Fetching the paper…
Reading the bibliography…
A central notion in practical and theoretical machine learning is that of a $\textit{weak learner}$, classifiers that achieve better-than-random performance (on any given distribution over data), even by a small margin.
The strength of weak learnability
R. E. Schapire · 1990
Earlier work this paper cites.
A decision-theoretic generalization of on-line learning and an application to boosting
Y. Freund and R. E. Schapire · 1997
Earlier work this paper cites.
Greedy function approximation: a gradient boosting machine
J. H. Friedman · 2001
Earlier work this paper cites.
Stochastic gradient boosting
J. H. Friedman · 2002
Earlier work this paper cites.
Openml: networked science in machine learning
J. Vanschoren, J. N. Van Rijn, B. Bischl, and L. Torgo · 2014
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
Earlier work this paper cites.
Introduction to HPC with MPI for Data Science
F. Nielsen · 2016
Earlier work this paper cites.
UCI machine learning repository, 2017
D. Dua and C. Graff · 2017
Earlier work this paper cites.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova · 2018
Earlier work this paper cites.
Somerville happiness survey data set
W. W. Koczkodaj · 2018
Earlier work this paper cites.
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 · 2019
Earlier work this paper cites.
Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
Earlier work this paper cites.
Tapas: Weakly supervised table parsing via pre-training
J. Herzig, P. K. Nowak, T. Müller, F. Piccinno, and J. M. Eisenschlos · 2020
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
T. Shin, Y. Razeghi, R. L. Logan IV, E. Wallace, and S. Singh · 2020
Earlier work this paper cites.
Tabert: Pretraining for joint understanding of textual and tabular data
P. Yin, G. Neubig, W.-t. Yih, and S. Riedel · 2020
Cited alongside, same era.
Deep neural networks and tabular data: A survey
V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci · 2021
Cited alongside, same era.
Revisiting deep learning models for tabular data
Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko · 2021
Cited alongside, same era.
Knowledgeable prompt-tuning: Incorporating knowledge into prompt verbalizer for text classification
S. Hu, N. Ding, H. Wang, Z. Liu, J. Li, and M. Sun · 2021
Cited alongside, same era.
The power of scale for parameter-efficient prompt tuning
B. Lester, R. Al-Rfou, and N. Constant · 2021
Large language models can self-improve
J. Huang, S. S. Gu, L. Hou, Y. Wu, X. Wang, H. Yu, and J. Han · 2022
Later among the works it cites.
Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
Later among the works it cites.
Solving quantitative reasoning problems with language models
A. Lewkowycz, A. Andreassen, D. Dohan, E. Dyer, H. Michalewski, V. Ramasesh, A. Slone, C. Anil, I. Schlag, T. Gutman-Solo, et al · 2022
Later among the works it cites.
Can foundation models wrangle your data?
A. Narayan, I. Chami, L. Orr, and C. Ré · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
X. Liu, Y. Zheng, Z. Du, M. Ding, Y. Qian, Z. Yang, and J. Tang · 2021
Cited alongside, same era.
Learning how to ask: Querying lms with mixtures of soft prompts
G. Qin and J. Eisner · 2021
Cited alongside, same era.
Prompt programming for large language models: Beyond the few-shot paradigm
L. Reynolds and K. McDonell · 2021
Cited alongside, same era.
Prototypical verbalizer for prompt-based few-shot tuning
G. Cui, S. Hu, N. Ding, L. Huang, and Z. Liu · 2022
Cited alongside, same era.
Lift: Language-interfaced fine-tuning for non-language machine learning tasks
T. Dinh, Y. Zeng, R. Zhang, Z. Lin, S. Rajput, M. Gira, J.-y. Sohn, D. Papailiopoulos, and K. Lee · 2022
Cited alongside, same era.
Language models can teach themselves to program better
P. Haluptzok, M. Bowers, and A. T. Kalai · 2022
Cited alongside, same era.
Structured prompting: Scaling in-context learning to 1,000 examples
Y. Hao, Y. Sun, L. Dong, Z. Han, Y. Gu, and F. Wei · 2022
Cited alongside, same era.
L. Ouyang, J. Wu, X. Jiang, D. Almeida, C. Wainwright, P. Mishkin, C. Zhang, S. Agarwal, K. Slama, A. Ray, et al · 2022
Later among the works it cites.
Murmur: Modular multi-step reasoning for semi-structured data-to-text generation
S. Saha, X. V. Yu, M. Bansal, R. Pasunuru, and A. Celikyilmaz · 2022
Later among the works it cites.
Hopular: Modern hopfield networks for tabular data
B. Schäfl, L. Gruber, A. Bitto-Nemling, and S. Hochreiter · 2022
Later among the works it cites.
Tabular data: Deep learning is not all you need
R. Shwartz-Ziv and A. Armon · 2022
Later among the works it cites.
Chain of thought prompting elicits reasoning in large language models
J. Wei, X. Wang, D. Schuurmans, M. Bosma, E. Chi, Q. Le, and D. Zhou · 2022
Later among the works it cites.
Generate rather than retrieve: Large language models are strong context generators
W. Yu, D. Iter, S. Wang, Y. Xu, M. Ju, S. Sanyal, C. Zhu, M. Zeng, and M. Jiang · 2022
Later among the works it cites.
Automatic chain of thought prompting in large language models
Z. Zhang, A. Zhang, M. Li, and A. Smola · 2022
Later among the works it cites.
Active prompting with chain-of-thought for large language models
S. Diao, P. Wang, Y. Lin, and T. Zhang · 2023
Closest in time.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
Closest in time.
Can discrete information extraction prompts generalize across language models?
N. C. Rakotonirina, R. Dessì, F. Petroni, S. Riedel, and M. Baroni · 2023
Closest in time.