Fetching the paper…
Reading the bibliography…
With the accumulation of data at an unprecedented rate, its potential to fuel scientific discovery is growing exponentially.
Multiple comparisons among means
Dunn, O. J · 1961
Earlier work this paper cites.
Computer science as empirical inquiry: symbols and search
Newell, A. and Simon, H. A · 1976
Earlier work this paper cites.
Bacon: A production system that discovers empirical laws
Langley, P · 1977
Earlier work this paper cites.
Optimal search for the best alternative
Weitzman, M · 1978
Earlier work this paper cites.
Data-driven discovery of physical laws
Langley, P · 1981
Earlier work this paper cites.
Social background, academic resources, and college graduation: Recent evidence from the national longitudinal survey
Alexander, K. L., Riordan, C., Fennessey, J., and Pallas, A. M · 1982
Earlier work this paper cites.
Rediscovering chemistry with the bacon system
Langley, P., Bradshaw, G. L., and Simon, H. A · 1983
Earlier work this paper cites.
The search for regularity: Four aspects of scientific discovery
Langley, P., Zytkow, J. M., Simon, H. A., and Bradshaw, G. L · 1984
Earlier work this paper cites.
Undiscovered public knowledge
Swanson, D. R · 1986
Earlier work this paper cites.
Fundamental principles of deception in genetic search
Whitley, L. D · 1991
Earlier work this paper cites.
Overview of issues in the longitudinal analysis of respiratory data
Weiss, S. T. and Ware, J. H · 1996
Earlier work this paper cites.
Data mining: From serendipity to science
Ramakrishnan, N. and Grama, A. Y · 1999
Earlier work this paper cites.
Using ai to accelerate scientific discovery, 2002
Hassabis, D · 2002
Earlier work this paper cites.
Serendipity and information seeking: an empirical study
Foster, A. and Ford, N · 2003
Earlier work this paper cites.
An ontology-driven framework for data transformation in scientific workflows
Bowers, S. and Ludäscher, B · 2004
Earlier work this paper cites.
Are time preference and body mass index associated?: Evidence from the national longitudinal survey of youth
Smith, P. K., Bogin, B., and Bishai, D · 2005
Earlier work this paper cites.
Conceptual biology, hypothesis discovery, and text mining: Swanson’s legacy
Bekhuis, T · 2006
Earlier work this paper cites.
Intrinsic motivation systems for autonomous mental development
Oudeyer, P.-Y., Kaplan, F., and Hafner, V. V · 2007
Earlier work this paper cites.
The Black Swan: The Impact of the Highly Improbable
Taleb, N. N · 2007
Earlier work this paper cites.
The end of theory: The data deluge makes the scientific method obsolete
Anderson, C · 2008
Earlier work this paper cites.
How can we define intrinsic motivation?
Oudeyer, P.-Y. and Kaplan, F · 2008
Earlier work this paper cites.
Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
Earlier work this paper cites.
Thinking, fast and slow
Kahneman, D · 2011
Earlier work this paper cites.
Entropy search for information-efficient global optimization
Hennig, P. and Schuler, C. J · 2012
Earlier work this paper cites.
Using semantic workflows to disseminate best practices and accelerate discoveries in multi-omic data analysis
Gil, Y., McWeeney, S. K., and Mason, C. E · 2013
Earlier work this paper cites.
Reproducibility project: Psychology, 2015
Collaboration, O. S · 2015
Earlier work this paper cites.
Efficient and robust automated machine learning
Feurer, M., Klein, A., Eggensperger, K., Springenberg, J., Blum, M., and Hutter, F · 2015
Earlier work this paper cites.
Semantics derived automatically from language corpora contain human-like biases
Caliskan, A., Bryson, J. J., and Narayanan, A · 2016
Earlier work this paper cites.
Conceptions of good science in our data-rich world
Elliott, K. C., Cheruvelil, K. S., Montgomery, G. M., and Soranno, P. A · 2016
Earlier work this paper cites.
Vime: Variational information maximizing exploration
Houthooft, R., Chen, X., Duan, Y., Schulman, J., De Turck, F., and Abbeel, P · 2016
Earlier work this paper cites.
Semantic web in data mining and knowledge discovery: A comprehensive survey
Ristoski, P. and Paulheim, H · 2016
Earlier work this paper cites.
Complex embeddings for simple link prediction
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., and Bouchard, G · 2016
Earlier work this paper cites.
The asa statement on p-values: Context, process, and purpose
Wasserstein, R. and Lazar, N. A · 2016
Earlier work this paper cites.
Race, wealth and incarceration: Results from the national longitudinal survey of youth
Zaw, K., Hamilton, D., and Darity, W. A. J · 2016
Earlier work this paper cites.
Macrobase: Prioritizing attention in fast data
Bailis, P., Gan, E., Madden, S., Narayanan, D., Rong, K., and Suri, S · 2017
Earlier work this paper cites.
Towards continuous scientific data analysis and hypothesis evolution
Gil, Y., Garijo, D., Ratnakar, V., Mayani, R., Adusumilli, R., Boyce, H., Srivastava, A., and Mallick, P · 2017
Earlier work this paper cites.
Noscope: Optimizing deep cnn-based queries over video streams at scale
Kang, D., Emmons, J., Abuzaid, F., Bailis, P. D., and Zaharia, M. A · 2017
Earlier work this paper cites.
The cancer genomics cloud: collaborative, reproducible, and democratized—a new paradigm in large-scale computational research
Lau, J. W., Lehnert, E., Sethi, A., Malhotra, R., Kaushik, G., Onder, Z., Groves-Kirkby, N., Mihajlovic, A., DiGiovanna, J., Srdic, M., et al · 2017
Cited alongside, same era.
Curiosity-driven exploration by self-supervised prediction
Pathak, D., Agrawal, P., Efros, A. A., and Darrell, T · 2017
Cited alongside, same era.
Open-endedness: The last grand challenge you’ve never heard of
Stanley, K. O., Lehman, J., and Soros, L · 2017
Cited alongside, same era.
Exploration by random network distillation
Burda, Y., Edwards, H., Storkey, A. J., and Klimov, O · 2018
Cited alongside, same era.
Evaluating the replicability of social science experiments in nature and science between 2010 and 2015
Camerer, C., Dreber, A., Holzmeister, F., Ho, T.-H., Huber, J., Johannesson, M., Kirchler, M., Nave, G., Nosek, B. A., Pfeiffer, T., Altmejd, A., Buttrick, N., Chan, T., Chen, Y., Forsell, E., Gampa, A., Heikensten, E., Hummer, L., Imai, T., Isaksson, S., Manfredi, D., Rose, J., Wagenmakers, E., and Wu, H · 2018
Artificial intelligence and scientific discovery: A model of prioritized search
Agrawal, A., McHale, J., and Oettl, A · 2023
Later among the works it cites.
Autonomous chemical research with large language models
Boiko, D. A., MacKnight, R., Kline, B., and Gomes, G · 2023
Later among the works it cites.
Large language models as tool makers
Cai, T., Wang, X., Ma, T., Chen, X., and Zhou, D · 2023
Later among the works it cites.
Understanding generative artificial intelligence and its relationship to copyright
Callison-Burch, C · 2023
Later among the works it cites.
Cao, H., Dodge, J., Lo, K., McFarland, D. A., and Wang, L. L · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Opinion: Is science really facing a reproducibility crisis, and do we need it to?
Fanelli, D · 2018
Cited alongside, same era.
A practical guide to methods controlling false discoveries in computational biology
Korthauer, K. D., Kimes, P. K., Duvallet, C., Reyes, A., Subramanian, A., Teng, M., Shukla, C. J., Alm, E. J., and Hicks, S. C · 2018
Cited alongside, same era.
surrosurv: An r package for the evaluation of failure time surrogate endpoints in individual patient data meta-analyses of randomized clinical trials
Rotolo, F., Paoletti, X., and Michiels, S · 2018
Cited alongside, same era.
Google dataset search: Building a search engine for datasets in an open web ecosystem
Brickley, D., Burgess, M., and Noy, N · 2019
Cited alongside, same era.
Diversity is all you need: Learning skills without a reward function
Eysenbach, B., Gupta, A., Ibarz, J., and Levine, S · 2019
Cited alongside, same era.
A practical guide to methods controlling false discoveries in computational biology
Korthauer, K., Kimes, P. K., Duvallet, C., Reyes, A., Subramanian, A., Teng, M., Shukla, C., Alm, E. J., and Hicks, S. C · 2019
Cited alongside, same era.
Dreamcoder: growing generalizable, interpretable knowledge with wake–sleep bayesian program learning
Ellis, K., Wong, C., Nye, M., Sablé-Meyer, M., Cary, L., Morales, L., Hewitt, L., Solar-Lezama, A., and Tenenbaum, J. B · 2020
Cited alongside, same era.
Citesee: Augmenting citations in scientific papers with persistent and personalized historical context
Chang, J. C., Zhang, A. X., Bragg, J., Head, A., Lo, K., Downey, D., and Weld, D. S · 2023
Later among the works it cites.
AI2’s Response to the US Copyright Requence for Comments on Artificial Intelligence and Copyright
Farhadi, A., Atkinson, D., Callison-Burch, C., DeCario, N., Dumas, J., Lo, K., and Soldiani, L · 2023
Later among the works it cites.
Feng, S., Park, C. Y., Liu, Y., and Tsvetkov, Y · 2023
Later among the works it cites.
How do data analysts respond to ai assistance? a wizard-of-oz study
Gu, K., Grunde-McLaughlin, M., McNutt, A. M., Heer, J., and Althoff, T · 2023
Later among the works it cites.
Large language models for software engineering: A systematic literature review
Hou, X., Zhao, Y., Liu, Y., Yang, Z., Wang, K., Li, L., Luo, X., Lo, D., Grundy, J. C., and Wang, H · 2023
Later among the works it cites.
Benchmarking large language models as ai research agents
Huang, Q., Vora, J., Liang, P., and Leskovec, J · 2023
Later among the works it cites.
Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts
Lu, P., Bansal, H., Xia, T., Liu, J., yue Li, C., Hajishirzi, H., Cheng, H., Chang, K.-W., Galley, M., and Gao, J · 2023
Later among the works it cites.
Self-refine: Iterative refinement with self-feedback
Madaan, A., Tandon, N., Gupta, P., Hallinan, S., Gao, L., Wiegreffe, S., Alon, U., Dziri, N., Prabhumoye, S., Yang, Y., Welleck, S., Majumder, B. P., Gupta, S., Yazdanbakhsh, A., and Clark, P · 2023
Later among the works it cites.
Reproducibility in nlp: What have we learned from the checklist?
Magnusson, I. H., Smith, N. A., and Dodge, J · 2023
Later among the works it cites.
Clin: A continually learning language agent for rapid task adaptation and generalization
Majumder, B. P., Dalvi, B., Jansen, P., Tafjord, O., Tandon, N., Zhang, L., Callison-Burch, C., and Clark, P · 2023
Later among the works it cites.
Relatedly: Scaffolding literature reviews with existing related work sections
Palani, S., Naik, A., Downey, D., Zhang, A. X., Bragg, J., and Chang, J. C · 2023
Later among the works it cites.
Pelrine, K., Taufeeque, M., Zajkac, M., McLean, E., and Gleave, A · 2023
Later among the works it cites.
Adapt: As-needed decomposition and planning with language models
Prasad, A., Koller, A., Hartmann, M., Clark, P., Sabharwal, A., Bansal, M., and Khot, T · 2023
Later among the works it cites.
Qiu, L., Jiang, L., Lu, X., Sclar, M., Pyatkin, V., Bhagavatula, C., Wang, B., Kim, Y., Choi, Y., Dziri, N., and Ren, X · 2023
Later among the works it cites.
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., Kohli, P., Fawzi, A., Grochow, J., Lodi, A., Mouret, J.-B., Ringer, T., and Yu, T · 2023
Later among the works it cites.
An empirical evaluation of using large language models for automated unit test generation
Schäfer, M., Nadi, S., Eghbali, A., and Tip, F · 2023
Later among the works it cites.
Toolformer: Language models can teach themselves to use tools
Schick, T., Dwivedi-Yu, J., Dessi, R., Raileanu, R., Lomeli, M., Hambro, E., Zettlemoyer, L., Cancedda, N., and Scialom, T · 2023
Later among the works it cites.
Automatic data transformation using large language model - an experimental study on building energy data
Sharma, A., Li, X., Guan, H., Sun, G., Zhang, L., Wang, L., Wu, K., Cao, L., Zhu, E., Sim, A., Wu, T., and Zou, J · 2023
Later among the works it cites.
Reflexion: Language agents with verbal reinforcement learning
Shinn, N., Cassano, F., Labash, B., Gopinath, A., Narasimhan, K., and Yao, S · 2023
Later among the works it cites.
Sql-palm: Improved large language model adaptation for text-to-sql
Sun, R., Arik, S. Ö., Nakhost, H., Dai, H., Sinha, R., Yin, P., and Pfister, T · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models
Touvron, H., Martin, L., Stone, K. R., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S., Bikel, D. M., Blecher, L., Ferrer, C. C., Chen, M., Cucurull, G., Esiobu, D., Fernandes, J., Fu, J., Fu, W., Fuller, B., Gao, C., Goswami, V., Goyal, N., Hartshorn, A. S., Hosseini, S., Hou, R., Inan, H., Kardas, M., Kerkez, V., Khabsa, M., Kloumann, I. M., Korenev, A. V., Koura, P. S., Lachaux, M.-A., Lavril, T., Lee, J., Liskovich, D., Lu, Y., Mao, Y., Martinet, X., Mihaylov, T., Mishra, P., Molybog, I., Nie, Y., Poulton, A., Reizenstein, J., Rungta, R., Saladi, K., Schelten, A., Silva, R., Smith, E. M., Subramanian, R., Tan, X., Tang, B., Taylor, R., Williams, A., Kuan, J. X., Xu, P., Yan, Z., Zarov, I., Zhang, Y., Fan, A., Kambadur, M., Narang, S., Rodriguez, A., Stojnic, R., Edunov, S., and Scialom, T · 2023
Later among the works it cites.
Fundamental limitations of alignment in large language models
Wolf, Y., Wies, N., Levine, Y., and Shashua, A · 2023
Later among the works it cites.
Omni: Open-endedness via models of human notions of interestingness
Zhang, J., Lehman, J., Stanley, K., and Clune, J · 2023
Later among the works it cites.
Sotopia: Interactive evaluation for social intelligence in language agents
Zhou, X., Zhu, H., Mathur, L., Zhang, R., Yu, H., Qi, Z., Morency, L.-P., Bisk, Y., Fried, D., Neubig, G., and Sap, M · 2023
Later among the works it cites.
Navigator: A gen-ai system for discovery of factual and predictive insights on domain-specific tabular datasets
Chakraborty, A., Banerjee, A., Dasgupta, S., Raturi, V., Soni, A., Gupta, A., Harsola, S., and Subrahmaniam, V. T · 2024
Closest in time.
Introducing duet ai for google workspace
Google · 2024
Closest in time.
Llms can’t plan, but can help planning in llm-modulo frameworks
Kambhampati, S., Valmeekam, K., Guan, L., Stechly, K., Verma, M., Bhambri, S., Saldyt, L., and Murthy, A · 2024
Closest in time.
Challenges in high-throughput inorganic material prediction and autonomous synthesis
Leeman, J., Liu, Y., Stiles, J., Lee, S., Bhatt, P., Schoop, L., and Palgrave, R · 2024
Closest in time.
Introducing copilot support for python in excel: Advanced data analysis using natural language
Monroy, D · 2024
Closest in time.
Pandas-profiling now supports apache spark
Santos, M., Clemente, F., and Abshire, C · 2024
Closest in time.
Solving olympiad geometry without human demonstrations
Trinh, T. H., Wu, Y., Le, Q. V., He, H., and Luong, T · 2024
Closest in time.