A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
AlphaZero reveals new chess concepts learnable by top experts.
Chess engines Stockfish and LCZero differ in their approach to solving endgame puzzles.
Transformers learn to predict chess moves with surprising accuracy and strength.
Constructing agents with planning capabilities has long been one of the main challenges in the pursuit of artificial intelligence. Tree-based planning methods have enjoyed huge success in challenging domains, such as chess and Go, where a perfect simulator is available. However, in real-world problems the dynamics gove…
The paper proposes a method to evaluate superhuman models by checking for logical inconsistencies.
AlphaZero assesses new chess variants for balance and dynamics.
We present an end-to-end learning method for chess, relying on deep neural networks. Without any a priori knowledge, in particular without any knowledge regarding the rules of chess, a deep neural network is trained using a combination of unsupervised pretraining and supervised training. The unsupervised training extra…
This article reports on an investigation of the use of convolutional neural networks to predict the visual attention of chess players. The visual attention model described in this article has been created to generate saliency maps that capture hierarchical and spatial features of chessboard, in order to predict the pro…
AlphaZero learns human chess knowledge during training.
This paper demonstrates the use of genetic algorithms for evolving: 1) a grandmaster-level evaluation function, and 2) a search mechanism for a chess program, the parameter values of which are initialized randomly. The evaluation function of the program is evolved by learning from databases of (human) grandmaster games…
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate expert (or mentor), we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Com…
Crazyhouse is a chess variant that incorporates all of the classical chess rules, but allows users to drop pieces captured from the opponent as a normal move. Until 2018, all competitive computer engines for this board game made use of an alpha-beta pruning algorithm with a hand-crafted evaluation function for each pos…
Dimensionality reduction is ubiquitous in analysis of complex dynamics. The conventional dimensionality reduction techniques, however, focus on reproducing the underlying configuration space, rather than the dynamics itself. The constructed low-dimensional space does not provide complete and accurate description of the…
In this paper we demonstrate how genetic algorithms can be used to reverse engineer an evaluation function's parameters for computer chess. Our results show that using an appropriate mentor, we can evolve a program that is on par with top tournament-playing chess programs, outperforming a two-time World Computer Chess …
FGNNs improve game-playing AI by exploiting symmetries.
This paper demonstrates the use of genetic algorithms for evolving a grandmaster-level evaluation function for a chess program. This is achieved by combining supervised and unsupervised learning. In the supervised learning phase the organisms are evolved to mimic the behavior of human grandmasters, and in the unsupervi…
Language models trained on chess board states outperform those on moves, even with causal masking.
We prove that the homotopy class of a Morin mapping f: P^p --> Q^q with p-q odd contains a cusp mapping. This affirmatively solves a strengthened version of the Chess conjecture [DS Chess, A note on the classes [S_1^k(f)], Proc. Symp. Pure Math., 40 (1983) 221-224] and [VI Arnol'd, VA Vasil'ev, VV Goryunov, OV Lyashenk…
In this paper we present the first results of a pilot experiment in the capture and interpretation of multimodal signals of human experts engaged in solving challenging chess problems. Our goal is to investigate the extent to which observations of eye-gaze, posture, emotion and other physiological signals can be used t…
From the early days of computing, games have been important testbeds for studying how well machines can do sophisticated decision making. In recent years, machine learning has made dramatic advances with artificial agents reaching superhuman performance in challenge domains like Go, Atari, and some variants of poker. A…
In this paper we construct and study a new 15-vertex triangulation of the complex projective plane $\CP^2$. The automorphism group of is isomorphic to . We prove that the triangulation is the minimal by the number of vertices triangulation of $\CP^2$ admitting a chess colouring of four-dimens…
Deep RL shows promise in algo trading, but more research needed.
Monte Carlo Tree Search improves financial derivative hedging efficiency.
ESCHER avoids importance sampling to estimate regret in large games.
In ranking problems, the goal is to learn a ranking function from labeled pairs of input points. In this paper, we consider the related comparison problem, where the label indicates which element of the pair is better, or if there is no significant difference. We cast the learning problem as a margin maximization, and …
The AlphaZero algorithm for the learning of strategy games via self-play, which has produced superhuman ability in the games of Go, chess, and shogi, uses a quantitative reward function for game outcomes, requiring the users of the algorithm to explicitly balance different components of the reward against each other, s…
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go. These games have a discrete action space. However, many real-world reinforcement learning domains have continuous action spaces, for example in robotic control, na…
AI stocks hedge against AI singularity's economic impact.
Study analyzes AI's impact on firms, markets, and workers using large language model data.
New AI stock indices classify firms' AI engagement using 10-K filings.
The paper analyzes risk spillovers between AI ETFs, AI tokens, and green markets.
This review covers AI in finance, challenges, techniques, and opportunities.
Explainable AI improves human decision accuracy but does not enhance it significantly.
The paper explores AI in finance, focusing on XAI's role in enhancing interpretability and trust.
Paper defines AI-specific loss reconstruction problem and introduces CER framework.
Experiment shows cognitive biases impact human-AI collaboration, highlighting the need for diverse evaluator samples.
AI enhances financial forecasting with challenges in regulation and privacy.
AI+MPS workshop aims to strengthen AI's role in science.
Improved AI patent classifier measures U.S. and China's AI patenting.
FST.ai 2.0 improves Taekwondo decision-making with AI, reducing review time and increasing trust.
Article evaluates AI security threats and proposes multiple measures.
AI threatens financial stability through misuse and stealth adoption.
Adversarial policies beat superhuman Go AI systems.
The DoD needs a robust process to evaluate AI/ML model performance and robustness.
Paper tackles AI risks by customizing metrics and models.
Qlib aims to integrate AI into quantitative investment.
Open AI models affect bond yields differently than closed ones.
AI systems are being deployed to support human decision making in high-stakes domains. In many cases, the human and AI form a team, in which the human makes decisions after reviewing the AI's inferences. A successful partnership requires that the human develops insights into the performance of the AI system, including …