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Human expertise emerges through iterative cycles of interaction, reflection, and internal model updating, which are central to cognitive theories such as Kolb's experiential learning and Vygotsky's zone of proximal development.
“Large language models in medicine”
Arun Thirunavukarasu et al · 1940
Earlier work this paper cites.
“Learning representations by back-propagating errors”
David Rumelhart, Geoffrey Hinton and Ronald Williams · 1986
Earlier work this paper cites.
“Open-interpreter: A natural language interface for computers”
Open-interpreter Team · 2013
Earlier work this paper cites.
“Deep feature synthesis: Towards automating data science endeavors”
James Kanter and Kalyan Veeramachaneni · 2015
Earlier work this paper cites.
“Cognito: Automated Feature Engineering for Supervised Learning”
Udayan Khurana, Deepak Turaga, Horst Samulowitz and Srinivasan Parthasrathy · 2016
Earlier work this paper cites.
“AutoLearn — Automated Feature Generation and Selection”
Ambika Kaul, Saket Maheshwary and Vikram Pudi · 2017
Earlier work this paper cites.
“HoloClean: holistic data repairs with probabilistic inference”
Theodoros Rekatsinas, Xu Chu, Ihab. Ilyas and Christopher Ré · 2017
Earlier work this paper cites.
“Babyai: A platform to study the sample efficiency of grounded language learning”
Maxime Chevalier-Boisvert et al · 2018
Earlier work this paper cites.
“The RAMP framework: from reproducibility to transparency in the design and optimization of scientific workflows”, 2018
Balázs Kégl et al · 2018
Earlier work this paper cites.
“Reinforcement learning: An introduction”
Richard Sutton · 2018
Earlier work this paper cites.
“Optuna: A Next-Generation Hyperparameter Optimization Framework”
Takuya Akiba et al · 2019
Earlier work this paper cites.
“An Open Source AutoML Benchmark”
P… Gijsbers et al · 2019
Earlier work this paper cites.
“DARTS: Differentiable Architecture Search”
Hanxiao Liu, Karen Simonyan and Yiming Yang · 2019
Earlier work this paper cites.
“PyTorch: An Imperative Style, High-Performance Deep Learning Library”
Adam Paszke et al · 2019
Earlier work this paper cites.
“BOTORCH: a framework for efficient monte-carlo Bayesian optimization”
Maximilian Balandat et al · 2020
Earlier work this paper cites.
“H2O AutoML: Scalable Automatic Machine Learning”, 2020
Erin LeDell and S. Poirier · 2020
Earlier work this paper cites.
“Evaluating Large Language Models Trained on Code”
Mark Chen et al · 2021
Earlier work this paper cites.
“An elo-like system for massive multiplayer competitions”
Aram Ebtekar and Paul Liu · 2021
Earlier work this paper cites.
“Meta-Learning in Neural Networks: A Survey”
T. Hospedales, A. Antoniou, P. Micaelli and A. Storkey · 2021
Earlier work this paper cites.
“HEBO: Pushing The Limits of Sample-Efficient Hyperparameter Optimisation”
Alexander Cowen-Rivers et al · 2022
Earlier work this paper cites.
“Large Language Models Are Zero-Shot Fuzzers: Fuzzing Deep-Learning Libraries via Large Language Models”
Yinlin Deng et al · 2022
Earlier work this paper cites.
“Auto-sklearn 2.0: hands-free AutoML via meta-learning”
Matthias Feurer et al · 2022
Earlier work this paper cites.
“Towards Reasoning in Large Language Models: A Survey”
Jie Huang and Kevin-Chuan Chang · 2022
Earlier work this paper cites.
“DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation”
Yuhang Lai et al · 2022
Earlier work this paper cites.
“Pre-Trained Language Models for Interactive Decision-Making”
Shuang Li et al · 2022
Earlier work this paper cites.
“Training language models to follow instructions with human feedback”
Long Ouyang et al · 2022
Earlier work this paper cites.
“Chain of Thought Prompting Elicits Reasoning in Large Language Models”
Jason Wei et al · 2022
Earlier work this paper cites.
“ReAct: Synergizing Reasoning and Acting in Language Models”
Shunyu Yao et al · 2022
Earlier work this paper cites.
“GPT-4 Technical Report”, 2023
OpenAI Achiam et al · 2023
Earlier work this paper cites.
“Language Models Enable Simple Systems for Generating Structured Views of Heterogeneous Data Lakes”
Simran Arora et al · 2023
Earlier work this paper cites.
“Do as i can, not as i say: Grounding language in robotic affordances”
Anthony Brohan et al · 2023
Earlier work this paper cites.
“SEED: Domain-Specific Data Curation With Large Language Models”
Zui Chen et al · 2023
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“Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning”
Filippos Christianos et al · 2023
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“Framework and Benchmarks for Combinatorial and Mixed-variable Bayesian Optimization”
Kamil Dreczkowski, Antoine Grosnit and Haitham Ammar · 2023
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“Alphazero-like tree-search can guide large language model decoding and training”
Xidong Feng et al · 2023
Cited alongside, same era.
“Large Language Models for Automated Data Science: Introducing CAAFE for Context-Aware Automated Feature Engineering”
Noah Hollmann, Samuel Müller and Frank Hutter · 2023
“MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework”
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