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Deep learning (DL) has revolutionized areas such as computer vision, natural language processing, and more.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Adam: A method for stochastic optimization
Diederik, P. K · 2014
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Xgboost: A scalable tree boosting system
Chen, T. and Guestrin, C · 2016
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Deep learning and its applications: A review
Hordri, N. F., Yuhaniz, S. S., and Shamsuddin, S. M · 2016
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Measuring inter-rater reliability for nominal data–which coefficients and confidence intervals are appropriate?
Zapf, A., Castell, S., Morawietz, L., and Karch, A · 2016
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Deep learning for time-series analysis
Gamboa, J. C. B · 2017
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Software engineering challenges of deep learning
Arpteg, A., Brinne, B., Crnkovic-Friis, L., and Bosch, J · 2018
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Deep learning in agriculture: A survey
Kamilaris, A. and Prenafeta-Boldú, F. X · 2018
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Neural network methods for natural language processing, 2018
Liu, Y. and Zhang, M · 2018
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Yu, T., Zhang, R., Yang, K., Yasunaga, M., Wang, D., Li, Z., Ma, J., Li, I., Yao, Q., Roman, S., et al · 2018
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JuICe: A large scale distantly supervised dataset for open domain context-based code generation
Agashe, R., Iyer, S., and Zettlemoyer, L · 2019
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Searching for mobilenetv3
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al · 2019
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A comprehensive study on deep learning bug characteristics
Islam, M. J., Nguyen, G., Pan, R., and Rajan, H · 2019
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Panoptic feature pyramid networks
Kirillov, A., Girshick, R., He, K., and Dollár, P · 2019
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Does bleu score work for code migration?
Tran, N., Tran, H., Nguyen, S., Nguyen, H., and Nguyen, T · 2019
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Codebert: A pre-trained model for programming and natural languages
Feng, Z., Guo, D., Tang, D., Duan, N., Feng, X., Gong, M., Shou, L., Qin, B., Liu, T., Jiang, D., et al · 2020
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Explainable deep convolutional learning for intuitive model development by non–machine learning domain experts
Singaravel, S., Suykens, J., Janssen, H., and Geyer, P · 2020
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Time series data augmentation for deep learning: A survey
Wen, Q., Sun, L., Yang, F., Song, X., Gao, J., Wang, X., and Xu, H · 2020
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A comprehensive survey on graph neural networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., and Philip, S. Y · 2020
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Program synthesis with large language models
Austin, J., Odena, A., Nye, M., Bosma, M., Michalewski, H., Dohan, D., Jiang, E., Cai, C. J., Terry, M., Le, Q. V., and Sutton, C · 2021
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Repository-level prompt generation for large language models of code
Shrivastava, D., Larochelle, H., and Tarlow, D · 2023
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A survey on deep learning based segmentation, detection and classification for 3d point clouds
Vinodkumar, P. K., Karabulut, D., Avots, E., Ozcinar, C., and Anbarjafari, G · 2023
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Repocoder: Repository-level code completion through iterative retrieval and generation
Zhang, F., Chen, B., Zhang, Y., Keung, J., Liu, J., Zan, D., Mao, Y., Lou, J.-G., and Chen, W · 2023
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Unraveling code clone dynamics in deep learning frameworks
Assi, M., Hassan, S., and Zou, Y · 2024
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Mle-bench: Evaluating machine learning agents on machine learning engineering
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Chen, M., Tworek, J., Jun, H., Yuan, Q., Pondé, H., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., Ray, A., Puri, R., Krueger, G., Petrov, M., Khlaaf, H., Sastry, G., Mishkin, P., Chan, B., Gray, S., Ryder, N., Pavlov, M., Power, A., Kaiser, L., Bavarian, M., Winter, C., Tillet, P., Such, F. P., Cummings, D. W., Plappert, M., Chantzis, F., Barnes, E., Herbert-Voss, A., Guss, W. H., Nichol, A., Babuschkin, I., Balaji, S., Jain, S., Carr, A., Leike, J., Achiam, J., Misra, V., Morikawa, E., Radford, A., Knight, M. M., Brundage, M., Murati, M., Mayer, K., Welinder, P., McGrew, B., Amodei, D., McCandlish, S., Sutskever, I., and Zaremba, W · 2021
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Codexglue: A machine learning benchmark dataset for code understanding and generation
Lu, S., Guo, D., Ren, S., Huang, J., Svyatkovskiy, A., Blanco, A., Clement, C., Drain, D., Jiang, D., Tang, D., et al · 2021
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Automating test case identification in java open source projects on github
Madeja, M., Porubän, J., Bačíková, M., Sulír, M., Juhár, J., Chodarev, S., and Gurbál’, F · 2021
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Facilitating knowledge sharing from domain experts to data scientists for building nlp models
Park, S., Wang, A. Y., Kawas, B., Liao, Q. V., Piorkowski, D., and Danilevsky, M · 2021
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Machine learning: Algorithms, real-world applications and research directions
Sarker, I. H · 2021
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Wang, Y., Wang, W., Joty, S., and Hoi, S. C · 2021
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Multipl-e: A scalable and extensible approach to benchmarking neural code generation
Cassano, F., Gouwar, J., Nguyen, D., Nguyen, S., Phipps-Costin, L., Pinckney, D., Yee, M.-H., Zi, Y., Anderson, C. J., Feldman, M. Q., et al · 2022
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Aixbench: A code generation benchmark dataset
Hao, Y., Li, G., Liu, Y., Miao, X., Zong, H., Jiang, S., Liu, Y., and Wei, H · 2022
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Chan, J. S., Chowdhury, N., Jaffe, O., Aung, J., Sherburn, D., Mays, E., Starace, G., Liu, K., Maksin, L., Patwardhan, T., et al · 2024
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Using prompts to guide large language models in imitating a real person’s language style
Chen, Z. and Moscholios, S · 2024
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Evaluating large language models in class-level code generation
Du, X., Liu, M., Wang, K., Wang, H., Liu, J., Chen, Y., Feng, J., Sha, C., Peng, X., and Lou, Y · 2024
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Top pass: Improve code generation by pass@ k-maximized code ranking
Lyu, Z.-C., Li, X.-Y., Xie, Z., and Li, M · 2024
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A review of machine learning and deep learning for object detection, semantic segmentation, and human action recognition in machine and robotic vision
Manakitsa, N., Maraslidis, G. S., Moysis, L., and Fragulis, G. F · 2024
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Using an llm to help with code understanding
Nam, D., Macvean, A., Hellendoorn, V., Vasilescu, B., and Myers, B · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Sahoo, P., Singh, A. K., Saha, S., Jain, V., Mondal, S., and Chadha, A · 2024
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The good, the bad, and the greedy: Evaluation of llms should not ignore non-determinism
Song, Y., Wang, G., Li, S., and Lin, B. Y · 2024
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Bugs in large language models generated code
Tambon, F., Dakhel, A. M., Nikanjam, A., Khomh, F., Desmarais, M. C., and Antoniol, G · 2024
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Frontiers of deep learning: From novel application to real-world deployment
Xie, R · 2024
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Codereval: A benchmark of pragmatic code generation with generative pre-trained models
Yu, H., Shen, B., Ran, D., Zhang, J., Zhang, Q., Ma, Y., Liang, G., Li, Y., Wang, Q., and Xie, T · 2024
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Bigcodebench: Benchmarking code generation with diverse function calls and complex instructions
Zhuo, T. Y., Vu, M. C., Chim, J., Hu, H., Yu, W., Widyasari, R., Yusuf, I. N. B., Zhan, H., He, J., Paul, I., et al · 2024
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