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The fields of both Natural Language Processing (NLP) and Automated Machine Learning (AutoML) have achieved remarkable results over the past years.
Roberta: A robustly optimized bert pretraining approach
Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov · 1907
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On Data Snooping and Multiple Outlier Testing
J. Kok · 1984
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Japanese and korean coice search
M. Schuster and K. Nakajima · 2012
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Auto-WEKA: combined selection and Hyperparameter Optimization of classification algorithms
C. Thornton, F. Hutter, H. Hoos, and K. Leyton-Brown · 2013
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Automatic pipeline construction for real-time annotation
H. Wachsmuth, M. Rose, and G. Engels · 2013
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Hyperopt-sklearn: Automatic hyperparameter configuration for scikit-learn
B. Komer, J. Bergstra, and C. Eliasmith · 2014
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Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
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Gradient-based Hyperparameter Optimization through Reversible Learning
D. Maclaurin, D. Duvenaud, and R. Adams · 2015
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Hyperparameter search space pruning - A new component for sequential model-based hyperparameter optimization
M. Wistuba, N. Schilling, and L. Schmidt-Thieme · 2015
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Colmenarejo, M. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
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J. Ba, J. Kiros, and G. Hinton · 2016
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Bridging nonlinearities and stochastic regularizers with gaussian error linear units
D. Hendrycks and K. Gimpel · 2016
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Scalable Gradient-Based Tuning of Continuous Regularization Hyperparameters
J. Luketina, M. Berglund, K. Greff, and T. Raiko · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Neural machine translation of rare words with subword units
R. Sennrich, B. Haddow, and A. Birch · 2016
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Forward and Reverse Gradient-Based Hyperparameter Optimization
L. Franceschi, M. Donini, P. Frasconi, and M. Pontil · 2017
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Learning curve prediction with Bayesian neural networks
A. Klein, S. Falkner, J. Springenberg, and F. Hutter · 2017
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Auto-weka 2.0: Automatic model selection and hyperparameter optimization in WEKA
L. Kotthoff, C. Thornton, H. Hoos, F. Hutter, and K. Leyton-Brown · 2017
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SGDR: Stochastic gradient descent with warm restarts
I. Loshchilov and F. Hutter · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
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Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. Gomez, L. Kaiser, and I. Polosukhin · 2017
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Understanding and simplifying one-shot architecture search
G. Bender, P-J. Kindermans, B. Zoph, V. Vasudevan, and Q. Le · 2018
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Smash: One-shot model architecture search through hypernetworks
A. Brock, T. Lim, J. Ritchie, and N. Weston · 2018
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Towards further automation in AutoML
M. Feurer and F. Hutter · 2018
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Explaining explanations: An overview of interpretability of machine learning
L. Gilpin, D. Bau, B. Yuan, A. Bajwa, M. Specter, and L. Kagal · 2018
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Subword regularization: Improving neural network translation models with multiple subword candidates
T. Kudo · 2018
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Reinforcement learning algorithm selection
R. Laroche and R. Feraud · 2018
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Hyperband: A novel bandit-based approach to Hyperparameter Optimization
L. Li, K. Jamieson, G. DeSalvo, A. Rostamizadeh, and A. Talwalkar · 2018
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Fixing weight decay regularization in adam
I. Loshchilov and F. Hutter · 2018
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ML-Plan for unlimited-length machine learning pipelines
F. Mohr M. Wever and E. Hüllermeier · 2018
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Scalable hyperparameter transfer learning
V. Perrone, R. Jenatton, M. Seeger, and C. Archambeau · 2018
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Improving language understanding by generative pre-training
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever · 2018
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Adafactor: Adaptive learning rates with sublinear memory cost
N. Shazeer and M. Stern · 2018
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AttnGAN: Fine-grained text to image generation with attentional generative adversarial networks
T. Xu, P. Zhang, Q. Huang, H. Zhang, Z. Gan, X. Huang, and X. He · 2018
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Optuna: A next-generation Hyperparameter Optimization framework
T. Akiba, S. Sano, T. Yanase, T. Ohta, and M. Koyama · 2019
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OpenML: An R package to connect to the machine learning platform OpenML
G. Casalicchio, J. Bossek, M. Lang, D. Kirchhoff, P. Kerschke, B. Hofner, H. Seibold, J. Vanschoren, and B. Bischl · 2019
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Progressive differentiable architecture search: Bridging the depth gap between search and evaluation
X. Chen, L. Xie, J. Wu, and Q. Tian · 2019
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BERT: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M. Chang, K. Lee, and K. Toutanova · 2019
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Neural Architecture Search: A survey
T. Elsken, J. Metzen, and F. Hutter · 2019
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Hyperparameter Optimization
M. Feurer and F. Hutter · 2019
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Auto-sklearn: Efficient and robust automated machine learning
M. Feurer, A. Klein, K. Eggensperger, J. Springenberg, M. Blum, and F. Hutter · 2019
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Parameter-efficient transfer learning for NLP
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly · 2019
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Automated Machine Learning: Methods, Systems, Challenges
F. Hutter, L. Kotthoff, and J. Vanschoren (eds.) · 2019
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Auto-Keras: An efficient neural architecture search system
H. Jin, Q. Song, and X. Hu · 2019
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Auto-WEKA: automatic model selection and hyperparameter optimization in WEKA
L. Kotthoff, C. Thornton, H. Hoos, F. Hutter, and K. Leyton-Brown · 2019
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Hyperparameter learning via distributional transfer
H. Law, P. Zhao, L. Chan, J. Huang, and D. Sejdinovic · 2019
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What would elsa do? freezing layers during transformer fine-tuning
J. Lee, R. Tang, and J. Lin · 2019
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A generalized framework for population based training
A. Li, O. Spyra, S. Perel, V. Dalibard, M. Jaderberg, C. Gu, D. Budden, T. Harley, and P. Gupta · 2019
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Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions
M. MacKay, P. Vicol, J. Lorraine, D. Duvenaud, and R. Grosse · 2019
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Filtering bayesian optimization approach in weakly specified search space
V. Nguyen, S. Gupta, S. Rana, C. Li, and S. Venkatesh · 2019
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TPOT: A tree-based pipeline optimization tool for automating machine learning
R. Olson and J. Moore · 2019
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F. Pfisterer, J. Thomas, and B. Bischl · 2019
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Language models are unsupervised multitask learners
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever · 2019
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Megatron-LM: Training multi-billion parameter language models using model parallelism
M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro · 2019
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Meta-learning
J. Vanschoren · 2019
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A survey on neural architecture search
M. Wistuba, A. Rawat, and T. Pedapati · 2019
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NAS-Bench-101: Towards reproducible Neural Architecture Search
C. Ying, A. Klein, E. Christiansen, E. Real, K. Murphy, and F. Hutter · 2019
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Chatbots: History, technology, and applications
E. Adamopoulou and L. Moussiades · 2020
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Botorch: A framework for efficient monte-carlo Bayesian optimization
M. Balandat, B. Karrer, D. Jiang, S. Daulton, B. Letham, A. Wilson, and E. Bakshy · 2020
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei · 2020
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AdaBERT: Task-adaptive BERT compression with differentiable neural architecture search
D. Chen, Y. Li, M. Qiu, Z. Z. Wang, B. Li, B. Ding, H. Deng, J. Huang, W. Lin, and J. Zhou · 2020
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NAS-Bench-201: Extending the scope of reproducible Neural Architecture Search
X. Dong and Y. Yang · 2020
Cited alongside, same era.
Autogluon-tabular: Robust and accurate automl for structured data
N. Erickson, J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li, and A. Smola · 2020
Cited alongside, same era.
Angle-based search space shrinking for neural architecture search
Y. Hu, Y. Liang, Z. Guo, R. Wan, X. Zhang, Y. Wei, Q. Gu, and J. Sun · 2020
Cited alongside, same era.
Scaling laws for neural language models, 2020
J. Kaplan, S. McCandlish, T. Henighan, T. Brown, B. Chess, R. Child, S. Gray, A. Radford, J. Wu, and D. Amodei · 2020
Cited alongside, same era.
BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer · 2020
Cited alongside, same era.
RoFormer: Enhanced transformer with rotary position embedding
J. Su, Y. Lu, S. Pan, A. Murtadha, B. Wen, and Y. Liu · 2022
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LaMDA: Language models for dialog applications
R. Thoppilan, D. De Freitas, J. Hall, N. Shazeer, A. Kulshreshtha, H. Cheng, A. Jin, T. Bos, L. Baker, Y. Du, Y. Li, H. Lee, H. Zheng, A. Ghafouri, M. Menegali, Y. Huang, M. Krikun, D. Lepikhin, J. Qin, D. Chen, Y. Xu, Z. Chen, A. Roberts, M. Bosma, Y. Zhou, C. Chang, I. Krivokon, W. Rusch, M. Pickett, K. Meier-Hellstern, M. Morris, T. Doshi, R. Santos, T. Duke, J. Soraker, B. Zevenbergen, V. Prabhakaran, M. Diaz, B. Hutchinson, K. Olson, A. Molina, E. Hoffman-John, J. Lee, L. Aroyo, R. Rajakumar, A. Butryna, M. Lamm, V. Kuzmina, J. Fenton, A. Cohen, R. Bernstein, R. Kurzweil, B. Aguera-Arcas, C. Cui, M. Croak, E. Chi, and Q. Le · 2022
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Algorithm selection on a meta level
A. Tornede, L. Gehring, T. Tornede, M. Wever, and E. Hüllermeier · 2022
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Bayesian generational population-based training
X. Wan, C. Lu, J. Parker-Holder, P. Ball, V. Nguyen, B. Ru, and M. Osborne · 2022
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Towards debiasing sentence representations
P. Liang, I. Li, E. Zheng, Y. Lim, R. Salakhutdinov, and L. Morency · 2020
Cited alongside, same era.
Best practices for scientific research on Neural Architecture Search
M. Lindauer and F. Hutter · 2020
Cited alongside, same era.
Optimizing millions of hyperparameters by implicit differentiation
J. Lorraine, P. Vicol, and D. Duvenaud · 2020
Cited alongside, same era.
Provably efficient online Hyperparameter Optimization with population-based bandits
J. Parker-Holder, V. Nguyen, and S. J. Roberts · 2020
Cited alongside, same era.
GLU variants improve transformer
N. Shazeer · 2020
Cited alongside, same era.
Q-BERT: hessian based ultra low precision quantization of BERT
S. Shen, Z. Dong, J. Ye, L. Ma, Z. Yao, A. Gholami, M. Mahoney, and K. Keutzer · 2020
Cited alongside, same era.
Bridging the gap between sample-based and one-shot Neural Architecture Search with BONAS
H. Shi, R. Pi, H. Xu, Z. Li, J. Kwok, and T. Zhang · 2020
Cited alongside, same era.
Finetuned language models are zero-shot learners
J. Wei, M Bosma, V. Zhao, K. Guu, A. Wei Yu, B. Lester, Du N, A. Dai, and Q. Le · 2022
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Factorizing knowledge in neural networks
X. Yang, J. Ye, and X. Wang · 2022
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Surrogate NAS benchmarks: Going beyond the limited search spaces of tabular NAS benchmarks
A. Zela, J. Siems, L. Zimmer, J. Lukasik, M. Keuper, and F. Hutter · 2022
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OPT: Open pre-trained transformer language models
S. Zhang, S. Roller, N. Goyal, M. Artetxe, M. Chen, S. Chen, C. Dewan, M. Diab, X. Li, X. Lin, T. Mihaylov, M. Ott, S. Shleifer, K. Shuster, D. Simig, P. Koura, A. Sridhar, T. Wang, and L. Zettlemoyer · 2022
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Large language models (LLM) and ChatGPT: What will the impact on nuclear medicine be?
I. Alberts, L. Mercolli, T. Pyka, G. Prenosil, K. Shi, A. Rominger, and A. Afshar-Oromieh · 2023
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A cookbook of self-supervised learning
R. Balestriero, M. Ibrahim, V. Sobal, A. Morcos, S. Shekhar, T. Goldstein, F. Bordes, A. Bardes, G. Mialon, Y. Tian, A. Schwarzschild, A. Wilson, J. Geiping, Q. Garrido, P. Fernandez, A. Bar, H. Pirsiavash, Y. LeCun, and M. Goldblum · 2023
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Graph of thoughts: Solving elaborate problems with large language models
M. Besta, N. Blach, A. Kubicek, R. Gerstenberger, L. Gianinazzi, J. Gajda, T. Lehmann, M. Podstawski, H. Niewiadomski, P. Nyczyk, and T. Hoefler · 2023
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Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
B. Bischl, M. Binder, M. Lang, T. Pielok, J. Richter, S. Coors, J. Thomas, T. Ullmann, M. Becker, A.-L. Boulesteix, D. Deng, and M. Lindauer · 2023
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PASHA: Efficient HPO and NAS with progressive resource allocation
O. Bohdal, L. Balles, M. Wistuba, B. Ermis, C. Archambeau, and G. Zappella · 2023
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EvoPrompting: Language models for code-level neural architecture search
A. Chen, D. Dohan, and D. So · 2023
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Aliro: an automated machine learning tool leveraging large language models
H. Choi, J. Moran, N. Matsumoto, M. Hernandez, and J. Moore · 2023
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GitHub copilot AI pair programmer: Asset or liability?
A. Dakhel, V. Majdinasab, A. Nikanjam, F. Khomh, M. Desmarais, and Z. Jiang · 2023
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AtMan: Understanding transformer predictions through memory efficient attention manipulation
M. Deb, B. Deiseroth, S. Weinbach, P. Schramowski, and K. Kersting · 2023
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Hyperparameters in reinforcement learning and how to tune them
T. Eimer, M. Lindauer, and R. Raileanu · 2023
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Perspectives on large language models for relevance judgment, 2023
G. Faggioli, L. Dietz, C. Clarke, G. Demartini, M. Hagen, C. Hauff, N. Kando, E. Kanoulas, M. Potthast, B. Stein, and H. Wachsmuth · 2023
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Bridging the gap: A survey on integrating (Human) feedback for natural language generation
P. Fernandes, A. Madaan, E. Liu, A. Farinhas, P. Martins, A. Bertsch, J. de Souza, S. Zhou, T. Wu, G. Neubig, and A. Martins · 2023
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Cramming: Training a language model on a single gpu in one day
J. Geiping and T. Goldstein · 2023
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Deep learning tuning playbook, 2023
V. Godbole, G. Dahl, J. Gilmer, C. Shallue, and Z. Nado · 2023
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TIAM – a metric for evaluating alignment in text-to-image generation
P. Grimal, H. L. Borgne, O. Ferret, and J. Tourille · 2023
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Knowledge distillation of large language models
Y. Gu, L. Dong, F. Wei, and M. Huang · 2023
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MedAlpaca - an open-source collection of medical conversational AI models and training data
T. Han, L. Adams, J. Papaioannou, P. Grundmann, T. Oberhauser, A. Löser, D. Truhn, and K. Bressem · 2023
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Measuring and manipulating knowledge representations in language models
E. Hernandez, B. Li, and J. Andreas · 2023
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Distilling step-by-step! outperforming larger language models with less training data and smaller model sizes
C. Hsieh, C. Li, C. Yeh, H. Nakhost, Y. Fujii, A. Ratner, R. Krishna, C. Lee, and T. Pfister · 2023
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Survey of hallucination in natural language generation
Z. Ji, N. Lee, R. Frieske, T. Yu, D. Su, Y. Xu, E. Ishii, Y. Bang, A. Madotto, and P. Fung · 2023
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General intelligence requires rethinking exploration
M. Jiang, T. Rocktäschel, and E. Grefenstette · 2023
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Multi-objective hyperparameter optimization – an overview
F. Karl, T. Pielok, J. Moosbauer, F. Pfisterer, S. Coors, M. Binder, L. Schneider, J. Thomas, J. Richter, M. Lang, E. Garrido-Merchán, J. Branke, and B. Bischl · 2023
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ChatGPT for good? on opportunities and challenges of large language models for education
E. Kasneci, K. Seßler, S. Küchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Günnemann, E. Hüllermeier, S. Krusche, G. Kutyniok, T. Michaeli, C. Nerdel, J. Pfeffer, O. Poquet, M. Sailer, A. Schmidt, T. Seidel, M. Stadler, J. Weller, J. Kuhn, and G. Kasneci · 2023
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OpenAssistant conversations - democratizing large language model alignment
A. Köpf, Y. Kilcher, D. von Rütte, S. Anagnostidis, Z. Tam, K. Stevens, A. Barhoum, N. Duc, O. Stanley, R. Nagyfi, S. ES, S. Suri, D. Glushkov, A. Dantuluri, A. Maguire, C. Schuhmann, H. Nguyen, and A. Mattick · 2023
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Language generation models can cause harm: So what can we do about it? an actionable survey
S. Kumar, V. Balachandran, L. Njoo, A. Anastasopoulos, and Y. Tsvetkov · 2023
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Active instruction tuning: Improving cross-task generalization by training on prompt sensitive tasks
P. Kung, F. Yin, D. Wu, K. Chang, and N. Peng · 2023
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Learning to optimize for reinforcement learning
Q. Lan, A. Mahmood, S. Yan, and Z. Xu · 2023
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J. Li, D. Li, S. Savarese, and S. Hoi · 2023
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Holistic evaluation of language models
P. Liang, R. Bommasani, T. Lee, D. Tsipras, D. Soylu, M. Yasunaga, Y. Zhang, D. Narayanan, Y. Wu, A. Kumar, B. Newman, B. Yuan, B. Yan, C. Zhang, C. Cosgrove, C. D. Manning, C. Ré, C. Acosta-Navas, D. Hudson, E. Zelikman, E. Durmus, F. Ladhak, F. Rong, H. Ren, H. Yao, J. Wang, K. Santhanam, L. J. Orr, L. Zheng, M. Yüksekgönül, M. Suzgun, N. Kim, N. Guha, N. S. Chatterji, O. Khattab, P. Henderson, Q. Huang, R. Chi, S. Michael Xie, S. Santurkar, S. Ganguli, T. Hashimoto, T. Icard, T. Zhang, V. Chaudhary, W. Wang, X. Li, Y. Mai, Y. Zhang, and Y. Koreeda · 2023
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Mftcoder: Boosting code llms with multitask fine-tuning
B. Liu, C. Chen, C. Liao, Z. Gong, H. Wang, Z. Lei, M. Liang, D. Chen, M. Shen, H. Zhou, H. Yu, and J. Li · 2023
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PriorBand: Practical hyperparameter optimization in the age of deep learning
N. Mallik, C. Hvarfner, E. Bergman, D. Stoll, M. Janowski, M. Lindauer, L. Nardi, and F. Hutter · 2023
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Autorl hyperparameter landscapes
A. Mohan, C. Benjamins, K. Wienecke, A. Dockhorn, and M. Lindauer · 2023
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Pfns4bo: In-context learning for bayesian optimization
S. Müller, M. Feurer, N. Hollmann, and F. Hutter · 2023
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Llmatic: Neural architecture search via large language models and quality-diversity optimization
M. Nasir, S. Earle, J. Togelius, S. James, and C. Cleghorn · 2023
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Skeleton-of-thought: Large language models can do parallel decoding
X. Ning, Z. Lin, Z. Zhou, H. Yang, and Y. Wang · 2023
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Creative mutation: A prescriptive approach to the use of ChatGPT and large language models in lawyering
N. Noonan · 2023
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Gpt-4 is OpenAI’s most advanced system, producing safer and more useful responses
OpenAI · 2023
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
X. Qi, Y. Zeng, T. Xie, P. Chen, R. Jia, P. Mittal, and P. Henderson · 2023
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Direct preference optimization: Your language model is secretly a reward model
R. Rafailov, A. Sharma, E. Mitchell, S. Ermon, C. Manning, and C. Finn · 2023
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MASIF: Meta-learned algorithm selection using implicit fidelity information
T. Ruhkopf, A. Mohan, D. Deng, A. Tornede, F. Hutter, and M. Lindauer · 2023
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FineDeb: A debiasing framework for language models
A. Saravanan, D. Mullick, H. Rahman, and N. Hegde · 2023
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Symbolic explanations for hyperparameter optimization
S. Segel, H. Graf, A. Tornede, B. Bischl, and M. Lindauer · 2023
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Towards green automated machine learning: Status quo and future directions
T. Tornede, A. Tornede, J. Hanselle, F. Mohr, M. Wever, and E. Hüllermeier · 2023
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LLaMA: Open and efficient foundation language models
H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, and G. Lample · 2023
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Automl-gpt: Large language model for automl
Y. Tsai, Y. Tsai, B. Huang, C. Yang, and S. Lin · 2023
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Can fairness be automated? guidelines and opportunities for fairness-aware automl
H. Weerts, F. Pfisterer, M. Feurer, K. Eggensperger, E. Bergman, N. Awad, J. Vanschoren, M. Pechenizkiy, B. Bischl, and F. Hutter · 2023
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Neural architecture search: Insights from 1000 papers
C. White, M. Safari, R. Sukthanker, B. Ru, T. Elsken, A. Zela, D. Dey, and F. Hutter · 2023
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A survey on multimodal large language models
S. Yin, C. Fu, S. Zhao, K. Li, X. Sun, T. Xu, and E. Chen · 2023
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A comprehensive review of binary neural network
C. Yuan and S. Agaian · 2023
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Can gpt-4 perform neural architecture search?
M. Zheng, X. Su, S. You, F. Wang, C. Qian, C. Xu, and S. Albanie · 2023
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Large language models are human-level prompt engineers
Y. Zhou, A. Ioan Muresanu, Z. Han, K. Paster, S. Pitis, H. Chan, and J. Ba · 2023
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Large language models to enhance bayesian optimization
T. Liu, N. Astorga, N. Seedat, and M. van der Schaar · 2024
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