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Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields.
1907
Earlier work this paper cites.
M. G. Kendall, “A new measure of rank correlation,” Biometrika , vol. 30, no. 1/2, pp. 81–93, 1938
1938
Earlier work this paper cites.
H. B. Mann and D. R. Whitney, “On a test of whether one of two random variables is stochastically larger than the other,” The annals of mathematical statistics , pp. 50–60, 1947
1947
Earlier work this paper cites.
L. E. Baum, T. Petrie, G. Soules, and N. Weiss, “A maximization technique occurring in the statistical analysis of probabilistic functions of markov chains,” The annals of mathematical statistics , vol. 41, no. 1, pp. 164–171, 1970
1970
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, R. J. Williams et al. , “Learning internal representations by error propagation,” 1985
1985
Earlier work this paper cites.
L. Rabiner and B. Juang, “An introduction to hidden markov models,” ieee assp magazine , vol. 3, no. 1, pp. 4–16, 1986
1986
Earlier work this paper cites.
Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel, “Handwritten digit recognition with a back-propagation network,” Advances in neural information processing systems , vol. 2, 1989
1989
Earlier work this paper cites.
G. R. Terrell and D. W. Scott, “Variable kernel density estimation,” The Annals of Statistics , pp. 1236–1265, 1992
1992
Earlier work this paper cites.
H. T. Siegelmann and E. D. Sontag, “On the computational power of neural nets,” in Proceedings of the fifth annual workshop on Computational learning theory , 1992, pp. 440–449
1992
Earlier work this paper cites.
Z. Zeng, R. M. Goodman, and P. Smyth, “Learning finite state machines with self-clustering recurrent networks,” Neural Computation , vol. 5, no. 6, pp. 976–990, 1993
1993
Earlier work this paper cites.
Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE transactions on neural networks , vol. 5, no. 2, pp. 157–166, 1994
1994
Earlier work this paper cites.
D. B. Lenat, “Cyc: A large-scale investment in knowledge infrastructure,” Commun. ACM , vol. 38, no. 11, p. 33–38, nov 1995. [Online]. Available: https://doi.org/10.1145/219717.219745
1995
Earlier work this paper cites.
G. Gigerenzer and U. Hoffrage, “How to improve bayesian reasoning without instruction: Frequency formats.” Psychological review , vol. 102, no. 4, p. 684, 1995
1995
Earlier work this paper cites.
C. W. Omlin and C. L. Giles, “Extraction of rules from discrete-time recurrent neural networks,” Neural networks , vol. 9, no. 1, pp. 41–52, 1996
1996
Earlier work this paper cites.
A. P. Bradley, “The use of the area under the roc curve in the evaluation of machine learning algorithms,” Pattern recognition , vol. 30, no. 7, pp. 1145–1159, 1997
1997
Earlier work this paper cites.
S. R. Eddy, “Profile hidden markov models.” Bioinformatics (Oxford, England) , vol. 14, no. 9, pp. 755–763, 1998
1998
Earlier work this paper cites.
S. Fine, Y. Singer, and N. Tishby, “The hierarchical hidden markov model: Analysis and applications,” Machine learning , vol. 32, pp. 41–62, 1998
1998
Earlier work this paper cites.
K. Krishna and M. N. Murty, “Genetic k-means algorithm,” IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) , vol. 29, no. 3, pp. 433–439, 1999
1999
Earlier work this paper cites.
P. Y. Chen and P. M. Popovich, Correlation: Parametric and nonparametric measures . Sage, 2002, no. 139
2002
Earlier work this paper cites.
A. L. Cechin, D. Regina, P. Simon, and K. Stertz, “State automata extraction from recurrent neural nets using k-means and fuzzy clustering,” in 23rd International Conference of the Chilean Computer Science Society, 2003. SCCC 2003. Proceedings. IEEE, 2003, pp. 73–78
2003
Earlier work this paper cites.
E. Nummelin, General irreducible Markov chains and non-negative operators . Cambridge University Press, 2004, no. 83
2004
Earlier work this paper cites.
O. Kupferman, “Sanity checks in formal verification,” in International Conference on Concurrency Theory . Springer, 2006, pp. 37–51
2006
Earlier work this paper cites.
D. A. Reynolds et al. , “Gaussian mixture models.” Encyclopedia of biometrics , vol. 741, no. 659-663, 2009
2009
Earlier work this paper cites.
I. Cohen, Y. Huang, J. Chen, J. Benesty, J. Benesty, J. Chen, Y. Huang, and I. Cohen, “Pearson correlation coefficient,” Noise reduction in speech processing , pp. 1–4, 2009
2009
Earlier work this paper cites.
J. G. Moreno-Torres, T. Raeder, R. Alaiz-Rodríguez, N. V. Chawla, and F. Herrera, “A unifying view on dataset shift in classification,” Pattern recognition , vol. 45, no. 1, pp. 521–530, 2012
2012
Earlier work this paper cites.
R. Pascanu, T. Mikolov, and Y. Bengio, “On the difficulty of training recurrent neural networks,” in International conference on machine learning . Pmlr, 2013, pp. 1310–1318
2013
Earlier work this paper cites.
B. Biggio, I. Corona, D. Maiorca, B. Nelson, N. Šrndić, P. Laskov, G. Giacinto, and F. Roli, “Evasion attacks against machine learning at test time,” in Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2013, Prague, Czech Republic, September 23-27, 2013, Proceedings, Part III 13 . Springer, 2013, pp. 387–402
2013
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Y. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. J. Goodfellow, and R. Fergus, “Intriguing properties of neural networks,” in 2nd International Conference on Learning Representations (ICLR) , 2014
2014
Earlier work this paper cites.
R. Bro and A. K. Smilde, “Principal component analysis,” Analytical methods , vol. 6, no. 9, pp. 2812–2831, 2014
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “” why should i trust you?” explaining the predictions of any classifier,” in Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining , 2016, pp. 1135–1144
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. Pei, Y. Cao, J. Yang, and S. Jana, “Deepxplore: Automated whitebox testing of deep learning systems,” in proceedings of the 26th Symposium on Operating Systems Principles , 2017, pp. 1–18
2017
Earlier work this paper cites.
D. Hendrycks and K. Gimpel, “A baseline for detecting misclassified and out-of-distribution examples in neural networks,” in International Conference on Learning Representations , 2017. [Online]. Available: https://openreview.net/forum?id=Hkg4TI9xl
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Y. Ming, S. Cao, R. Zhang, Z. Li, Y. Chen, Y. Song, and H. Qu, “Understanding hidden memories of recurrent neural networks,” in 2017 IEEE conference on visual analytics science and technology (VAST) . IEEE, 2017, pp. 13–24
2017
Earlier work this paper cites.
Z. Hu, Z. Yang, X. Liang, R. Salakhutdinov, and E. P. Xing, “Toward controlled generation of text,” in International conference on machine learning . PMLR, 2017, pp. 1587–1596
2017
Earlier work this paper cites.
L. Ma, F. Juefei-Xu, F. Zhang, J. Sun, M. Xue, B. Li, C. Chen, T. Su, L. Li, Y. Liu et al. , “Deepgauge: Multi-granularity testing criteria for deep learning systems,” in Proceedings of the 33rd ACM/IEEE international conference on automated software engineering , 2018, pp. 120–131
2018
Earlier work this paper cites.
L. Ma, F. Zhang, J. Sun, M. Xue, B. Li, F. Juefei-Xu, C. Xie, L. Li, Y. Liu, J. Zhao et al. , “Deepmutation: Mutation testing of deep learning systems,” in 2018 IEEE 29th International Symposium on Software Reliability Engineering (ISSRE) . IEEE, 2018, pp. 100–111
2018
Earlier work this paper cites.
Y. Tian, K. Pei, S. Jana, and B. Ray, “Deeptest: Automated testing of deep-neural-network-driven autonomous cars,” in Proceedings of the 40th international conference on software engineering , 2018, pp. 303–314
2018
Earlier work this paper cites.
M. Zhang, Y. Zhang, L. Zhang, C. Liu, and S. Khurshid, “Deeproad: Gan-based metamorphic testing and input validation framework for autonomous driving systems,” in 2018 33rd IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2018, pp. 132–142
2018
Earlier work this paper cites.
K. Lee, K. Lee, H. Lee, and J. Shin, “A simple unified framework for detecting out-of-distribution samples and adversarial attacks,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
S. Liang, Y. Li, and R. Srikant, “Enhancing the reliability of out-of-distribution image detection in neural networks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=H1VGkIxRZ
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
R. Matinnejad, S. Nejati, L. C. Briand, and T. Bruckmann, “Test generation and test prioritization for simulink models with dynamic behavior,” IEEE Transactions on Software Engineering , vol. 45, no. 9, pp. 919–944, 2018
2018
Earlier work this paper cites.
X. Hu, G. Li, X. Xia, D. Lo, S. Lu, and Z. Jin, “Summarizing source code with transferred api knowledge,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
J. Thorne, A. Vlachos, C. Christodoulopoulos, and A. Mittal, “FEVER: a large-scale dataset for fact extraction and VERification,” in Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) . New Orleans, Louisiana: Association for Computational Linguistics, Jun. 2018, pp. 809–819. [Online]. Available: https://aclanthology.org/N18-1074
2018
Earlier work this paper cites.
G. Weiss, Y. Goldberg, and E. Yahav, “On the practical computational power of finite precision RNNs for language recognition,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Melbourne, Australia: Association for Computational Linguistics, Jul. 2018, pp. 740–745. [Online]. Available: https://aclanthology.org/P18-2117
2018
Earlier work this paper cites.
G. Weiss, Y. Goldberg, and E. Yahav, “Extracting automata from recurrent neural networks using queries and counterexamples,” in International Conference on Machine Learning . PMLR, 2018, pp. 5247–5256
2018
Earlier work this paper cites.
J. Kim, R. Feldt, and S. Yoo, “Guiding deep learning system testing using surprise adequacy,” in 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE) . IEEE, 2019, pp. 1039–1049
2019
Earlier work this paper cites.
X. Xie, L. Ma, F. Juefei-Xu, M. Xue, H. Chen, Y. Liu, J. Zhao, B. Li, J. Yin, and S. See, “Deephunter: a coverage-guided fuzz testing framework for deep neural networks,” in Proceedings of the 28th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2019, pp. 146–157
2019
Earlier work this paper cites.
H. Wang, B. Ustun, and F. Calmon, “Repairing without retraining: Avoiding disparate impact with counterfactual distributions,” in International Conference on Machine Learning . PMLR, 2019, pp. 6618–6627
2019
Earlier work this paper cites.
M. Sotoudeh and A. V. Thakur, “Correcting deep neural networks with small, generalizing patches,” in Workshop on Safety and Robustness in Decision Making , 2019
2019
Earlier work this paper cites.
H. Zhang and W. Chan, “Apricot: A weight-adaptation approach to fixing deep learning models,” in 2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE) . IEEE, 2019, pp. 376–387
2019
Earlier work this paper cites.
X. Du, X. Xie, Y. Li, L. Ma, Y. Liu, and J. Zhao, “Deepstellar: Model-based quantitative analysis of stateful deep learning systems,” in Proceedings of the 2019 27th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2019, pp. 477–487
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) . Minneapolis, Minnesota: Association for Computational Linguistics, Jun. 2019, pp. 4171–4186. [Online]. Available: https://aclanthology.org/N19-1423
2019
Earlier work this paper cites.
A. Rohrbach, L. A. Hendricks, K. Burns, T. Darrell, and K. Saenko, “Object hallucination in image captioning,” 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
A. Balakrishnan, J. Rao, K. Upasani, M. White, and R. Subba, “Constrained decoding for neural NLG from compositional representations in task-oriented dialogue,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics . Florence, Italy: Association for Computational Linguistics, Jul. 2019, pp. 831–844. [Online]. Available: https://www.aclweb.org/anthology/P19-1080
2019
Earlier work this paper cites.
E. Ozen and A. Orailoglu, “Sanity-check: Boosting the reliability of safety-critical deep neural network applications,” in 2019 IEEE 28th Asian Test Symposium (ATS) . IEEE, 2019, pp. 7–75
2019
Earlier work this paper cites.
B. G. Vegetabile, S. A. Stout-Oswald, E. P. Davis, T. Z. Baram, and H. S. Stern, “Estimating the entropy rate of finite markov chains with application to behavior studies,” Journal of Educational and Behavioral Statistics , vol. 44, no. 3, pp. 282–308, 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C.-H. Cheng, G. Nührenberg, and H. Yasuoka, “Runtime monitoring neuron activation patterns,” in 2019 Design, Automation & Test in Europe Conference & Exhibition (DATE) . IEEE, 2019, pp. 300–303
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
W. Merrill, “Sequential neural networks as automata,” in Proceedings of the Workshop on Deep Learning and Formal Languages: Building Bridges . Florence: Association for Computational Linguistics, Aug. 2019, pp. 1–13. [Online]. Available: https://aclanthology.org/W19-3901
2019
Earlier work this paper cites.
——, “Learning deterministic weighted automata with queries and counterexamples,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
S. Ayache, R. Eyraud, and N. Goudian, “Explaining black boxes on sequential data using weighted automata,” in International Conference on Grammatical Inference . PMLR, 2019, pp. 81–103
2019
Earlier work this paper cites.
B. Wei, G. Li, X. Xia, Z. Fu, and Z. Jin, “Code generation as a dual task of code summarization,” Advances in neural information processing systems , vol. 32, 2019
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
A. Stocco, M. Weiss, M. Calzana, and P. Tonella, “Misbehaviour prediction for autonomous driving systems,” in Proceedings of the ACM/IEEE 42nd international conference on software engineering , 2020, pp. 359–371
2020
Earlier work this paper cites.
J. Zhou, F. Li, J. Dong, H. Zhang, and D. Hao, “Cost-effective testing of a deep learning model through input reduction,” in 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE) , 2020, pp. 289–300
2020
Earlier work this paper cites.
G. Dong, J. Wang, J. Sun, Y. Zhang, X. Wang, T. Dai, J. S. Dong, and X. Wang, “Towards interpreting recurrent neural networks through probabilistic abstraction,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 499–510
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 7871–7880. [Online]. Available: https://aclanthology.org/2020.acl-main.703
2020
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research , vol. 21, no. 1, pp. 5485–5551, 2020
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell et al. , “Language models are few-shot learners,” Advances in neural information processing systems , vol. 33, pp. 1877–1901, 2020
2020
Earlier work this paper cites.
A. Kamath, R. Jia, and P. Liang, “Selective question answering under domain shift,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics . Online: Association for Computational Linguistics, Jul. 2020, pp. 5684–5696. [Online]. Available: https://aclanthology.org/2020.acl-main.503
2020
Earlier work this paper cites.
X. Du, Y. Li, X. Xie, L. Ma, Y. Liu, and J. Zhao, “Marble: model-based robustness analysis of stateful deep learning systems,” in Proceedings of the 35th IEEE/ACM International Conference on Automated Software Engineering , 2020, pp. 423–435
2020
Earlier work this paper cites.
S. Gehman, S. Gururangan, M. Sap, Y. Choi, and N. A. Smith, “RealToxicityPrompts: Evaluating neural toxic degeneration in language models,” in Findings of the Association for Computational Linguistics: EMNLP 2020 . Online: Association for Computational Linguistics, Nov. 2020, pp. 3356–3369. [Online]. Available: https://aclanthology.org/2020.findings-emnlp.301
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
A. Svyatkovskiy, S. K. Deng, S. Fu, and N. Sundaresan, “Intellicode compose: Code generation using transformer,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering , 2020, pp. 1433–1443
2020
Cited alongside, same era.
A. Abid, M. Farooqi, and J. Zou, “Persistent anti-muslim bias in large language models,” in Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society , 2021, pp. 298–306
2021
Cited alongside, same era.
B. Yu, H. Qi, Q. Guo, F. Juefei-Xu, X. Xie, L. Ma, and J. Zhao, “Deeprepair: Style-guided repairing for deep neural networks in the real-world operational environment,” IEEE Transactions on Reliability , vol. 71, no. 4, pp. 1401–1416, 2021
2021
Z. Wei, H. Wang, I. Ashraf, and W.-K. Chan, “Deeppatch: Maintaining deep learning model programs to retain standard accuracy with substantial robustness improvement,” ACM Transactions on Software Engineering and Methodology , 2023
2023
Closest in time.
R. Schumi and J. Sun, “Semantic-based neural network repair,” arXiv preprint arXiv:2306.07995 , 2023
2023
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T. Zohdinasab, V. Riccio, and P. Tonella, “Deepatash: Focused test generation for deep learning systems,” 2023
2023
Closest in time.
T. Zohdinasab, V. Riccio, A. Gambi, and P. Tonella, “Efficient and effective feature space exploration for testing deep learning systems,” ACM Trans. Softw. Eng. Methodol. , vol. 32, no. 2, mar 2023. [Online]. Available: https://doi.org/10.1145/3544792
2023
Closest in time.
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alphaXiv is searching for related work…
Cited alongside, same era.
X. Xie, W. Guo, L. Ma, W. Le, J. Wang, L. Zhou, Y. Liu, and X. Xing, “Rnnrepair: Automatic rnn repair via model-based analysis,” in International Conference on Machine Learning . PMLR, 2021, pp. 11 383–11 392
2021
Cited alongside, same era.
N. Humbatova, G. Jahangirova, and P. Tonella, “Deepcrime: mutation testing of deep learning systems based on real faults,” in Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2021, pp. 67–78
2021
Cited alongside, same era.
T. Zohdinasab, V. Riccio, A. Gambi, and P. Tonella, “Deephyperion: exploring the feature space of deep learning-based systems through illumination search,” in Proceedings of the 30th ACM SIGSOFT International Symposium on Software Testing and Analysis , 2021, pp. 79–90
2021
Cited alongside, same era.
I. Khmelnitsky, D. Neider, R. Roy, X. Xie, B. Barbot, B. Bollig, A. Finkel, S. Haddad, M. Leucker, and L. Ye, “Property-directed verification and robustness certification of recurrent neural networks,” in Automated Technology for Verification and Analysis: 19th International Symposium, ATVA 2021, Gold Coast, QLD, Australia, October 18–22, 2021, Proceedings 19 . Springer, 2021, pp. 364–380
2021
Cited alongside, same era.
H. Chefer, S. Gur, and L. Wolf, “Generic attention-model explainability for interpreting bi-modal and encoder-decoder transformers,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 397–406
2021
Cited alongside, same era.
U. Arora, W. Huang, and H. He, “Types of out-of-distribution texts and how to detect them,” in Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing . Online and Punta Cana, Dominican Republic: Association for Computational Linguistics, Nov. 2021, pp. 10 687–10 701. [Online]. Available: https://aclanthology.org/2021.emnlp-main.835
2021
Cited alongside, same era.
B. Wang, C. Xu, S. Wang, Z. Gan, Y. Cheng, J. Gao, A. H. Awadallah, and B. Li, “Adversarial glue: A multi-task benchmark for robustness evaluation of language models,” in Advances in Neural Information Processing Systems , 2021
2021
Cited alongside, same era.
V. Raunak, A. Menezes, and M. Junczys-Dowmunt, “The curious case of hallucinations in neural machine translation,” 2021
2021
Cited alongside, same era.
N. Humbatova, G. Jahangirova, and P. Tonella, “Deepcrime: from real faults to mutation testing tool for deep learning,” in 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) , 2023, pp. 68–72
2023
Closest in time.
J. Kim, G. An, R. Feldt, and S. Yoo, “Learning test-mutant relationship for accurate fault localisation,” Information and Software Technology , p. 107272, 2023
2023
Closest in time.
J. Sohn, S. Kang, and S. Yoo, “Arachne: Search-based repair of deep neural networks,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 4, pp. 1–26, 2023
2023
Closest in time.
J. Kim, N. Humbatova, G. Jahangirova, P. Tonella, and S. Yoo, “Repairing dnn architecture: Are we there yet?” in 2023 IEEE Conference on Software Testing, Verification and Validation (ICST) , 2023, pp. 234–245
2023
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X. Ren, Y. Lin, Y. Xue, R. Liu, J. Sun, Z. Feng, and J. S. Dong, “Deeparc: Modularizing neural networks for the model maintenance,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) , 2023, pp. 1008–1019
2023
Closest in time.
J. Song, X. Xie, and L. Ma, “Siege: A semantics-guided safety enhancement framework for ai-enabled cyber-physical systems,” IEEE Transactions on Software Engineering , 2023
2023
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2023
Closest in time.
H. Qi, Z. Wang, Q. Guo, J. Chen, F. Juefei-Xu, F. Zhang, L. Ma, and J. Zhao, “Archrepair: Block-level architecture-oriented repairing for deep neural networks,” ACM Transactions on Software Engineering and Methodology , vol. 32, no. 5, pp. 1–31, 2023
2023
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” 2023
2023
Closest in time.
X. Hou, Y. Zhao, Y. Liu, Z. Yang, K. Wang, L. Li, X. Luo, D. Lo, J. Grundy, and H. Wang, “Large language models for software engineering: A systematic literature review,” 2023
2023
Closest in time.
Y. Charalambous, N. Tihanyi, R. Jain, Y. Sun, M. A. Ferrag, and L. C. Cordeiro, “A new era in software security: Towards self-healing software via large language models and formal verification,” 2023
2023
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D. Lo, “Trustworthy and synergistic artificial intelligence for software engineering: Vision and roadmaps,” 2023
2023
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2023
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Y. Shen, L. Heacock, J. Elias, K. D. Hentel, B. Reig, G. Shih, and L. Moy, “Chatgpt and other large language models are double-edged swords,” p. e230163, 2023
2023
Closest in time.
2023
Closest in time.
E. Kasneci, K. Seßler, S. Küchemann, M. Bannert, D. Dementieva, F. Fischer, U. Gasser, G. Groh, S. Günnemann, E. Hüllermeier et al. , “Chatgpt for good? on opportunities and challenges of large language models for education,” Learning and individual differences , vol. 103, p. 102274, 2023
2023
Closest in time.
2023
Closest in time.
2023
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2023
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Y. Tay, M. Dehghani, V. Q. Tran, X. Garcia, J. Wei, X. Wang, H. W. Chung, D. Bahri, T. Schuster, S. Zheng, D. Zhou, N. Houlsby, and D. Metzler, “UL2: Unifying language learning paradigms,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=6ruVLB727MC
2023
Closest in time.
2023
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2023
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2023
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2023
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J. Ren, J. Luo, Y. Zhao, K. Krishna, M. Saleh, B. Lakshminarayanan, and P. J. Liu, “Out-of-distribution detection and selective generation for conditional language models,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=kJUS5nD0vPB
2023
Closest in time.
2023
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S. Goyal, S. Doddapaneni, M. M. Khapra, and B. Ravindran, “A survey of adversarial defenses and robustness in nlp,” ACM Computing Surveys , vol. 55, no. 14s, pp. 1–39, 2023
2023
Closest in time.
“Countable-state markov chains,” https://ocw.mit.edu/courses/6-262-discrete-stochastic-processes-spring-2011/01d0892549619cb25d928f15ec7230ed_MIT6_262S11_chap05.pdf , 2023
2023
Closest in time.
Y. Ishimoto, M. Kondo, N. Ubayashi, and Y. Kamei, “Pafl: Probabilistic automaton-based fault localization for recurrent neural networks,” Information and Software Technology , vol. 155, p. 107117, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Wang, Y. Kordi, S. Mishra, A. Liu, N. A. Smith, D. Khashabi, and H. Hajishirzi, “Self-instruct: Aligning language models with self-generated instructions,” 2023
2023
Closest in time.
W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J. E. Gonzalez, I. Stoica, and E. P. Xing, “Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality,” March 2023. [Online]. Available: https://lmsys.org/blog/2023-03-30-vicuna/
2023
Closest in time.
R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model,” https://github.com/tatsu-lab/stanford_alpaca , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
“Gpt 3.5,” https://platform.openai.com/docs/models/gpt-3-5 , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Y. Wu, Z. Li, J. M. Zhang, M. Papadakis, M. Harman, and Y. Liu, “Large language models in fault localisation,” 2023
2023
Closest in time.
D. Huang, Q. Bu, J. Zhang, X. Xie, J. Chen, and H. Cui, “Bias assessment and mitigation in llm-based code generation,” 2023
2023
Closest in time.
X. Song, Y. Sun, M. A. Mustafa, and L. C. Cordeiro, “Airepair: A repair platform for neural networks,” in 2023 IEEE/ACM 45th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) . IEEE, 2023, pp. 98–101
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
T. Markov, C. Zhang, S. Agarwal, F. E. Nekoul, T. Lee, S. Adler, A. Jiang, and L. Weng, “A holistic approach to undesired content detection in the real world,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 12, 2023, pp. 15 009–15 018
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Kuhn, Y. Gal, and S. Farquhar, “Semantic uncertainty: Linguistic invariances for uncertainty estimation in natural language generation,” in The Eleventh International Conference on Learning Representations , 2023. [Online]. Available: https://openreview.net/forum?id=VD-AYtP0dve
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Z. Wang, Y. Huang, D. Song, L. Ma, and T. Zhang, “Deepseer: Interactive rnn explanation and debugging via state abstraction,” in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems , 2023, pp. 1–20
2023
Closest in time.
2023
Closest in time.
M. Kuchnik, V. Smith, and G. Amvrosiadis, “Validating large language models with relm,” Proceedings of Machine Learning and Systems , vol. 5, 2023
2023
Closest in time.
E. Nijkamp, B. Pang, H. Hayashi, L. Tu, H. Wang, Y. Zhou, S. Savarese, and C. Xiong, “Codegen: An open large language model for code with multi-turn program synthesis,” 2023
2023
Closest in time.
2023
Closest in time.
T. Ahmed and P. Devanbu, “Few-shot training llms for project-specific code-summarization,” in Proceedings of the 37th IEEE/ACM International Conference on Automated Software Engineering , ser. ASE ’22. New York, NY, USA: Association for Computing Machinery, 2023. [Online]. Available: https://doi.org/10.1145/3551349.3559555
2023
Closest in time.
Z. Liu, C. Chen, J. Wang, X. Che, Y. Huang, J. Hu, and Q. Wang, “Fill in the blank: Context-aware automated text input generation for mobile gui testing,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1355–1367
2023
Closest in time.
C. Lemieux, J. P. Inala, S. K. Lahiri, and S. Sen, “Codamosa: Escaping coverage plateaus in test generation with pre-trained large language models,” in International conference on software engineering (ICSE) , 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
N. Jiang, K. Liu, T. Lutellier, and L. Tan, “Impact of code language models on automated program repair,” in Proceedings of the International Conference on Software Engineering (ICSE) . IEEE/ACM, 2023
2023
Closest in time.
C. S. Xia, Y. Wei, and L. Zhang, “Automated program repair in the era of large pre-trained language models,” in Proceedings of the 45th International Conference on Software Engineering (ICSE 2023). Association for Computing Machinery , 2023
2023
Closest in time.
Z. Fan, X. Gao, M. Mirchev, A. Roychoudhury, and S. H. Tan, “Automated repair of programs from large language models,” in 2023 IEEE/ACM 45th International Conference on Software Engineering (ICSE) . IEEE, 2023, pp. 1469–1481
2023
Closest in time.
Y. Liu, T. Le-Cong, R. Widyasari, C. Tantithamthavorn, L. Li, X.-B. D. Le, and D. Lo, “Refining chatgpt-generated code: Characterizing and mitigating code quality issues,” 2023
2023
Closest in time.
2023
Closest in time.
R. Meng, M. Mirchev, M. Böhme, and A. Roychoudhury, “Large language model guided protocol fuzzing,” in Proceedings of the Network and Distributed System Security Symposium (NDSS) , 2024
2024
Closest in time.