‘algorithms’ tag
- See Also
- Gwern
-
Links
- “Float Self-Tagging”, Melançon et al 2024
- “Breaking Bad: How Compilers Break Constant-Time”, Schneider et al 2024
- “It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages”, Kempen et al 2024
- “Glue and Coprocessor Architectures”, Buterin 2024
- “Amit’s A✱ Pages”, Patel 2024
- “Writing Commit Messages”, Tatham 2024
- “Polyamorous Scheduling”, Gąsieniec et al 2024
- “Beyond A✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024
- “Hamiltonicity of Expanders: Optimal Bounds and Applications”, Draganić et al 2024
- “Efficient Parallelization of an Ubiquitous Sequential Computation”, Heinsen 2023
- “Towards Automatic Design of Factorio Blueprints”, Patterson et al 2023
- “U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning”, Hsu & Serrão 2023
- “Blockwise Parallel Transformer for Long Context Large Models”, Liu & Abbeel 2023
- “You And Your Research”, Hamming 2023
- “Distinct Elements in Streams: An Algorithm for the (Text) Book”, Chakraborty et al 2023
- “Monolith: Real Time Recommendation System With Collisionless Embedding Table”, Liu et al 2022
- “A Library for Representing Python Programs As Graphs for Machine Learning”, Bieber et al 2022
- “TextWorldExpress: Simulating Text Games at One Million Steps Per Second”, Jansen & Côté 2022
- “Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Dalle et al 2022
- “Overwatch: Learning Patterns in Code Edit Sequences”, Zhang et al 2022
- “Heisenbugs: The Most Elusive Kind of Bug, and How to Capture Them With Perfect Replayability—Eliminate Heisenbugs and Endless Debugging Sessions!”, Ovadia 2022
- “Progress in Mathematical Programming Solvers 2001–2020”, Koch et al 2022
- “Searching for Cyclic TV Reference Paradoxes”, Pinheiro 2022
- “Fast Text Placement Scheme for ASCII Art Synthesis”, Chung & Kwon 2022
- “Monarch: Expressive Structured Matrices for Efficient and Accurate Training”, Dao et al 2022
- “Maximum Flow and Minimum-Cost Flow in Almost-Linear Time”, Chen et al 2022
- “Clock: 解説”
- “Bamboo Trimming Revisited: Simple Algorithms Can Do Well Too”, Kuszmaul 2022
- “What Goes into Making an OS to Be Unix Compliant Certified?”, Lambert 2022
- “Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow”, Tambon et al 2021
- “Improving Real-Time Rendering of Dynamic Digital Characters in Cycles”, Dietrich 2021
- “Real Time Cluster Path Tracing”, Xie et al 2021
- “Small-Amp: Test Amplification in a Dynamically Typed Language”, Abdi et al 2021
- “Introducing Triton: Open-Source GPU Programming for Neural Networks”, Tillet 2021
- “Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021
- “Hacker News Folk Wisdom on Visual Programming”, Keer 2021
- “Real-Time Neural Radiance Caching for Path Tracing”, Müller et al 2021
- “Randomness In Neural Network Training: Characterizing The Impact of Tooling”, Zhuang et al 2021
- “How Developers Choose Names”, Feitelson et al 2021
- “Pretrained Transformers As Universal Computation Engines”, Lu et al 2021
- “Entropy Trade-Offs in Artistic Design: A Case Study of Tamil kolam”, Tran et al 2021
- “Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros 2021
- “MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Trofin et al 2021
- “NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher 2021
- “I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem”, Fleder & Shah 2020
- “Presyn: Modeling Black-Box Components With Probabilistic Synthesis”, Collie et al 2020
- “Why Johnny Won’t Upgrade”, Mattheij 2020
- “Optimal Peanut Butter and Banana Sandwiches”, Rosenthal 2020
- “A Time Leap Challenge for SAT Solving”, Fichte et al 2020
- “Measuring Hardware Overhang”, hippke 2020
- “A Bayesian Approach to the Simulation Argument”, Kipping 2020
- “Algorithms With Predictions”, Mitzenmacher & Vassilvitskii 2020
- “BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits”, Tiwari et al 2020
- “Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing”, Dai et al 2020
- “Lessons Learned from Bugs in Models of Human History”, Ragsdale et al 2020
- “Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown 2020
- “Tech Notes: The Success and Failure of Ninja”
- “Bringing GNU Emacs to Native Code”, Corallo et al 2020
- “Learning-Based Memory Allocation for C++ Server Workloads”, Maas et al 2020
- “Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020
- “The History of the URL”, Bloom 2020
- “Quantifying Independently Reproducible Machine Learning”, Raff 2020
- “Solving Billion-Scale Knapsack Problems”, Zhang et al 2020
- “Taxonomy of Real Faults in Deep Learning Systems”, Humbatova et al 2019
- “They Might Never Tell You It’s Broken”, Chevalier-Boisvert 2019
- “Co-Dfns: A Data Parallel Compiler Hosted on the GPU”, Hsu 2019c
- “Hyrum’s Law: An Observation on Software Engineering”, Wright 2019
- “Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
- “Local-First Software: You Own Your Data, in spite of the Cloud [Paper]”, Kleppmann et al 2019
- “Gated Linear Networks”, Veness et al 2019
- “A Step Toward Quantifying Independently Reproducible Machine Learning Research”, Raff 2019
- “Different Languages, Similar Encoding Efficiency: Comparable Information Rates across the Human Communicative Niche”, Coupé et al 2019
- “A View on Deep Reinforcement Learning in System Optimization”, Haj-Ali et al 2019
- “Moral Permissibility of Action Plans”, Lindner et al 2019
- “ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data”, Stehle & Jacobsen 2019
- “Real-World Dynamic Programming: Seam Carving”, Das 2019
- “Local-First Software: You Own Your Data, in spite of the Cloud [Web]”, Kleppmann et al 2019
- “GAP: Generalizable Approximate Graph Partitioning Framework”, Nazi et al 2019
- “Parsing Gigabytes of JSON per Second”, Langdale & Lemire 2019
- “AutoPhase: Compiler Phase-Ordering for High Level Synthesis With Deep Reinforcement Learning”, Haj-Ali et al 2019
- “Reinventing the Wheel: Discovering the Optimal Rolling Shape With PyTorch”, Wiener 2019
- “Meta-Learning Neural Bloom Filters”, Rae 2019
- “SageDB: A Learned Database System”, Kraska 2019
- “Test Driving ‘Power of Two Random Choices’ Load Balancing”, Tarreau 2019
- “Slow Software”, McGranaghan 2018
- “Learning to Perform Local Rewriting for Combinatorial Optimization”, Chen & Tian 2018
- “How to Shuffle a Big Dataset”, Hardin 2018
- “Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”, Nagarajan et al 2018
- “Learning to Optimize Join Queries With Deep Reinforcement Learning”, Krishnan et al 2018
- “Always Measure One Level Deeper: Performance Measurements Often Go Wrong, Reporting Surface-Level Results That Are More Marketing Than Science”, Ousterhout 2018
- “Learning to Optimize Tensor Programs”, Chen et al 2018
- “Optimizing Query Evaluations Using Reinforcement Learning for Web Search”, Rosset et al 2018
- “Learning Memory Access Patterns”, Hashemi et al 2018
- “Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions”, Vasilache et al 2018
- “Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
- “The Case for Learned Index Structures”, Kraska et al 2017
- “Automatic Differentiation in PyTorch”, Paszke et al 2017
- “From Punched Cards to Flat Screens: A Technical Autobiography”, Hazel 2017
- “DAG Reduction: Fast Answering Reachability Queries”, Zhou et al 2017
- “Stochastic Constraint Programming As Reinforcement Learning”, Prestwich et al 2017
- “Learning to Superoptimize Programs”, Bunel et al 2017
- “Web Bloat”, Luu 2017
- “Resource-Efficient Machine Learning in 2 KB RAM for the Internet of Things”, Kumar et al 2017
- “Machine Learning for Systems and Systems for Machine Learning”, Dean 2017
- “P≟NP § AI”, Aaronson 2017 (page 5)
- “Neural Combinatorial Optimization With Reinforcement Learning”, Bello et al 2016
- “The Doodle Theorem, and Beyond: Colin Wright Juggles Euler, Doodling and Millennium Problems”, Wright 2016
- “Coz: Finding Parallel Code That Counts With Causal Profiling”, Curtsinger & Berger 2016
- “A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents”, Aziz & Mackenzie 2016
- “Why WhatsApp Only Needs 50 Engineers for Its 900M Users: One of the (many) Intriguing Parts of the WhatsApp Story Is That It Has Achieved Such Enormous Scale With Such a Tiny Team”, Metz 2015
- “Scalability! But at What COST?”, McSherry et al 2015
- “Inferring Algorithmic Patterns With Stack-Augmented Recurrent Nets”, Holdings et al 2015
- “The Ph.D. Grind: A Ph.D. Student Memoir”, Guo 2015
- “The Misfortunes of a Trio of Mathematicians Using Computer Algebra Systems—Can We Trust in Them?”, Durán et al 2014
- “Always Bet on Text”, graydon2 2014
- “The Mystery Machine: End-To-End Performance Analysis of Large-Scale Internet Services”, Chow et al 2014 (page 2)
- “Core-Guided MaxSAT With Soft Cardinality Constraints”, Morgado et al 2014
- “Algorithmic Progress in Six Domains”, Grace 2013
- “Intelligence Explosion Microeconomics”, Yudkowsky 2013
- “Homotopy Groups of Suspended Classifying Spaces: An Experimental Approach”, Romero & Rubio 2013
- “Bounded Kolmogorov Complexity Based on Cognitive Models”, Strannegård et al 2013
- “Really Automatic Scalable Object-Oriented Reengineering”, Trudel et al 2013
- “Planning As Satisfiability: Heuristics”, Rintanen 2012
- “The Algebraic Combinatorial Approach for Low-Rank Matrix Completion”, Király et al 2012
- “Evaluating the Design of the R Language: Objects and Functions for Data Analysis”, Morandat et al 2012
- “The International SAT Solver Competitions”, Järvisalo et al 2012
- “Uniform Random Generation of Large Acyclic Digraphs”, Kuipers & Moffa 2012
- “National Cryptologic Museum Opens New Exhibit on Dr. John Nash”, NSA 2012
- “How Trello Is Different”, Spolsky 2012
- “A Brief History of NP-Completeness, 1954–2012”, Johnson 2012
- “Cutting the Pipe: Achieving Sub-Second Iteration Times”, 5.1.1 2012
- “STEPS Toward Expressive Programming Systems: "A Science Experiment"”, Ohshima et al 2012 (page 2)
- “Why Philosophers Should Care About Computational Complexity”, Aaronson 2011
- “You’re Doing It Wrong: Think You’ve Mastered the Art of Server Performance? Think Again.”, Kamp 2010
- “Formal Theory of Creativity & Fun & Intrinsic Motivation (1990–2010)”, Schmidhuber 2010
- “Coolex: The Coolest Way to Generate Combinations”, Ruskey & Williams 2009
- “De-Anonymizing Social Networks”, Narayanan & Shmatikov 2009
- “The Gödel Letter”, Gödel 2009
- “Producing Wrong Data Without Doing Anything Obviously Wrong!”, Mytkowicz et al 2009
- “The Tactical Amulet Extraction Bot: Predicting and Controlling NetHack's Randomness”
- “Is There A Fourth Futamura Projection?”, Robert 2009
- “Dual-Pivot Quicksort Algorithm”, Yaroslavskiy 2009
- “Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes”, Schmidhuber 2008
- “Aggregating Inconsistent Information: Ranking and Clustering”, Ailon et al 2008
- “Bigtable: A Distributed Storage System for Structured Data”, Chang et al 2008
- “Interview With Donald Knuth”, Binstock 2008
- “Optimal Boarding Method for Airline Passengers”, Steffen 2008
- “Harnessing Vision for Computation”, Changizi 2008
- “Communication in Economic Mechanisms”, Segal 2006b
- “How To Break Anonymity of the Netflix Prize Dataset”, Narayanan & Shmatikov 2006
- “Global Multiple Objective Line Breaking”, Holkner 2006
- “Oral History of Butler Lampson § WWW”, Lampson & Kay 2006 (page 36)
- “History of Combinatorial Generation (The Art of Computer Programming: Volume 4: Pre-Fascicle 4B: §7.2.1.7) § Pg22”, Knuth 2005 (page 22)
- “Modeling Bursts and Heavy Tails in Human Dynamics”, Vazquez et al 2005
- “NP-Complete Problems and Physical Reality”, Aaronson 2005
- “Lower-Stretch Spanning Trees”, Elkin et al 2004
- “A Short History of Computational Complexity”, Fortnow et al 2003
- “Least Effort and the Origins of Scaling in Human Language”, Cancho & Sole 2003
- “Extended Comment on Language Trees and Zipping”, Goodman 2002
- “Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby 2002
- “A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances”, Hyyrö 2002
- “Naked Objects: a Technique for Designing More Expressive Systems”, Pawson & Matthews 2001
- “On Proebsting’s Law”, Scott 2001
- “Peopleware: Why Measure Performance”, DeMarco & Lister 2001
- “The Effects of Moore’s Law and Slacking on Large Computations”, Gottbrath et al 1999
- “Bridging the Algorithm Gap: A Linear-Time Functional Program for Paragraph Formatting”, Moor & Gibbons 1999
- “Feynman’s War: Modeling Weapons, Modeling Nature”, Galison 1998
- “The Concave Least-Weight Subsequence Problem Revisited”, Wilber 1998
- “Applications of Randomness in System Performance Measurement”, Blackwell 1998
- “The PageRank Citation Ranking: Bringing Order to the Web”, Page et al 1998
- “Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”, Proebsting 1998
- “The T-Experiments: Errors in Scientific Software”, Hatton 1997
- “George Prices’s Contributions to Evolutionary Genetics”, Frank 1995
- “The Nature of Selection”, Price 1995
- “Digital Filters, Part II”, Hamming 1995
- “A Plea for Lean Software”, Wirth 1995
- “Building a Large Annotated Corpus of English: The Penn Treebank”, Marcus et al 1993
- “On the Computational Complexity of the Jones and Tutte Polynomials”, Jaeger et al 1990
- “A Linear-Time Algorithm for Concave One-Dimensional Dynamic Programming”, Galil & Park 1990
- “Planning and Learning in Permutation Groups”, Mose et al 1989
- “Separating Strings With Small Automata”, Robson 1989
- “Optimal Nonlinear Approximation”, DeVore et al 1989
- “Hypertext and the Oxford English Dictionary”, Raymond & Tompa 1988
- Three Scientists and Their Gods: Looking for Meaning in an Age of Information, Wright 1988
- “Incentive Engineering: for Computational Resource Management”, Drexler & Miller 1988
- “The Least Weight Subsequence Problem”, Hirschberg & Larmore 1987
- “Geometric Applications of a Matrix Searching Algorithm”, Aggarwal et al 1986
- “The Back of the Envelope Returns”, Bentley 1986
- “Average Case Complete Problems”, Levin 1986
- “Probabilistic Counting Algorithms for Data Base Applications”, Flajolet & Martin 1985
- “Programming As Theory Building”, Naur 1985
- “The Back of the Envelope”, Bentley 1984
- “The Competitive Allocation Process Is Informationally Efficient Uniquely”, Jordan 1982
- “Epigrams on Programming”, Perlis 1982
- “Procedural Reflection in Programming Languages”, Smith 1982
- “On Holy Wars and a Plea for Peace”, Cohen 1981
- “Mutation Analysis Of Program Test Data”, Budd 1980
- “A Correct Preprocessing Algorithm for Boyer-Moore String-Searching”, Rytter 1980
- “Algorithms for Loop Matchings”, Nussinov et al 1978
- “Fast Pattern Matching in Strings”, Knuth et al 1977
-
“Structured Programming With
go ToStatements”, Knuth 1974 - Interstellar Communication: Scientific Perspectives, Ponnamperuma & Cameron 1974
- “A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations”, Kogge & Stone 1973
- “Universal Sequential Search Problems”, Levin 1973
- “The Dangers of Computer-Science Theory”, Knuth 1973
- “The Humble Programmer [EWD340]”, Dijkstra 1972
- “The Pattern of Streets”, Alexander 1966
- “A Simple Randomization Procedure”, Sandelius 1962
- “Generation of Random Permutations of Given Number of Elements Using Random Sampling Numbers”, Rao 1961
- “Addressing for Random-Access Storage”, Peterson 1957
- “The Codeless Code: Case 96: ‘Stateless’”
- “Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
- “Accidentally Quadratic”
-
“Summing ASCII Encoded Integers on Haswell at Almost the Speed of
memcpy” - “We Need Visual Programming. No, Not like That.”
- “Visualizing Algorithms”
- “The Final Cut [Ford-Fulkerson’s Max-Flow Min-Cut As Planning Paradigm]”
- “Personality Value”
- “The Untold Story of SQLite With Richard Hipp”
- “Measurement, Benchmarking, and Data Analysis Are Underrated”
- “Dynamicland”, Victor 2024
- “Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
- “Differentiable Finite State Machines”
- “Getting the World Record in HATETRIS”
- “LISP With GC in 436 Bytes”
- “Heuristics on the High Seas: Mathematical Optimization for Cargo Ships”
- “Programming’s Dirtiest Little Secret”, Yegg 2024
-
“Submission #6347: Chef Stef’s NES Arkanoid
warplessin 11:11.18” - “Differentiable Programming from Scratch”
- “Technical Dimensions of Programming Systems”
- “How Much of a Genius-Level Move Was Using Binary Space Partitioning in Doom?”
- “An Open Letter to Netflix from the Authors of the De-Anonymization Paper”
- “Scaling Our Spreadsheet Engine from Thousands to Billions of Cells”
- “The Art and Mathematics of Genji-Kō”
- “TSP Art”
- Wikipedia
- Miscellaneous
- Bibliography
See Also
Gwern
“The Scaling Hypothesis”, Gwern 2020
“Computer Optimization: Your Computer Is Faster Than You Think”, Gwern 2021
Computer Optimization: Your Computer Is Faster Than You Think
Links
“Float Self-Tagging”, Melançon et al 2024
“Breaking Bad: How Compilers Break Constant-Time”, Schneider et al 2024
“It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages”, Kempen et al 2024
It’s Not Easy Being Green: On the Energy Efficiency of Programming Languages
“Glue and Coprocessor Architectures”, Buterin 2024
“Amit’s A✱ Pages”, Patel 2024
“Writing Commit Messages”, Tatham 2024
“Polyamorous Scheduling”, Gąsieniec et al 2024
“Beyond A✱: Better Planning With Transformers via Search Dynamics Bootstrapping (Searchformer)”, Lehnert et al 2024
Beyond A✱: Better Planning with Transformers via Search Dynamics Bootstrapping (Searchformer)
“Hamiltonicity of Expanders: Optimal Bounds and Applications”, Draganić et al 2024
“Efficient Parallelization of an Ubiquitous Sequential Computation”, Heinsen 2023
Efficient Parallelization of an Ubiquitous Sequential Computation
“Towards Automatic Design of Factorio Blueprints”, Patterson et al 2023
“U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning”, Hsu & Serrão 2023
U-Net CNN in APL: Exploring Zero-Framework, Zero-Library Machine Learning
“Blockwise Parallel Transformer for Long Context Large Models”, Liu & Abbeel 2023
Blockwise Parallel Transformer for Long Context Large Models
“You And Your Research”, Hamming 2023
“Distinct Elements in Streams: An Algorithm for the (Text) Book”, Chakraborty et al 2023
Distinct Elements in Streams: An Algorithm for the (Text) Book
“Monolith: Real Time Recommendation System With Collisionless Embedding Table”, Liu et al 2022
Monolith: Real Time Recommendation System With Collisionless Embedding Table
“A Library for Representing Python Programs As Graphs for Machine Learning”, Bieber et al 2022
A Library for Representing Python Programs as Graphs for Machine Learning
“TextWorldExpress: Simulating Text Games at One Million Steps Per Second”, Jansen & Côté 2022
TextWorldExpress: Simulating Text Games at One Million Steps Per Second
“Learning With Combinatorial Optimization Layers: a Probabilistic Approach”, Dalle et al 2022
Learning with Combinatorial Optimization Layers: a Probabilistic Approach
“Overwatch: Learning Patterns in Code Edit Sequences”, Zhang et al 2022
“Heisenbugs: The Most Elusive Kind of Bug, and How to Capture Them With Perfect Replayability—Eliminate Heisenbugs and Endless Debugging Sessions!”, Ovadia 2022
View External Link:
“Progress in Mathematical Programming Solvers 2001–2020”, Koch et al 2022
“Searching for Cyclic TV Reference Paradoxes”, Pinheiro 2022
“Fast Text Placement Scheme for ASCII Art Synthesis”, Chung & Kwon 2022
“Monarch: Expressive Structured Matrices for Efficient and Accurate Training”, Dao et al 2022
Monarch: Expressive Structured Matrices for Efficient and Accurate Training
“Maximum Flow and Minimum-Cost Flow in Almost-Linear Time”, Chen et al 2022
“Clock: 解説”
“Bamboo Trimming Revisited: Simple Algorithms Can Do Well Too”, Kuszmaul 2022
Bamboo Trimming Revisited: Simple Algorithms Can Do Well Too
“What Goes into Making an OS to Be Unix Compliant Certified?”, Lambert 2022
“Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow”, Tambon et al 2021
Silent Bugs in Deep Learning Frameworks: An Empirical Study of Keras and TensorFlow
“Improving Real-Time Rendering of Dynamic Digital Characters in Cycles”, Dietrich 2021
Improving Real-time Rendering of Dynamic Digital Characters in Cycles
“Real Time Cluster Path Tracing”, Xie et al 2021
“Small-Amp: Test Amplification in a Dynamically Typed Language”, Abdi et al 2021
Small-Amp: Test Amplification in a Dynamically Typed Language
“Introducing Triton: Open-Source GPU Programming for Neural Networks”, Tillet 2021
Introducing Triton: Open-source GPU programming for neural networks
“Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs”, Sonnerat et al 2021
Learning a Large Neighborhood Search Algorithm for Mixed Integer Programs
“Hacker News Folk Wisdom on Visual Programming”, Keer 2021
“Real-Time Neural Radiance Caching for Path Tracing”, Müller et al 2021
“Randomness In Neural Network Training: Characterizing The Impact of Tooling”, Zhuang et al 2021
Randomness In Neural Network Training: Characterizing The Impact of Tooling
“How Developers Choose Names”, Feitelson et al 2021
“Pretrained Transformers As Universal Computation Engines”, Lu et al 2021
“Entropy Trade-Offs in Artistic Design: A Case Study of Tamil kolam”, Tran et al 2021
Entropy trade-offs in artistic design: A case study of Tamil kolam
“Investment vs. Reward in a Competitive Knapsack Problem”, Neumann & Gros 2021
“MLGO: a Machine Learning Guided Compiler Optimizations Framework”, Trofin et al 2021
MLGO: a Machine Learning Guided Compiler Optimizations Framework
“NNUE: The Neural Network of the Stockfish Chess Engine”, Goucher 2021
“I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem”, Fleder & Shah 2020
I Know What You Bought At Chipotle for $9.81 by Solving A Linear Inverse Problem
“Presyn: Modeling Black-Box Components With Probabilistic Synthesis”, Collie et al 2020
Presyn: Modeling Black-Box Components with Probabilistic Synthesis
“Why Johnny Won’t Upgrade”, Mattheij 2020
“Optimal Peanut Butter and Banana Sandwiches”, Rosenthal 2020
“A Time Leap Challenge for SAT Solving”, Fichte et al 2020
“Measuring Hardware Overhang”, hippke 2020
“A Bayesian Approach to the Simulation Argument”, Kipping 2020
“Algorithms With Predictions”, Mitzenmacher & Vassilvitskii 2020
“BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits”, Tiwari et al 2020
BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits
“Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing”, Dai et al 2020
Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing
“Lessons Learned from Bugs in Models of Human History”, Ragsdale et al 2020
“Measuring the Algorithmic Efficiency of Neural Networks”, Hernandez & Brown 2020
“Tech Notes: The Success and Failure of Ninja”
“Bringing GNU Emacs to Native Code”, Corallo et al 2020
“Learning-Based Memory Allocation for C++ Server Workloads”, Maas et al 2020
“Reinforcement Learning for Combinatorial Optimization: A Survey”, Mazyavkina et al 2020
Reinforcement Learning for Combinatorial Optimization: A Survey
“The History of the URL”, Bloom 2020
“Quantifying Independently Reproducible Machine Learning”, Raff 2020
“Solving Billion-Scale Knapsack Problems”, Zhang et al 2020
“Taxonomy of Real Faults in Deep Learning Systems”, Humbatova et al 2019
“They Might Never Tell You It’s Broken”, Chevalier-Boisvert 2019
“Co-Dfns: A Data Parallel Compiler Hosted on the GPU”, Hsu 2019c
“Hyrum’s Law: An Observation on Software Engineering”, Wright 2019
“Grandmaster Level in StarCraft II Using Multi-Agent Reinforcement Learning”, Vinyals et al 2019
Grandmaster level in StarCraft II using multi-agent reinforcement learning
“Local-First Software: You Own Your Data, in spite of the Cloud [Paper]”, Kleppmann et al 2019
Local-First Software: You Own Your Data, in spite of the Cloud [paper]
“Gated Linear Networks”, Veness et al 2019
“A Step Toward Quantifying Independently Reproducible Machine Learning Research”, Raff 2019
A Step Toward Quantifying Independently Reproducible Machine Learning Research
“Different Languages, Similar Encoding Efficiency: Comparable Information Rates across the Human Communicative Niche”, Coupé et al 2019
“A View on Deep Reinforcement Learning in System Optimization”, Haj-Ali et al 2019
A View on Deep Reinforcement Learning in System Optimization
“Moral Permissibility of Action Plans”, Lindner et al 2019
“ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data”, Stehle & Jacobsen 2019
ParPaRaw: Massively Parallel Parsing of Delimiter-Separated Raw Data
“Real-World Dynamic Programming: Seam Carving”, Das 2019
“Local-First Software: You Own Your Data, in spite of the Cloud [Web]”, Kleppmann et al 2019
Local-first software: You own your data, in spite of the cloud [web]
“GAP: Generalizable Approximate Graph Partitioning Framework”, Nazi et al 2019
“Parsing Gigabytes of JSON per Second”, Langdale & Lemire 2019
“AutoPhase: Compiler Phase-Ordering for High Level Synthesis With Deep Reinforcement Learning”, Haj-Ali et al 2019
AutoPhase: Compiler Phase-Ordering for High Level Synthesis with Deep Reinforcement Learning
“Reinventing the Wheel: Discovering the Optimal Rolling Shape With PyTorch”, Wiener 2019
Reinventing the Wheel: Discovering the Optimal Rolling Shape with PyTorch
“Meta-Learning Neural Bloom Filters”, Rae 2019
“SageDB: A Learned Database System”, Kraska 2019
“Test Driving ‘Power of Two Random Choices’ Load Balancing”, Tarreau 2019
“Slow Software”, McGranaghan 2018
“Learning to Perform Local Rewriting for Combinatorial Optimization”, Chen & Tian 2018
Learning to Perform Local Rewriting for Combinatorial Optimization
“How to Shuffle a Big Dataset”, Hardin 2018
“Deterministic Implementations for Reproducibility in Deep Reinforcement Learning”, Nagarajan et al 2018
Deterministic Implementations for Reproducibility in Deep Reinforcement Learning
“Learning to Optimize Join Queries With Deep Reinforcement Learning”, Krishnan et al 2018
Learning to Optimize Join Queries With Deep Reinforcement Learning
“Always Measure One Level Deeper: Performance Measurements Often Go Wrong, Reporting Surface-Level Results That Are More Marketing Than Science”, Ousterhout 2018
View External Link:
https://cacm.acm.org/research/always-measure-one-level-deeper/
“Learning to Optimize Tensor Programs”, Chen et al 2018
“Optimizing Query Evaluations Using Reinforcement Learning for Web Search”, Rosset et al 2018
Optimizing Query Evaluations using Reinforcement Learning for Web Search
“Learning Memory Access Patterns”, Hashemi et al 2018
“Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions”, Vasilache et al 2018
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
“Innovation and Cumulative Culture through Tweaks and Leaps in Online Programming Contests”, Miu et al 2018
Innovation and cumulative culture through tweaks and leaps in online programming contests
“The Case for Learned Index Structures”, Kraska et al 2017
“Automatic Differentiation in PyTorch”, Paszke et al 2017
“From Punched Cards to Flat Screens: A Technical Autobiography”, Hazel 2017
From Punched Cards to Flat Screens: A Technical Autobiography:
View PDF:
“DAG Reduction: Fast Answering Reachability Queries”, Zhou et al 2017
“Stochastic Constraint Programming As Reinforcement Learning”, Prestwich et al 2017
“Learning to Superoptimize Programs”, Bunel et al 2017
“Web Bloat”, Luu 2017
“Resource-Efficient Machine Learning in 2 KB RAM for the Internet of Things”, Kumar et al 2017
Resource-Efficient Machine Learning in 2 KB RAM for the Internet of Things
“Machine Learning for Systems and Systems for Machine Learning”, Dean 2017
Machine Learning for Systems and Systems for Machine Learning
“P≟NP § AI”, Aaronson 2017 (page 5)
“Neural Combinatorial Optimization With Reinforcement Learning”, Bello et al 2016
Neural Combinatorial Optimization with Reinforcement Learning
“The Doodle Theorem, and Beyond: Colin Wright Juggles Euler, Doodling and Millennium Problems”, Wright 2016
The doodle theorem, and beyond: Colin Wright juggles Euler, doodling and Millennium problems
“Coz: Finding Parallel Code That Counts With Causal Profiling”, Curtsinger & Berger 2016
Coz: Finding Parallel Code that Counts with Causal Profiling
“A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents”, Aziz & Mackenzie 2016
A Discrete and Bounded Envy-Free Cake Cutting Protocol for Any Number of Agents
“Why WhatsApp Only Needs 50 Engineers for Its 900M Users: One of the (many) Intriguing Parts of the WhatsApp Story Is That It Has Achieved Such Enormous Scale With Such a Tiny Team”, Metz 2015
“Scalability! But at What COST?”, McSherry et al 2015
“Inferring Algorithmic Patterns With Stack-Augmented Recurrent Nets”, Holdings et al 2015
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets
“The Ph.D. Grind: A Ph.D. Student Memoir”, Guo 2015
“The Misfortunes of a Trio of Mathematicians Using Computer Algebra Systems—Can We Trust in Them?”, Durán et al 2014
The Misfortunes of a Trio of Mathematicians Using Computer Algebra Systems—Can We Trust in Them?
“Always Bet on Text”, graydon2 2014
“The Mystery Machine: End-To-End Performance Analysis of Large-Scale Internet Services”, Chow et al 2014 (page 2)
The Mystery Machine: End-to-end performance analysis of large-scale Internet services
“Core-Guided MaxSAT With Soft Cardinality Constraints”, Morgado et al 2014
“Algorithmic Progress in Six Domains”, Grace 2013
“Intelligence Explosion Microeconomics”, Yudkowsky 2013
“Homotopy Groups of Suspended Classifying Spaces: An Experimental Approach”, Romero & Rubio 2013
Homotopy groups of suspended classifying spaces: An experimental approach
“Bounded Kolmogorov Complexity Based on Cognitive Models”, Strannegård et al 2013
“Really Automatic Scalable Object-Oriented Reengineering”, Trudel et al 2013
“Planning As Satisfiability: Heuristics”, Rintanen 2012
“The Algebraic Combinatorial Approach for Low-Rank Matrix Completion”, Király et al 2012
The Algebraic Combinatorial Approach for Low-Rank Matrix Completion
“Evaluating the Design of the R Language: Objects and Functions for Data Analysis”, Morandat et al 2012
Evaluating the Design of the R Language: Objects and Functions for Data Analysis
“The International SAT Solver Competitions”, Järvisalo et al 2012
“Uniform Random Generation of Large Acyclic Digraphs”, Kuipers & Moffa 2012
“National Cryptologic Museum Opens New Exhibit on Dr. John Nash”, NSA 2012
National Cryptologic Museum Opens New Exhibit on Dr. John Nash
“How Trello Is Different”, Spolsky 2012
“A Brief History of NP-Completeness, 1954–2012”, Johnson 2012
“Cutting the Pipe: Achieving Sub-Second Iteration Times”, 5.1.1 2012
Cutting the Pipe: Achieving Sub-Second Iteration Times:
View PDF:
“STEPS Toward Expressive Programming Systems: "A Science Experiment"”, Ohshima et al 2012 (page 2)
STEPS Toward Expressive Programming Systems: "A Science Experiment"
“Why Philosophers Should Care About Computational Complexity”, Aaronson 2011
“You’re Doing It Wrong: Think You’ve Mastered the Art of Server Performance? Think Again.”, Kamp 2010
You’re Doing It Wrong: Think you’ve mastered the art of server performance? Think again.
“Formal Theory of Creativity & Fun & Intrinsic Motivation (1990–2010)”, Schmidhuber 2010
Formal Theory of Creativity & Fun & Intrinsic Motivation (1990–2010)
“Coolex: The Coolest Way to Generate Combinations”, Ruskey & Williams 2009
“De-Anonymizing Social Networks”, Narayanan & Shmatikov 2009
“The Gödel Letter”, Gödel 2009
“Producing Wrong Data Without Doing Anything Obviously Wrong!”, Mytkowicz et al 2009
Producing Wrong Data Without Doing Anything Obviously Wrong!
“The Tactical Amulet Extraction Bot: Predicting and Controlling NetHack's Randomness”
The Tactical Amulet Extraction Bot: Predicting and controlling NetHack's randomness
“Is There A Fourth Futamura Projection?”, Robert 2009
Is There A Fourth Futamura Projection?:
View PDF:
“Dual-Pivot Quicksort Algorithm”, Yaroslavskiy 2009
“Driven by Compression Progress: A Simple Principle Explains Essential Aspects of Subjective Beauty, Novelty, Surprise, Interestingness, Attention, Curiosity, Creativity, Art, Science, Music, Jokes”, Schmidhuber 2008
“Aggregating Inconsistent Information: Ranking and Clustering”, Ailon et al 2008
Aggregating inconsistent information: Ranking and clustering
“Bigtable: A Distributed Storage System for Structured Data”, Chang et al 2008
“Interview With Donald Knuth”, Binstock 2008
“Optimal Boarding Method for Airline Passengers”, Steffen 2008
“Harnessing Vision for Computation”, Changizi 2008
“Communication in Economic Mechanisms”, Segal 2006b
“How To Break Anonymity of the Netflix Prize Dataset”, Narayanan & Shmatikov 2006
“Global Multiple Objective Line Breaking”, Holkner 2006
“Oral History of Butler Lampson § WWW”, Lampson & Kay 2006 (page 36)
“History of Combinatorial Generation (The Art of Computer Programming: Volume 4: Pre-Fascicle 4B: §7.2.1.7) § Pg22”, Knuth 2005 (page 22)
“Modeling Bursts and Heavy Tails in Human Dynamics”, Vazquez et al 2005
“NP-Complete Problems and Physical Reality”, Aaronson 2005
“Lower-Stretch Spanning Trees”, Elkin et al 2004
“A Short History of Computational Complexity”, Fortnow et al 2003
A Short History of Computational Complexity:
View PDF:
“Least Effort and the Origins of Scaling in Human Language”, Cancho & Sole 2003
“Extended Comment on Language Trees and Zipping”, Goodman 2002
“Solving Real-World Linear Programs: A Decade and More of Progress”, Bixby 2002
Solving Real-World Linear Programs: A Decade and More of Progress
“A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances”, Hyyrö 2002
A Bit-Vector Algorithm for Computing Levenshtein and Damerau Edit Distances
“Naked Objects: a Technique for Designing More Expressive Systems”, Pawson & Matthews 2001
Naked objects: a technique for designing more expressive systems
“On Proebsting’s Law”, Scott 2001
“Peopleware: Why Measure Performance”, DeMarco & Lister 2001
“The Effects of Moore’s Law and Slacking on Large Computations”, Gottbrath et al 1999
The Effects of Moore’s Law and Slacking on Large Computations
“Bridging the Algorithm Gap: A Linear-Time Functional Program for Paragraph Formatting”, Moor & Gibbons 1999
Bridging the algorithm gap: A linear-time functional program for paragraph formatting
“Feynman’s War: Modeling Weapons, Modeling Nature”, Galison 1998
“The Concave Least-Weight Subsequence Problem Revisited”, Wilber 1998
“Applications of Randomness in System Performance Measurement”, Blackwell 1998
Applications of Randomness in System Performance Measurement
“The PageRank Citation Ranking: Bringing Order to the Web”, Page et al 1998
“Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years”, Proebsting 1998
Proebsting’s Law: Compiler Advances Double Computing Power Every 18 Years
“The T-Experiments: Errors in Scientific Software”, Hatton 1997
“George Prices’s Contributions to Evolutionary Genetics”, Frank 1995
“The Nature of Selection”, Price 1995
“Digital Filters, Part II”, Hamming 1995
“A Plea for Lean Software”, Wirth 1995
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“Building a Large Annotated Corpus of English: The Penn Treebank”, Marcus et al 1993
Building a Large Annotated Corpus of English: The Penn Treebank
“On the Computational Complexity of the Jones and Tutte Polynomials”, Jaeger et al 1990
On the computational complexity of the Jones and Tutte polynomials
“A Linear-Time Algorithm for Concave One-Dimensional Dynamic Programming”, Galil & Park 1990
A linear-time algorithm for concave one-dimensional dynamic programming:
View PDF:
“Planning and Learning in Permutation Groups”, Mose et al 1989
“Separating Strings With Small Automata”, Robson 1989
Separating strings with small automata:
View PDF:
“Optimal Nonlinear Approximation”, DeVore et al 1989
“Hypertext and the Oxford English Dictionary”, Raymond & Tompa 1988
Three Scientists and Their Gods: Looking for Meaning in an Age of Information, Wright 1988
Three Scientists and Their Gods: Looking for Meaning in an Age of Information
“Incentive Engineering: for Computational Resource Management”, Drexler & Miller 1988
Incentive Engineering: for Computational Resource Management
“The Least Weight Subsequence Problem”, Hirschberg & Larmore 1987
“Geometric Applications of a Matrix Searching Algorithm”, Aggarwal et al 1986
Geometric applications of a matrix searching algorithm:
View PDF:
“The Back of the Envelope Returns”, Bentley 1986
The back of the envelope returns:
View PDF:
“Average Case Complete Problems”, Levin 1986
“Probabilistic Counting Algorithms for Data Base Applications”, Flajolet & Martin 1985
Probabilistic counting algorithms for data base applications
“Programming As Theory Building”, Naur 1985
“The Back of the Envelope”, Bentley 1984
View PDF:
“The Competitive Allocation Process Is Informationally Efficient Uniquely”, Jordan 1982
The competitive allocation process is informationally efficient uniquely
“Epigrams on Programming”, Perlis 1982
“Procedural Reflection in Programming Languages”, Smith 1982
“On Holy Wars and a Plea for Peace”, Cohen 1981
“Mutation Analysis Of Program Test Data”, Budd 1980
“A Correct Preprocessing Algorithm for Boyer-Moore String-Searching”, Rytter 1980
A Correct Preprocessing Algorithm for Boyer-Moore String-Searching
“Algorithms for Loop Matchings”, Nussinov et al 1978
“Fast Pattern Matching in Strings”, Knuth et al 1977
“Structured Programming With go To Statements”, Knuth 1974
Interstellar Communication: Scientific Perspectives, Ponnamperuma & Cameron 1974
“A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations”, Kogge & Stone 1973
A Parallel Algorithm for the Efficient Solution of a General Class of Recurrence Equations
“Universal Sequential Search Problems”, Levin 1973
“The Dangers of Computer-Science Theory”, Knuth 1973
“The Humble Programmer [EWD340]”, Dijkstra 1972
“The Pattern of Streets”, Alexander 1966
“A Simple Randomization Procedure”, Sandelius 1962
“Generation of Random Permutations of Given Number of Elements Using Random Sampling Numbers”, Rao 1961
Generation of Random Permutations of Given Number of Elements Using Random Sampling Numbers
“Addressing for Random-Access Storage”, Peterson 1957
“The Codeless Code: Case 96: ‘Stateless’”
“Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
“Accidentally Quadratic”
“Summing ASCII Encoded Integers on Haswell at Almost the Speed of memcpy”
Summing ASCII encoded integers on Haswell at almost the speed of memcpy:
“We Need Visual Programming. No, Not like That.”
“Visualizing Algorithms”
“The Final Cut [Ford-Fulkerson’s Max-Flow Min-Cut As Planning Paradigm]”
The Final Cut [Ford-Fulkerson’s max-flow min-cut as planning paradigm]
“Personality Value”
“The Untold Story of SQLite With Richard Hipp”
“Measurement, Benchmarking, and Data Analysis Are Underrated”
Measurement, benchmarking, and data analysis are underrated:
View External Link:
“Dynamicland”, Victor 2024
“Programmed Differently? Testing for Gender Differences in Python Programming Style and Quality on GitHub”
“Differentiable Finite State Machines”
“Getting the World Record in HATETRIS”
“LISP With GC in 436 Bytes”
“Heuristics on the High Seas: Mathematical Optimization for Cargo Ships”
Heuristics on the high seas: Mathematical optimization for cargo ships:
“Programming’s Dirtiest Little Secret”, Yegg 2024
“Submission #6347: Chef Stef’s NES Arkanoid warpless in 11:11.18”
Submission #6347: Chef Stef’s NES Arkanoid warpless in 11:11.18
“Differentiable Programming from Scratch”
“Technical Dimensions of Programming Systems”
“How Much of a Genius-Level Move Was Using Binary Space Partitioning in Doom?”
How Much of a Genius-Level Move Was Using Binary Space Partitioning in Doom?
“An Open Letter to Netflix from the Authors of the De-Anonymization Paper”
An open letter to Netflix from the authors of the de-anonymization paper:
“Scaling Our Spreadsheet Engine from Thousands to Billions of Cells”
Scaling our Spreadsheet Engine from Thousands to Billions of Cells:
“The Art and Mathematics of Genji-Kō”
“TSP Art”
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Miscellaneous
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