifically "the most successful hedge fund manager of all time".Scientific Contributions1. Chern-Simons Form: Simons's most famous scientific contribution is the Chern-Simons form, developed in collaboration with Shiing-Shen Chern. This mathematical theory, arising from differential geometry, is crucial in the study of three-dimensional manifolds and has profound implications in theoretical physics, particularly in quantum field theory and string theory. The Chern-Simons form is used to construct invariants of 3-manifolds and knots, influencing areas like topology and geometric analysis.2. Mathematical Scholarship and Support: Beyond his personal research contributions, Simons has significantly impacted the scientific community through his philanthropic efforts. The Simons Foundation, which he founded, supports research in mathematics and physical sciences, providing grants and funding for projects, researchers, and institutions. This foundation plays a critical role in advancing scientific understanding and fostering innovation in several theoretical fields.Financial Contributions1. Quantitative Finance Pioneer: As the founder of Renaissance Technologies, Simons is one of the pioneers of quantitative finance. Renaissance Technologies, particularly its Medallion Fund, is known for its statistical and algorithmic strategies, which leverage mat
2024/05/10
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
1279
https://www.linkedin.com/feed/update/urn:li:activity:7194748716707123201
15
7
I'm delighted to share our new article "A Dynamic Regime-Switching Model Using Gated Recurrent Straight-Through Units," where we introduce a novel deep learning approach for regime identification.This is joint work with Nino Antulov-Fantulin and Alvaro C.. Part of this research was conducted while Alvaro was a visiting scholar at NYU Courant, pursuing his Master's thesis.Download:https://lnkd.in/ey5gPYKKAbstract:We introduce a novel approach for regime identification using deep learning, a recurrent neural network architecture termed the gated recurrent straight-through unit (GRSTU). The new model can be implemented using commonly available open-source machine learning libraries, enabling automatic differentiation, and trained with the Adam optimizer. Through comprehensive simulation studies, we illustrate that the GRSTU model surpasses statistical jump models, which have shown state-of-the-art performance in regime identification tasks. Specifically, the GRSTU excels in regime classification, particularly on smaller datasets, while demonstrating comparable performance on larger datasets. Finally, in an out-of-sample application, we employ the GRSTU to identify regime changes in the S&P500 index from January 1, 2003 through January 1, 2024. We find that simple regime-switching strategies outperform the index, in terms of lower volatility, CVaR, and drawdown, while maintaining a Sharpe ratio equivalent to or better than that of the baseline.hashtag#nyu hashtag#nyucourant hashtag#regime hashtag#switching hashtag#detection hashtag#deeplearning hashtag#rnn hashtag#gru hashtag#ste hashtag#statistical hashtag#jumpmodels hashtag#hyperparameter hashtag#tuning hashtag#trading hashtag#hmm hashtag#financialmachinelearning hashtag#highdimensional hashtag#return hashtag#dynamics hashtag#continuous hashtag#jumpmodels NYU Courant Institute of Mathematical Sciences NYU Courant Ins
2024/04/30
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
130
https://www.linkedin.com/feed/update/urn:li:activity:7190715611075723265
13
8
What impact does radioactivity have on scientific research? GenAI seems to be able to generate tricky questions, sometimes even beyond the human cognition level, as shown in this recent post! (See the yellow diamond under the photo). #ai #agi #science #radioactivity #research
2024/05/16
Fidelity Investments | AI Asset Management
13
https://www.linkedin.com/feed/update/urn:li:activity:7196608018468274176
10
7
I've started using Claude 3 for coding assistance (along with GitHub Copilot) instead of GPT-4.I'm not sure if the quality of GPT-4 for coding questions has declined, but regardless, I get much more consistency and accuracy with Claude 3.It costs $20 a month but you get a free trial. Give it a shot if you are having the same issues I am with GPT-4.
2024/05/06
Quant Developers | High Frequency Trading solutions | Founder & CTO / Lead Tech
109
https://www.linkedin.com/feed/update/urn:li:activity:7192975663111319552
10
16
A picture is worth a thousand words (see picture below)Cognitive biases are pervasive even (perhaps even more!) when real money is a stake.How can we continue thinking that micro founding economics on rational agents is a good idea — or even a rough first approximation ?
2024/04/29
Financial Risk and Engineering, NYU School of Engineering | Adjunct Professor
38
https://www.linkedin.com/feed/update/urn:li:activity:7190451500941234176
10
13
I’m confused, but I really like your diagrams.” ;) noted! #quants #iykyk #magic #pentel #quickerclicker #mac #diagrams
2024/05/25
Fidelity Investments | AI Asset Management
21
https://www.linkedin.com/feed/update/urn:li:activity:7199830340348059649
9
20
*** "Riding Wavelets": A New Method to Classify Financial Price Jumps ***Why do stock prices "jump"? And why do they jump sooo often? At the one minute level there is indeed roughly one 4-sigma event per stock every 2 days! As first observed for daily price jumps in a classic paper by Cutler, Poterba and Larry Summers https://lnkd.in/eCgrTtH2 most of these jumps seem be endogenous, i.e. are not related to any significant new piece of information. This is of course in blatant contradiction with the efficient market hypothesis, for which at least large price jumps should reflect new information. But out-of-the-blue events are in line with Shiller's well known observation that prices are much too volatile to be fully explained by "fundamentals". We have argued many times that the origin of such endogenous jumps is the intrinsic *fragility* of financial markets, shared with many other socio-technical systems, like firm networks or train networks (on this point, stay tuned for an upcoming paper in Nature with Debabrata Panja José Morán Frank P. Pijpers Utz Weitzel)In a new paper https://lnkd.in/eVpV6DWj Cecilia Aubrun Rudy Morel Michael Benzaquen and myself have introduced an unsupervised classification framework that leverages a multi-scale wavelet representation of time-series and apply it to stock price jumps. In line with a previous paper with Riccardo Marcaccioli, we recover the fact that the time-asymmetry of volatility is a major feature that separates rare exogenous, news-induced jumps from a majority of endogenously generated jumps. Local mean-reversion and trend are found to be two additional key features, allowing us to identify new classes of jumps. Using our wavelet-based representation, we investigate the reflexive properties of co-jumps, which occur when multiple stocks experience price jumps within the same minute. Perhaps surprisingly, our results suggest
2024/05/12
Financial Risk and Engineering, NYU School of Engineering | Adjunct Professor
210
https://www.linkedin.com/feed/update/urn:li:activity:7195480528681062400
9
4
I am excited to announce the publication of our paper, "Identifying Patterns in Financial Markets: Extending the Statistical Jump Model for Regime Identification," in the Annals of Operations Research. We dedicate this article to Professor William Ziemba, who spent his long career researching and discovering systematic approaches to improve investment performance by strategies and approaches based on conditional “inefficient” pricing behavior. His research covers examples in a wide range of markets and instruments, from sporting and gambling events to traditional financial markets, and even less conventional markets such as wine and Turkish rugs. These patterns are sometimes identified as anomalies (arbitrage), obtaining an edge (such card counting in blackjack) or systematic risk-factors (traditional asset classes). With the rise of micro-level data and new data science methods, the research Bill initiated has continued to grow, marking him as a pioneer explorer. This is joint work with Afşar Onat Aydınhan, Ph. D., John Mulvey and Yizhan (Oliver) Shu at Princeton University. Download: https://lnkd.in/eFQf4Arr #nyu #nyucourant NYU Courant Institute of Mathematical Sciences NYU Courant Institute of Mathematical Sciences M.S. in Mathematics in Finance, NYU Courant New York University Denis Dariotis #regimeswitching #continuous #jumpmodels Erik Lindstrom Federico Cortese Peter Nystrup #hmm Society of Quantitative Analysts (SQA) International Association for Quantitative Finance IAQF Operations Research & Financial Engineering Department, Princeton University Nino Antulov-Fantulin Stefan Klauser Lukas Sieber Keeyan Ravanshid Giovanni Beliossi Richard Lindsey PhD
2024/05/14
Financial Risk and Engineering, NYU School of Engineering | Adjunct Professor
121
https://www.linkedin.com/feed/update/urn:li:activity:7196141971344687105
8
10
Spot on. This is also what we deeply believe in at CFM — but we must be missing something for the recipe to work as successfully
2024/05/18
University of Oxford | Associate Professor
309
https://www.linkedin.com/feed/update/urn:li:activity:7197569971944615936
7
7
Classical machine learning v. LLMs/GenAI...that question sits underneath the surface of a lot of enterprise level decisions right now. My take is that if an enterprise is serious about LLMs/GenAI, it would be prudent to double possibly triple down on your classical machine learning talent/investment/culture as well. Have a nice Sunday. JR
2024/05/13
Truist Securities | Head of Data and Quantamental Research
74
https://www.linkedin.com/feed/update/urn:li:activity:7195461061683073025
7
20
JPMorgan Chase & Co. is pursuing a dual strategy in quantum-safe remediation.  One of the active steps we have taken is to integrate hashtag#QuantumKeyDistribution (hashtag#QKD) with standard security protocols, such as IPsec Tunnel, which are fully compatible with hashtag#PostQuantumCryptography (hashtag#PQC) solutions for defense in depth.We just announced the successful implementation of a novel Quantum-secured Crypto-Agile Network (Q-CAN), connecting two data centers over deployed fibers. A third quantum node has also been established. This work was led by Dr. Charles Lim and his team.This announcement follows JPMorgan Chase & Co.’s Global CIO Lori Beer’s interview with The Banker on the subject of hashtag#quantum hashtag#security in March 2024.Link to the Press Release: https://lnkd.in/gkhWBTG3Link to The Banker's article: https://lnkd.in/g76EJGpu
2024/05/09
JPMorgan Chase & Co. | Managing Director, Head of Research and Engineering
239
https://www.linkedin.com/feed/update/urn:li:activity:7194139246801080320
7
8
KAN: Kolmogorov-Arnold NetworksZiming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljačić, Thomas Y. Hou, Max TegmarkInspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no linear weights at all -- every weight parameter is replaced by a univariate function parametrized as a spline. We show that this seemingly simple change makes KANs outperform MLPs in terms of accuracy and interpretability. For accuracy, much smaller KANs can achieve comparable or better accuracy than much larger MLPs in data fitting and PDE solving. Theoretically and empirically, KANs possess faster neural scaling laws than MLPs. For interpretability, KANs can be intuitively visualized and can easily interact with human users. Through two examples in mathematics and physics, KANs are shown to be useful collaborators helping scientists (re)discover mathematical and physical laws. In summary, KANs are promising alternatives for MLPs, opening opportunities for further improving today's deep learning models which rely heavily on MLPs.https://lnkd.in/djt5k9seAIFI - Artificial Intelligence Finance Institute
2024/05/02
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
212
https://www.linkedin.com/feed/update/urn:li:activity:7191543274195533828
7
0
How likely is manipulation and collusion in electronic markets driven by AI? Delegates heard from Professor Álvaro Cartea of the Oxford-Man Institute of Quantitative Finance, University of Oxford who really knows his brief. His keynote on reinforcement learning and the applications and risks of using AI in the capital markets was detailed and a must-see for capital market professionals with an eye on the future use of this technology in their sector. Professor Cartea spoke about his research which has found that manipulative algorithms learn how predictive signals work and how to manipulate those signals in the market to their advantage – otherwise known as spoofing. They can also co-ordinate actions to achieve supracompetitive outcomes, effectively manipulating markets. Machines don’t have a moral compass but will look for the optimal outcome dependent on their programming and the rules they are following. Brilliant, measured analysis of the likely impact of AI applied to financial markets for ICMA’s delegates. #ICMAAGM2024 #Brussels #global #Europe #joinus #capitalmarkets #event #csuite #AI
2024/05/24
CFX Labs Inc. | Co-Founder
18
https://www.linkedin.com/feed/update/urn:li:activity:7199839470550999040
6
6
I'm excited to share that I am going to JPMorgan Chase & Co. this summer as a research intern in quantum computing! Thanks for the support from Professor Ilya Safro and I look forward to working on great projects with the team Ruslan Shaydulin, Marco Pistoia.
2024/05/20
JPMorgan Chase & Co. | Managing Director, Head of Research and Engineering
66
https://www.linkedin.com/feed/update/urn:li:activity:7198058554572910592
6
3
Statistical Mechanics of Deep Learning ▸ Haim Sompolinsky (Hebrew Univ.) Kavli Institute for Theoretical Physics AIFI - Artificial Intelligence Finance Institute
2024/05/14
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
130
https://www.linkedin.com/feed/update/urn:li:activity:7196223325101056000
6
13
Stock-Bond Correlation: Theory & Empirical Results Back to the basics of asset management. New publications from Amundi Investment Institute. With Lorenzo Portelli, we explore the topic of stock-bond correlation, an essential component for multi-asset portfolios, CTA strategies, risk parity funds, LDI rebalancing and some algo trading. Is the correlation between stocks and bonds positive or negative? Should it be positive or negative? In fact, the answer to these two questions depends on several parameters such as sensitivity to inflation, growth, credit risk component, monetary policy response, portfolio composition, investor behavior, etc. First, we note that the negative correlation between stocks and bonds is a purely US-centric view since the European debt crisis. In many countries, it has been positive for many years. Second, the stock-bond correlation is strongly related to the risk premium theory and has a large impact on the bond risk premium thanks to the covariance risk premium. This means that bonds can be both performance and hedging assets. This calls into question the fact that US bonds have been the universal hedging asset for equity markets since 2015. Third, we can ask whether there is one or more stock-bond correlations, how it depends on the portfolio construction and how the aggregate stock-bond correlation at the portfolio level relates to the individual stock-bond correlation at the security level. It is interesting to note that diversification reduces volatility but increases correlation, which means that correlation leverage is important when interpreting the stock-bond correlation. Fourth, the level of the stock-bond correlation is explained by only a few observations, while it is zero for the majority of observations. The dependence function of these "extreme" observations is completely different between 1980-2000 and 2000-2020 and can be p
2024/05/14
Financial Risk and Engineering, NYU School of Engineering | Adjunct Professor
369
https://www.linkedin.com/feed/update/urn:li:activity:7196166197808111619
6
9
Happy to share that Dr. Charles Lim, Global Head of hashtag#QuantumCommunications and hashtag#QuantumCryptography, has been promoted Managing Director, thanks to his scientific hashtag#quantum breakthroughs and incredible progress in deploying hashtag#QuantumKeyDistribution (hashtag#qkd) at JPMorgan Chase & Co. Congratulations, Charles!
2024/05/04
JPMorgan Chase & Co. | Managing Director, Head of Research and Engineering
121
https://www.linkedin.com/feed/update/urn:li:activity:7192377364192927744
6
3
Wow - as big as the Lord of the Rings?A non-linear factor model outperforming exisiting linear multifactor models?!Link to National Bureau of Economic Research working paper: https://lnkd.in/gB_H-exnWhat do you think?Cc: Nicola Borri Marcos Lopez de Prado Igor Halperin Alexander Fleiss Tony Berkman Harvey Stein Yu Yu Lisa Schirf Andrew Chin Judith G. Gregory Pelts Jim Kyung-Soo Liew, Ph.D. Claudia Perlich Lisa Huang Sasha Stoikov Petter Kolm Francesco Fabozzi Minh Trinh Joseph Simonian, Ph.D. Dr Miquel Noguer i Alonso Amit Gandhi Peter Cotton hashtag#assetpricing hashtag#financialmarkets hashtag#factormodels hashtag#nonlinearity
2024/04/29
Financial Risk and Engineering, NYU School of Engineering | Adjunct Professor
14
https://www.linkedin.com/feed/update/urn:li:activity:7190718318339608577
6
9
David Hilbert's 10 selected papers: 1. "Über die Theorie der algebraischen Formen" (1890): This seminal paper introduced Hilbert's Basis Theorem, crucial for commutative algebra and algebraic geometry, asserting that every ideal in a polynomial ring over a field is finitely generated. 2. "Über die vollen Invariantensysteme" (1893): Hilbert proved that systems of algebraic forms possess a finite basis of invariants and covariants, resolving a long-standing problem in invariant theory. 3. "Grundlagen der Geometrie" (1899): Hilbert presented a set of axioms for geometry, refining Euclidean concepts and supporting the development of non-Euclidean geometries, such as hyperbolic and elliptic geometries. 4. "Über die Grundlagen der Logik und der Arithmetik" (1900): In this address, Hilbert posed challenges in logic and arithmetic, stimulating research in mathematical logic and the formalization of theories. 5. "Mathematical Problems" (1902): This paper, an expansion of his 1900 speech, outlined 23 research problems across various mathematical disciplines, guiding 20th-century mathematical research. 6. "Über die Grundlagen der Mathematik" (1922, co-authored with Wilhelm Ackermann): Part of Hilbert's program, this work aimed at proving the consistency of arithmetic through a finite set of axioms, seeking a secure foundation for mathematics. 7. "Über das Unendliche" (1925): Hilbert discussed infinity, introducing "Hilbert's Hotel" to illustrate the counterintuitive properties of infinite sets, influencing foundational mathematics and set theory. 8. "Die Grundlagen der Physik" (1915-16): Hilbert contributed to general relativity by formulating the Hilbert action, connecting geometry and physics through the Lagrangian formulation of Einstein's field equations. 9. "Neue Begründung der Bolyai-Lobatschewskischen Geometrie" (1901): This paper provided a new
2024/05/25
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
206
https://www.linkedin.com/feed/update/urn:li:activity:7199834165524004866
5
3
Fantastic work as always from the brilliant Charles-Albert Lehalle of Abu Dhabi Investment Authority (ADIA) & fellow researchers Sébastien Geeraert Barak Pearlmutter, Olivier Pironneau Adil Reghai Adil Reghai Maynooth University
2024/05/21
Shell | Systematic Trader
60
https://www.linkedin.com/feed/update/urn:li:activity:7198369638190051331
5
8
We know that European Value stocks, despite this year’s outperformance continue to trade on historically low valuations, and naturally a portfolio of cheap stocks will tend to have a higher dividend yield. But to also see such a high buyback yield alongside that dividend yield is unusual. Many ask us what is the catalyst for these stocks to re-rate and when, but with a running yield of 8% you can certainly afford to wait and see.
2024/05/15
Societe Generale | Head of Quantitative Equity Research
69
https://www.linkedin.com/feed/update/urn:li:activity:7196431685146628097
5
87
VR for Cows?Yes... A farm in Russia is testing VR headsets on cows, showing them calming summer fields to reduce anxiety and potentially increase their milk production. In my opinion VR shouldn't be an excuse for poor living conditions. The focus should be on providing cows with a more natural environment whenever possible.There might be niche applications. VR could potentially benefit cows in situations where they can't access real pastures due to injuries, harsh weather, or limited space. But even then, VR should be viewed as a temporary supplement, not a permanent solution.After all, happy cows with access to pastures are likely the healthiest and most productive ones.What do you think?Follow Endrit Restelica to stay up to date with tech.hashtag#tech hashtag#vr
2024/05/10
CFX Labs Inc. | Co-Founder
222
https://www.linkedin.com/feed/update/urn:li:activity:7194604951208140800
5
7
Delighted to share that JPMorgan Chase & Co.'s Global Technology Applied Research Quantum Computing article on hashtag#quantum federatedlearning has just been published in the Quantum Science and Technology journal.Privacy concerns in distributed computing protocols, particularly in classical hashtag#FederatedLearning, where client data risks exposure through gradient inversion by servers, represent a significant challenge. This study introduces innovative quantum federated learning (QFL) protocols, leveraging hashtag#QuantumCommunications to enhance privacy by concealing individual client gradients. These protocols not only advance privacy preservation but also maintain low communication overhead compared with classical solutions, such as hashtag#HomomorphicEncryption. They could be integrated with hashtag#QuantumKeyDistribution (hashtag#QKD) techniques to further protect client’s information against third-party attacks. This research contributes to the development of secure distributed quantum hashtag#MachineLearning, addressing pivotal privacy issues in the hashtag#QuantumComputing era.Link to the scholarly article:https://lnkd.in/ecBWtmd6Co-authors: Changhao Li, Niraj Kumar, Zhixin Song, Shouvanik Chakrabarti and Marco Pistoia
2024/05/10
JPMorgan Chase & Co. | Managing Director, Head of Research and Engineering
255
https://www.linkedin.com/feed/update/urn:li:activity:7194441684787580929
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Ayoub HAIDA and I wrote a paper on: Deep Learning Approach for Multi-Asset Option PricingWe introduce an algorithm designed to address semi-linear high-dimensional partial differential equations (PDEs) that arise in the multi-asset option pricing. This approach draws an intriguing analogy between forward-backward stochastic differential equations (FBSDEs) and deep learning (DL), wherein the solution’s gradient assumes the role of a policy function in sense of a reinforcement learning problem. The loss function, in this context, quantifies the disparity between the specified terminal condition and the FBSDE’s solution. To efficiently approximate the policy function, we employ several time-dependent neural networks following the principles of deep learning. Harnessing Python and TensorFlow, we conducted a comprehensive series of numerical experiments to assess the efficiency and accuracy of the proposed algorithm for several multi-asset option pricing models such as 100-dimensional Black-Scholes model, as well as incorporating different interest rates and a risk that a default occurs.You can download it here:https://lnkd.in/dfGm6pJxAIFI - Artificial Intelligence Finance Institutehashtag#aiinfinance hashtag#quantfinance hashtag#quantitativefinance
2024/05/03
Artificial Intelligence Finance Institute - AIFI | Founder at Artificial Intelligence Finance Institute
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https://www.linkedin.com/feed/update/urn:li:activity:7191959810026680320
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"disagreement + short-sale constraints = overpricing"<br><br>Owen Lamont on rising borrowing costs and the stock loan market.<a href="https://t.co/Y8E11WRPCq">https://t.co/Y8E11WRPCq</a> <a href="https://t.co/9CuEsyUNlU">pic.twitter.com/9CuEsyUNlU</a>
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https://twitter.com/quantseeker/status/1793755047551287408
2024/05/23
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https://t.co/Y8E11WRPCqhttps://twitter.com/quantseeker/status/1793755047551287408
@quantseeker
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https://twitter.com/quantseeker/status/1793332442800996503
2024/05/22
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https://twitter.com/ManGroup?ref_src=twsrc%5Etfwhttps://twitter.com/quantseeker/status/1793332442800996503
@quantseeker
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https://twitter.com/quantseeker/status/1793226244793704614
2024/05/22
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https://twitter.com/ManGroup?ref_src=twsrc%5Etfwhttps://twitter.com/quantseeker/status/1793226244793704614
@quantseeker
Great interview with <a href="https://twitter.com/CliffordAsness?ref_src=twsrc%5Etfw">@CliffordAsness</a> <a href="https://t.co/WnyS6RxGic">https://t.co/WnyS6RxGic</a>
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https://twitter.com/quantseeker/status/1793186398628982836
2024/05/22
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https://twitter.com/CliffordAsness?ref_src=twsrc%5Etfwhttps://twitter.com/quantseeker/status/1793186398628982836
@quantseeker
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