Awesome List Updates on Jul 22, 2022

12 awesome lists updated today.

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1. Awesome Browser Extensions for Github

github.expandinizr (⭐131)

Add breakpoints at 1400px, 1600px and 1800px for full GitHub experience on large screens. Also removes the truncating of file and directory names in the repository browser.

Installs: 2000 | Stars: 131 | Last update: n/a

2. Awesome Db Tools

SQL / Learning

• Advanced SQL Puzzles (⭐644) - Difficult set-based SQL puzzles.

• Hackerrank - Practice coding, prepare for interviews, and get hired.

• LeetCode - Enhance your skills, expand your knowledge and prepare for technical interviews.

• Select Star SQL - Free interactive book which aims to be the best place on the internet for learning SQL.

• StrataScratch - Data science educational resources.

• SQL Murder Mystery (⭐1.7k) - Self-directed lesson to learn SQL concepts and commands and a fun game for experienced SQL users to solve an intriguing crime.

SQL / Scripts

• DBA MultiTool (⭐90) - T-SQL scripts for the long haul: optimizing storage, on-the-fly documentation, and general administrative needs for SQL Server.

• pgx_scripts (⭐1.4k) - A collection of useful little scripts for database analysis and administration, created by our team at PostgreSQL Experts.

• pgsql-bloat-estimation (⭐525) - Queries to mesure statistical bloat in indexes and tables for PostgreSQL.

• pgWikiDont - SQL test that checks if your database follows rules from https://wiki.postgresql.org/wiki/Don't_Do_This.

• pg-utils (⭐1.1k) - Useful PostgreSQL utilities.

• Postgres cheat sheet - Useful SQL-scripts and commands by <timescale.com>.

• postgres_dba (⭐1.1k) - The missing set of useful tools for Postgres DBAs and all engineers.

• postgres_queries_and_commands.sql - Useful PostgreSQL Queries and Commands.

• TPT (⭐665) - These sqlplus scripts are for Oracle Database performance optimization & troubleshooting.

3. Awesome Open Source Supporters

Miscellaneous

• Firezone requires-approval - Self-hosted remote access built on WireGuard.

4. Awesome Composer

Plugins / IRC

• Composer-Velocita (⭐27) - Fast and reliable Composer package downloads using Velocita (⭐61): a caching reverse proxy that does not require you to modify your projects.

5. Awesome Mac

Utilities / General Tools

• Etcher - Flash OS images to SD cards & USB drives, safely and easily.

6. Awesome Vue

Resources / Tutorials

• Developing a web application with Vue.js 3 and Vite.js (French) par Mickael Baron

• Deploying a web application powered by Vue.js 3 with Docker (French) par Mickael Baron

Components & Libraries / UI Utilities

• vorms (⭐647) - Vue Form Validate with Composition API.

7. Awesome Rust

Applications / Blockchain

• Holochain (⭐1.2k) - Scalable P2P alternative to blockchain for all those distributed apps you always wanted to build.

Applications / System tools

• pueue (⭐5.4k) - Manage your long running shell commands.

Libraries / Compression

• gzip
  • zopfli (⭐40) [zopfli] - implementation of the Zopfli compression algorithm for higher quality deflate or zlib compression

Libraries / Mobile

• Generic
  • Geal/rust_on_mobile (⭐171) - iOS CocoaPods / Android JNI
  • redbadger/crux (⭐1.9k) [crux_core] - Cross-platform app development. Crux helps you share your app's business logic and behavior across mobile (iOS/Android) and web - as a single reusable core.

8. Awesome Cpp

Websites

• C++ Tutorial for Beginners - A comprenhensive tutorial on C++ curated by trained experts.

9. Free Programming Books (English, By Programming Language)

C++ / Non-X86

• A Complete Guide to Standard C++ Algorithms (⭐996) - Šimon Tóth (PDF, LaTeX) (CC BY-NC-SA) (🚧 in process)

HTML and CSS / Spock Framework

• Practical Series: A website template - Michael Gledhill (HTML)

10. Awesome Software Patreons

Open Source Projects

• Firezone - Self-hosted VPN server using WireGuard.

11. Awesome Zsh Plugins

Plugins / superconsole - Windows-only

• alehouse (⭐16) - Contains short aliases for brew commands, inspired by betterbrew.

• aws-cli-mfa (⭐20) - AWS CLI MFA plugin based on sweharris' aws-cli-mfa (⭐25). Supports specifying mfa_device in profile.

• aws-plugin (⭐2) - Adds helper functions for aws command. Includes mfa and assume-role helpers.

• boss-docker (⭐1) - Manages docker on macOS.

• bumblebee (⭐2) - A plugin to toggle prepending optirun in the command line.

• cdr (⭐17) - Easy setup of cdr for ZSH.

• chgo (⭐0) - Clone of chruby modified to make it easy to switch between multiple Go versions.

• declare-zsh (⭐8) - A parser for zinit (⭐3.5k) commands in .zshrc. It allows you to perform the following actions on .zshrc from the command-line - enable and disable plugins add or remove snippets.

• evil-registers (⭐43) - Extends ZLE vi commands to remotely access named registers of the vim and nvim editors, and system selection and clipboard.

• exa (zplugin) (⭐0) - replace ls with ogham/exa (⭐24k).

• f-shortcuts (⭐8) - Makes a shortcuts toolbar using F1 to F12 keys.

• fancy-ctrl-z (⭐20) - Broken out version of the version in oh-my-zsh so users of other frameworks don't have to import all of oh-my-zsh.

• get-jquery (⭐1) - Plugin for fast downloading the jQuery library from code.jquery.com.

• gimme (⭐2) - Manage Go installations with gimme (⭐707).

• git-aliases (mdumitru) (⭐30) - Broken out version of the version in oh-my-zsh so users of other frameworks don't have to import all of oh-my-zsh.

• git-is-clean (⭐2) - This function will return true or false depending on if it finds out your git repo is dirty or not.

• git-smart-commends-wrapper (⭐0) - Wraps git-smart-commands (⭐12) to make it compatible with the oh-my-zsh plugins system.

• history-popup (⭐0) - Captures the PageUp key and uses dialog to open a popup menu with the history, so the user can interactively navigate through it and pick the history line to bring back to the prompt.

• iterm2 (⭐10) - Packs iTerm 2's ZSH integration scripts into a ZSH plugin to avoid polluting your $HOME directory, with a negligible time increase of only 2ms.

• jenv-lazy (⭐7) - A ZSH plugin for lazy loading of jEnv.

• kitsunebook (⭐0) - KitsuneBook plugin for oh-my-zsh.

• last-working-dir-tmux (⭐1) - Keeps track of the last used working directory globally and per tmux (⭐37k) session and automatically jumps into it for new shells.

• lesaint-git (⭐0) - Replacement git plugin for oh-my-zsh-compatible frameworks.

• lesaint-mvn (⭐0) - Maven plugins for oh-my-zsh.

• lux (⭐34) - ZSH plugin to toggle the light & dark modes of macOS, iTerm 2, Visual Studio Code and other items and applications via the lux command. Highly customizable: included items can be configured by defining variables. Highly extensible: items can be added by defining functions. Includes a macos_is_dark helper function to determine if the macOS dark mode is active for use in theming.

• mercurial (⭐2) - Extracted from oh-my-zsh so you can use it without the rest of oh-my-zsh.

• pkenv (⭐1) - Installs and loads pkenv.

• plugin (⭐12) - Creates custom oh-my-zsh plugins from a boilerplate template. Very oh-my-zsh centric, the generated plugins will need editing to work with other frameworks.

• rvm (⭐2) - Initiates rvm (⭐5.1k) and adds rubygem binaries (like compass) accessible in the user's $PATH.

• virtualenv-mod (⭐1) - A modified virtualenv ZSH plugin for oh-my-zsh.

• virtualenv-prompt (⭐35) - A fork of the virtualenv plugin from upstream oh-my-zsh. Adds support for customizing the virtualenv prompt in oh-my-zsh themes.

• yadm (⭐9) - Displays a warning if there are local yadm configuration changes.

• yeoman (⭐40) - Edouard Lopez's Yeoman plugin for oh-my-zsh, compatible with yeoman version ≥1.0 (includes options and command auto-completion).

• zshmarks (⭐277) - A port of Bashmarks (by Todd Werth), a simple command line bookmarking plugin, for oh-my-zsh.

Themes / superconsole - Windows-only

• bearings (⭐199) - A fast, clean, super-customizable shell prompt. Includes decorators for current directory, git status, exit code of last command, duration of last command, background jobs & username.

• vehemence (⭐0) - Includes decorators for pwd, user@host, tty, time, last command exit code and git status.

12. Awesome Agi Cocosci

Abduction / Explanation

• Abduction - Plato Stanford. A computational philosophy account on Abduction, one of the three thinking patterns besides Induction and Deduction, being unique for its potential to introduce new ideas into current knowledge.

• Scientific Explanation - Plato Stanford. A computational philosophy account on Scientific Explanation, a canonical application of Abduction.

• Scientific Reduction - Plato Stanford. A computational philosophy account on Scientific Reduction, which comes with no explicit boundary with Explanation.

• Non-monotonic Logic - Plato Stanford. A computational philosophy account on Non-monotonic Logic, a family of formal frameworks devised to capture and represent defeasible inference.

• Philosophical Writings of Peirce - Courier Corporation, 1955. [All Versions]. Original writings by C. S. Peirce, the philosopher who first introduces the concept of Abduction.

• Abductive Reasoning and Learning - Springer, 2000. [All Versions]. This book contains leading survey papers on the various aspects of Abduction, both logical and numerical approaches.

• Explanation and Abductive Inference - The Oxford Handbook of Thinking and Reasoning, 2012. [All Versions]. This chapter reviews evidence from cognitive psychology and cognitive development concerning the structure and function of explanations, with a focus on the role of explanations in learning and inference. The findings highlight the value of understanding explanation and abductive inference both as phenomena in their own right and for the insights they provide concerning foundational aspects of human cognition, such as representation, learning, and inference.

• The structure and function of explanations - Trends in Cognitive Sciences, 2006. [All Versions]. Generating and evaluating explanations is spontaneous, ubiquitous and fundamental to our sense of understanding. Recent evidence suggests that in the course of an individual's reasoning, engaging in explanation can have profound effects on the probability assigned to causal claims, on how properties are generalized and on learning. These effects follow from two properties of the structure of explanations: explanations accommodate novel information in the context of prior beliefs, and do so in a way that fosters generalization.

• Explanatory Preferences Shape Learning and Inference - Trends in Cognitive Sciences, 2016. [All Versions]. People often learn by seeking explanations, and they assess the viability of hypotheses by considering how well they explain the data. An emerging body of work reveals that both children and adults have strong and systematic intuitions about what constitutes a good explanation, and that these explanatory preferences have a systematic impact on explanation-based processes. In particular, people favor explanations that are simple and broad, with the consequence that engaging in explanation can shape learning and inference by leading people to seek patterns and favor hypotheses that support broad and simple explanations.

• Explanation, updating, and accuracy - Journal of Cognitive Psychology, 2016. [All Versions]. There is evidence that people update their credences partly on the basis of explanatory considerations. Philosophers have recently argued that to minimise the inaccuracy of their credences, people's updates also ought to be partly based on such considerations. However, there are many ways in which explanatory considerations can factor into updating, not all of which minimise inaccuracy. It is an open question whether in their updating, people take explanatory considerations into account in a way that philosophers would deem recommendable. To address this question, the authors re-analyse data from an experiment reported in Douven and Schupbach, “The role of explanatory considerations in updating”.

• Best, second-best, and good-enough explanations: How they matter to reasoning - Journal of Experimental Psychology, 2018. [All Versions]. There is a wealth of evidence that people’s reasoning is influenced by explanatory considerations. Three experiments investigate the descriptive adequacy of a precise proposal to be found in the philosophical literature, to wit, that we should infer to the best explanation, provided certain additional conditions are met. The main conslusions are that (a) the quality of an explanation is a good predictor of people’s willingness to accept that explanation, and a better predictor than the prior probability of the explanation, and (b) if more than one possible explanation is given, people are the less willing to infer the best explanation the better they deem the second-best explanation.

• How explanation guides belief change - Trends in Cognitive Sciences, 2021. [All Versions]. Philosophers have argued that people ought to change their graded beliefs via Bayes’ rule. Recent work in psychology indicates that people sometimes violate that rule by attending to explanatory factors. Results from computational modeling suggest that such violations may actually be rational.

• Patterns of abduction - Synthese, 2007. [All Versions]. A categorization for Abduction in the account of pure philosophy.

• On the distinction between Peirce's abduction and Lipton's Inference to the best explanation - Synthese, 2011. [All Versions].

• Probabilistic alternatives to Bayesianism: the case of explanationism - Frontiers in Psychology, 2015. [All Versions]. A non-Bayesian account of Abduction.

• A Probabilistic Theory of Abductive Reasoning - ICAART, 2021. [All Versions]. A probabilistic perspective for interpreting Abductive Reasoning.

• Abduction, Induction, and Analogy - Model-Based Reasoning in Science and Technology, 2010. [All Versions]. The distinctions and relations between Abduction, Induction, and Analogy.

• Remembrance of inferences past: Amortization in human hypothesis generation - Cognition, 2018. [All Versions]. A rational account of human hypothesis generation.

• Explanation-seeking curiosity in childhood - Current Opinion in Behavioral Sciences, 2020. [All Versions]. A piece of developmental pshchological evidence for Abduction in young children.

Abduction / Scientific Discovery

• Scientific Discovery - Plato Stanford. A computational philosophy account on Scientific Discovery, the process or product of successful scientific inquiry, sometimes an Abduction-like (Explanation) thinking pattern.

• Models of Discovery: And Other Topics in the Methods of Science - Springer, 1977. [All Versions]. The original book on search as scientific thinking.

• Scientific discovery: Computational explorations of the creative processes - MIT Press, 1987. [All Versions]. The book is divided into four parts. Part I introduces the subject of discovery, defines the scope of our work, and discusses some of the issues that have surrounded and still surround our topic. Parts II and III contain the main body of our results, largely in the form of accounts of the performance of computer programs that simulate human thought processes to make scientific discoveries. Part II is devoted largely to the processes for inducing quantitative theories from data. Part III is devoted mainly to the processes for inducing qualitative descriptive and structural theories from data. In Part IV, on the basis of our experience, we discuss at a lower level of precision how the programs described in the preceding chapters could be combined into a single, more general discovery system, and we describe a wide range of the other component processes that enter into scientific discovery.

• Dual Space Search During Scientific Reasoning - Cognitive Science, 1988. [All Versions]. The original paper on the dual space search as scientific thinking theory.

• Complexity Management in a Discovery Task - CogSci'92, 1992. [All Versions]. Advanced experiments on dual space search.

• A dual-space model of iteratively deepening exploratory learning - International Journal of Human-Computer Studies, 1996. [All Versions]. This paper describes a cognitive model of exploratory learning, which covers both trial-and-error and instruction-taking activities. The model, implemented in Soar, is grounded in empirical data of subjects in a task-oriented, trial-and-error exploratory learning situation. A key empirical finding reflected in the model is the repeated scanning of a subset of the available menu items, with increased attention to items on each successive scan. This is explained in terms of dual search spaces, the external interface and the user's internal knowledge, both of which must be tentatively explored with attention to changing costs and benefits.

• Heuristics for Scientific Experimentation: A Developmental Study - Cognitive Psychology, 1993. [All Versions]. A piece of evidence on children have basic scientific thinking skills.

• A 4-Space Model of Scientific Discovery - CogSci'95, 1995. [All Versions]. Extending the dual space search.

• When to trust the data: Further investigations of system error in a scientific reasoning task - Memory & Cognition, 1996. [All Versions]. A behavioral account on the shift between bottom-up observation and top-down reasoning.

• Confirmation, disconfirmation, and information in hypothesis testing - Psychological Review, 1987. [All Versions]. A psychological account on hypothesis testing.

• Children and adults as intuitive scientists - Psychological Review, 1989. [All Versions]. A perspective against search as scientific thinking.

• Abduction and styles of scientific thinking - Synthese, 2021. [All Versions]. A computational philosophy account connecting Abduction and scientific thinking.

Abduction / Rationalization

• Imagination and the generation of new ideas - Cognitive Development, 2015. [All Versions]. A piece of evidence for rationalization in childhood.

• Coalescing the Vapors of Human Experience into a Viable and Meaningful Comprehension - CogSci'16, 2016. [All Versions]. Constrainted thinking as rationalization.

• How We Know What Not To Think - Trends in Cognitive Sciences, 2019. [All Versions]. A comprehensive review on rationalization.

• Rationalization is rational - Behavioral and Brain Sciences, 2020. [All Versions]. [Preprint]. Rationalization occurs when a person has performed an action and then concocts the beliefs and desires that would have made it rational. Then, people often adjust their own beliefs and desires to match the concocted ones. While many studies demonstrate rationalization, and a few theories describe its underlying cognitive mechanisms, we have little understanding of its function. Why is the mind designed to construct post hoc rationalizations of its behavior, and then to adopt them? This may accomplish an important task: transferring information between the different kinds of processes and representations that influence our behavior. Human decision making does not rely on a single process; it is influenced by reason, habit, instinct, norms, and so on. Several of these influences are not organized according to rational choice (i.e., computing and maximizing expected value). Rationalization extracts implicit information – true beliefs and useful desires – from the influence of these non-rational systems on behavior.

• Why Imaginary Worlds? The psychological foundations and cultural evolution of fictions with imaginary worlds - Behavioral and Brain Sciences, 2021. [All Versions]. A review of rationalization as imaginary worlds in fictions. The perspective proposes that imaginary worlds co-opt our preferences for exploration, which have evolved in humans and nonhuman animals alike, to propel individuals toward new environments and new sources of reward.

Abduction / Applications in AI

• Functional genomic hypothesis generation and experimentation by a robot scientist - Nature, 2004. [All Versions]. This paper describes a physically implemented robotic system that applies techniques from artificial intelligence to carry out cycles of scientific experimentation. The system automatically originates hypotheses to explain observations, devises experiments to test these hypotheses, physically runs the experiments using a laboratory robot, interprets the results to falsify hypotheses inconsistent with the data, and then repeats the cycle. The system is applied to the determination of gene function using deletion mutants of yeast (Saccharomyces cerevisiae) and auxotrophic growth experiments. The authors built and tested a detailed logical model (involving genes, proteins and metabolites) of the aromatic amino acid synthesis pathway.

• Interpretation as abduction - Artificial Intelligence, 1993. [All Versions]. Abduction is inference to the best explanation. The authors have developed an approach to abductive inference, called “weighted abduction”, that has resulted in a significant simplification of how the problem of interpreting texts is conceptualized. The interpretation of a text is the minimal explanation of why the text would be true. More precisely, to interpret a text, one must prove the logical form of the text from what is already mutually known, allowing for coercions, merging redundancies where possible, and making assumptions where necessary. It is shown how such “local pragmatics” problems as reference resolution, the interpretation of compound nominals, the resolution of syntactic ambiguity and metonymy, and schema recognition can be solved in this manner. Moreover, this approach of “interpretation as abduction” can be combined with the older view of “parsing as deduction” to produce an elegant and thorough integration of syntax, semantics, and pragmatics, one that spans the range of linguistic phenomena from phonology to discourse structure.

• Probabilistic Horn abduction and Bayesian networks - Artificial Intelligence, 1993. [All Versions]. This paper presents a simple framework for Horn-clause abduction, with probabilities associated with hypotheses. The framework incorporates assumptions about the rule base and independence assumptions amongst hypotheses. It is shown how any probabilistic knowledge representable in a discrete Bayesian belief network can be represented in this framework. The main contribution is in finding a relationship between logical and probabilistic notions of evidential reasoning. This provides a useful representation language in its own right, providing a compromise between heuristic and epistemic adequacy.

• ACLP: Abductive Constraint Logic Programming - The Journal of Logic Programming, 1999. [All Versions]. This paper presents the framework of Abductive Constraint Logic Programming (ACLP), which integrates Abductive Logic Programming (ALP) and Constraint Logic Programming (CLP). In ACLP, the task of abduction is supported and enhanced by its non-trivial integration with constraint solving. This integration of constraint solving into abductive reasoning facilitates a general form of constructive abduction and enables the application of abduction to computationally demanding problems. The paper studies the formal declarative and operational semantics of the ACLP framework together with its application to various problems.

• Machine Translation Using Abductive Inference - COLING, 1990. [All Versions]. An application of abduction in language translating.

Bayesian Modeling / Bayesian Induction

• Bayesian Epistemology - Plato Stanford. A computational philosophy account on the nature of uncertainty modeling in Bayesian Epistemology.

Bayesian Modeling / Generative Model

• A Theory of Generative ConvNet - ICML'16, 2016. [All Versions]. The authors show that a generative random field model, which they call generative ConvNet, can be derived from the commonly used discriminative ConvNet, by assuming a ConvNet for multi-category classification and assuming one of the category is a base category generated by a reference distribution. For a further assumption that the non-linearity in the ConvNet is Rectified Linear Unit (ReLU) and the reference distribution is Gaussian white noise, then a generative ConvNet model that is unique among energy-based models is obtained: The model is piecewise Gaussian, and the means of the Gaussian pieces are defined by an auto-encoder, where the filters in the bottom-up encoding become the basis functions in the top-down decoding, and the binary activation variables detected by the filters in the bottom-up convolution process become the coefficients of the basis functions in the top-down deconvolution process.

• Learning Latent Space Energy-Based Prior Model - NeurIPS'20, 2020. [All Versions]. [Project]. [Code (⭐35)]. A milestone paper on Latent Energy-Based Model.

• A Tutorial on Energy-Based Learning - Predicting Structured Data, MIT Press, 2006. [All Versiosn]. Yann LeCun's tutorial on energy-based learning.

Bayesian Modeling / Nonparametric Model

• Mixtures of Dirichlet Process with Applications to Bayesian Nonparametric Problems - The Annals of Statistics, 1974. [All Versions]. The original paper on Dirichlet Process modeling for non-parametric problems.

• Latent Semantic Indexing: A Probabilistic Analysis - Journal of Computer and System Sciences, 2000. [All Versions]. The original paper on hierarchical topic model.

• Finding scientific topics - Proceedings of the National Academy of Sciences, 2004. [All Versions]. A first step in identifying the content of a document is determining which topics that document addresses. This paper describes a generative model for documents, in which each document is generated by choosing a distribution over topics and then choosing each word in the document from a topic selected according to this distribution. The authors then present a Markov chain Monte Carlo algorithm for inference in this model. The authors use this algorithm to analyze abstracts from PNAS by using Bayesian model selection to establish the number of topics. This work shows that the extracted topics capture meaningful structure in the data, consistent with the class designations provided by the authors of the articles, and outline further applications of this analysis, including identifying “hot topics” by examining temporal dynamics and tagging abstracts to illustrate semantic content.

• Hierarchical topic models and the nested Chinese restaurant process - NeurIPS'03, 2003. [All Versions]. The original paper for nested Chinese restaurant process.

• Infinite Latent Feature Models and the Indian Buffet Process - Gatsby Computational Neuroscience Unit Technical Report 2005-001, 2005. [All Versions].

• The Indian Buffet Process: An Introduction and Review - Journal of Machine Learning Research, 2011. [All Versions]. The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. This work gives a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. The authors then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes.

• Statistical Predicate Invention - ICML'07, 2007. [All Versions]. This work proposes statistical predicate invention as a key problem for statistical relational learning. SPI is the problem of discovering new concepts, properties and relations in structured data, and generalizes hidden variable discovery in statistical models and predicate invention in ILP. This work proposes an initial model for SPI based on second-order Markov logic, in which predicates as well as arguments can be variables, and the domain of discourse is not fully known in advance. The proposed approach iteratively refines clusters of symbols based on the clusters of symbols they appear in atoms with (e.g., it clusters relations by the clusters of the objects they relate).

Bayesian Modeling / Bayesian Optimization

• A Tutorial on Bayesian Optimization - 2018. [All Versions]. Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. This tutorial describes how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. The authors then discuss more advanced techniques, including running multiple function evaluations in parallel, multi-fidelity and multi-information source optimization, expensive-to-evaluate constraints, random environmental conditions, multi-task Bayesian optimization, and the inclusion of derivative information. The authors conclude with a discussion of Bayesian optimization software and future research directions in the field. This tutorial provides a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications.

Complexity & Information Theory / Theory

• An introduction to Kolmogorov complexity and its applications - Springer, 2008. [All Versions]. The introductory book for Algorithmic Information Theory, especially the Kolmogorov complexity theory.

• Complexity and the representation of patterned sequences of symbols - Psychological Review, 1972. [All Versions]. Herbert Simon's review on subjective complexity.

• Algorithmic Information Theory - IBM Journal of Research and Development, 1977. [All Versions]. Chaitin's original paper on Algorithmic Information Theory.

Complexity & Information Theory / Dimensionality Reduction

• A global geometric framework for nonlinear dimensionality reduction - Science, 2000. [All Versions]. The original paper on spectrum clustering.

• Reducing the dimensionality of data with neural networks - Science, 2006. [All Versions]. The original paper on Variational Autoencoder.

• Representation Learning: A Review and New Perspectives - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013. [All Versions]. Yoshua Bengio's review on representation learning.

• Representation Learning: A Statistical Perspective - Annual Review of Statistics and Its Application, 2020. [All Versions]. Song-Chun Zhu and Ying Nian Wu's review on representation learning, in an account of statistics.

• Deep Learning and the Information Bottleneck Principle - IEEE Information Theory Workshop'15, 2015. [All Versions]. The first paper identifying the problem of information bottleneck in representation learning.

• On the information bottleneck theory of deep learning - Journal of Statistical Mechanics: Theory and Experiment, 2019. [All Versions].

Complexity & Information Theory / Visual Complexity

• Visual complexity: a review - Psychological Bulletin, 2006. [All Versions]. [APA]. A psychological account on visual complexity.

• Seeing and speaking: How verbal “description length” encodes visual complexity - Journal of Experimental Psychology, 2022. [All Versions]. [APA]. Empirical evidencs showing the relation between visual complexity and description length.

• How variability shapes learning and generalization - Trends in Cognitive Sciences, 2022. [All Versions]. A comprehensive review on the trade-off between variability and generalization ability.

Communications / Non-Verbal Communication

• The Interactive Evolution of Human Communication Systems - Cognitive Science, 2010. [All Versions]. Nicolas Fay's original paper on iconicity.

• Iconicity: From sign to system in human communication and language - Pragmatics & Cognition, 2014. [All Versions]. This paper explores the role of iconicity in spoken language and other human communication systems.

• Graphical Language Games: Interactional Constraints on Representational Form - Cognitive Science, 2007. [All Versions]. The first paper introducing the graphical language game.

• Pixelor: A Competitive Sketching AI Agent. So you think you can beat me? - ACM SIGGRAPH'20, 2020. [All Versions]. [Project]. Rationality in feature sketching.

• Pragmatic Inference and Visual Abstraction Enable Contextual Flexibility During Visual Communication - Computational Brain & Behavior, 2020. [All Versions]. A computational account on the rational behavior in graphical language games.

• Communicating artificial neural networks develop efficient color-naming systems - Proceedings of the National Academy of Sciences, 2021. [All Versions]. Simulating the emergence of code as the communication bottleneck in color learning task.

• Bridging cultural and cognitive perspectives on similarity reasoning - CogSci'22, 2022. [All Versions].

• Twelve-month-olds communicate helpfully and appropriately for knowledgeable and ignorant partners - Cognition, 2008. [All Versions]. The original paper on child pointing.

Communications / Pragmatics

• Pragmatics - Plato Stanford. A computational philosophy account of Pragmatics, whilch studies utterances in specific contexts.

• Pragmatic Language Interpretation as Probabilistic Inference - Trends in Cognitive Sciences, 2016. [All Versions]. Understanding language requires more than the use of fixed conventions and more than decoding combinatorial structure. Instead, comprehenders make exquisitely sensitive inferences about what utterances mean given their knowledge of the speaker, language, and context. Building on developments in game theory and probabilistic modeling, the authors describe the rational speech act (RSA) framework for pragmatic reasoning. RSA models provide a principled way to formalize inferences about meaning in context; they have been used to make successful quantitative predictions about human behavior in a variety of different tasks and situations, and they explain why complex phenomena, such as hyperbole and vagueness, occur. More generally, they provide a computational framework for integrating linguistic structure, world knowledge, and context in pragmatic language understanding.

• Processing gradable adjectives in context: A visual world study - Semantics and Linguistic Theory, 2016. [All Versions]. Adjective understanding as a rational inference in the context.

• Social Pragmatics: Preschoolers Rely on Commonsense Psychology to Resolve Referential Underspecification - Child Development, 2019. [All Versions]. A piece of evidence for children's capability on social pragmatics.

• Disentangling contributions of visual information and interaction history in the formation of graphical conventions - CogSci'19, 2019. [All Versions].

Communications / Language Compositionality

• Compositionality - Plato Stanford. A computational philosophy account on compositionality, one of the distinctive feature of language.

• The Principle of Semantic Compositionality - Topoi, 1994. [All Versions]. The original paper on the principle of semantic compositionality.

• On The Emergence Of Compositionality - Proceedings of the Evolution of Language Conference'06, 2006. [All Versions]. The original paper on the emergence of compositionality.

• Multi-Agent Cooperation and the Emergence of (Natural) Language - ICLR'17, 2017. [All Versions]. The original paper on the emergence of language in multi-agent reinforcement learning.

Problem Solving / Human-Level Problem Solving

• Elements of a theory of human problem solving - Psychological Review, 1958. [All Versions]. Herbert Simon's original idea on human problem solving.

• Human Problem Solving - Englewood Cliffs, NJ: Prentice-hall, 1972. [All Versions]. Herbert Simon's classic idea of human problem solving as search.

• Judgment under Uncertainty: Heuristics and Biases: Biases in judgments reveal some heuristics of thinking under uncertainty - Science, 1974. [All Versions]. Daniel Kahneman's classic idea of prospective theory.

• Computational evidence for hierarchically structured reinforcement learning in humans - Proceedings of the National Academy of Sciences, 2020. [All Versions]. A piece of evidence on hierarchical human planning.

• People construct simplified mental representations to plan - Nature, 2022. [All Versions]. A computational account on rational problem representation in human planning.

• Rapid trail-and-error learning with simulation supports flexible tool use and physical reasoning. - Proceedings of the National Academy of Sciences, 2020. [All Versions]. [Project]. [Appendix]. A computational account on rapid trail-and-error problem solving with a noisy prior model.

• Insightful problem solving and creative tool modification by captive nontool-using rooks - Proceedings of the National Academy of Sciences, 2009. [All Versions]. [Supplementary Material]. A piece of evidence on creative tool use in intelligent animals.

Problem Solving / Planning

• From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning - Journal of Artificial Intelligence Research, 2018. [All Versions]. Leslie Kaelbling's review on hierarchical Task-and-Motion-Planning (hierarchical TAMP).

• Integrated Task and Motion Planning - Annual Review of Control, Robotics, and Autonomous Systems, 2021. [All Versions]. Leslie Kaelbling's review on Task-and-Motion-Planning (TAMP).

• Differentiable Physics and Stable Modes for Tool-Use and Manipulation Planning - Robotics: Science and Systems, 2018. [All Versions].

Problem Solving / Intrinsic Motivation

• Intrinsically Motivated Reinforcement Learning - NeurIPS'04, 2004. [All Versions]. A comprehensive review on intrinsic reward functions in classic reinforcement learning.

• Adapting Behavior via Intrinsic Reward: A Survey and Empirical Study - Journal of Artificial Intelligence Research, 2020. [All Versions].

• Curiosity-driven Exploration by Self-supervised Prediction - ICML'17, 2017. [All Versions]. The original paper on curiosity as intrinsic motivation.

• UCB Exploration via Q-Ensembles - 2017. [All Versions].

• Variational Information Maximisation for Intrinsically Motivated Reinforcement Learning - NeurIPS'15, 2015. [All Versions]. The original paper on empowerment as intrinsic motivation.

Problem Solving / Reinforcement Learning

• Reinforcement learning: An introduction - MIT Press, 2018. [All Versions]. Richard Sutton's comprehensive book on reinforcement learning.

• Reinforcement learning: A survey - Journal of Artificial Intelligence Research, 1996. [All Versions]. Leslie Kaelbling's review on reinforcement learning.

• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning - Artificial Intelligence, 1999. [All Versions]. The original paper on operation reinforcement learning.

• On Monte Carlo Tree Search and Reinforcement Learning - Journal of Artificial Intelligence Research, 2017. [All Versions].

• Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review - 2018. [All Versions]. [Slides]. Sergey Levine's tutorial on treating reinforcement learning probabilisticly.

• Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability - NeurIPS'21, 2021. [All Versions]. A formal treatment on the generalization problem in reinforcement learning.

• Constrained Policy Optimization - ICML'17, 2017. [All Versions]. The original paper on constrained reinforcement learning (safe reinforcement learning).

• The Quest for a Common Model of the Intelligent Decision Maker - Multi-disciplinary Conference on Reinforcement Learning and Decision Making'22, 2022. [All Versions]. Richard Sutton's perspective on the future directions of reinforcement learning research.

Problem Solving / Inverse Reinforcement Learning

• Apprenticeship Learning via Inverse Reinforcement Learning - ICML'04, 2004. [All Versions]. Pieter Abbeel and Andrew Ng's original paper on inverse reinforcement learning (IRL).

Meta-Level Considerations / Rationality

• A Study of Thinking - Routledge, 1956. [All Versions]. This book is a pioneering account of how human beings achieve a measure of rationality in spite of the constraints imposed by time and ignorance.

Science Logology / AI Assisted Research

• Highly accurate protein structure prediction with AlphaFold - Nature, 2021. [All Versions]. This paper provides the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. This approach is a canonical application of observation- and explanation- based method for protein structure prediction instead of first-principle-based methods.

Learning with Cognitive Plausibility / Commonsense Knowledgebase

• Accuracy and Precision - Wikipedia. Wikipedia on the distinctions and the trade-off between accuracy and precision.

• Recognition-by-Components: A Theory of Human Image Understanding - Psychological Review, 1987. [All Versions]. The original paper on the recognition-by-components theory.

• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense - Engineering, 2020. [All Versions]. Yixin Zhu and Song-Chun Zhu's review on visual commonsense.

• Self-supervised Learning Through the eyes of a Child - NeurIPS'20, 2020. [All Versions]. Concept learning through near-natural co-occurrence frequency estimation.

• BONGARD-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning - NeurIPS'20, 2020. [All Versions].

• Learning and development in networks: The importance of starting small. - Cognition, 1993. [All Versions]. The original paper on the idea of curriculum learning.

• Curriculum Learning - ICML'09, 2009. [All Versions]. The original paper applying the idea of curriculum learning to machine learning.

• Inferring "Dark Matter" and "Dark Energy" from Videos - ICCV'13, 2013. [All Versions]. The original paper on latent state discovery from videos.

Paper Reading / Commonsense Knowledgebase

• How to Read a Paper - ACM SIGCOMM Computer Communication Review, 2007. [All Versions]. A comprehensive tutorial on reading scientific papers.

Literature Management / Commonsense Knowledgebase

• Construction of the Literature Graph in Semantic Scholar - NAACL'18, 2018. [All Versions]. Semantic Scholar with extracting feature and metadata from raw paper data.

• S2ORC: The Semantic Scholar Open Research Corpus - ACL'20, 2020. [All Versions]. An open corpus of academic papers released by Semantic Scholar.

• StateOfTheArt.AI - StateOfTheArtAI. For tracking, collecting and visualizing the development of AI research.

Knowledge Management / Commonsense Knowledgebase

• Knowledge organization - Wikipedia. Wikipedia on knowledge organization methods.

• Zettelkasten - Wikipedia. Wikipedia on the Zettelkasten method.

• Roam Research - Roam Research. For linked document management, visualization, and sharing.

• Foam - Foambubble. For linked document management, visualization, and sharing, opensourced softward built on VSCode.

• Building a Second Brain - Forte Labs, LLC. Connecting ideas in graphs.

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