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We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data.
Sentence-bert: Sentence embeddings using siamese bert-networks
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XGBoost: A scalable tree boosting system
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Deep residual learning for image recognition
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WaveNet: A generative model for raw audio
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Neural message passing for quantum chemistry
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Lightgbm: A highly efficient gradient boosting decision tree
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Semi-supervised classification with graph convolutional networks
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Bert: Pre-training of deep bidirectional transformers for language understanding
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Catboost: unbiased boosting with categorical features
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A · 2018
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Optuna: A next-generation hyperparameter optimization framework
Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M · 2019
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Fast graph representation learning with PyTorch Geometric
Fey, M. and Lenssen, J. E · 2019
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Roberta: A robustly optimized bert pretraining approach
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., and Stoyanov, V · 2019
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Ludwig: a type-based declarative deep learning toolbox
Molino, P., Dudin, Y., and Miryala, S. S · 2019
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Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al · 2020
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Electra: Pre-training text encoders as discriminators rather than generators
Boost then convolve: Gradient boosting meets graph neural networks
Ivanov, S. and Prokhorenkova, L · 2021
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Pytorch tabular: A framework for deep learning with tabular data
Joseph, M · 2021
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Benchmarking multimodal automl for tabular data with text fields
Shi, X., Mueller, J., Erickson, N., Li, M., and Smola, A. J · 2021
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Saint: Improved neural networks for tabular data via row attention and contrastive pre-training
Somepalli, G., Goldblum, M., Schwarzschild, A., Bruss, C. B., and Goldstein, T · 2021
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On embeddings for numerical features in tabular deep learning
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Clark, K., Luong, M.-T., Le, Q. V., and Manning, C. D · 2020
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Array programming with numpy
Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
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Open graph benchmark: Datasets for machine learning on graphs
Hu, W., Fey, M., Zitnik, M., Dong, Y., Ren, H., Liu, B., Catasta, M., and Leskovec, J · 2020
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TabTransformer: Tabular data modeling using contextual embeddings
Huang, X., Khetan, A., Cvitkovic, M., and Karnin, Z · 2020
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Neural oblivious decision ensembles for deep learning on tabular data
Popov, S., Morozov, S., and Babenko, A · 2020
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Masked label prediction: Unified message passing model for semi-supervised classification
Shi, Y., Huang, Z., Wang, W., Zhong, H., Feng, S., and Sun, Y · 2020
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Dnf-net: A neural architecture for tabular data
Abutbul, A., Elidan, G., Katzir, L., and El-Yaniv, R · 2021
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TabNet: Attentive interpretable tabular learning
Arik Sercan O., Pfister, T · 2021
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Gorishniy, Y., Ivan, R., and Babenko, A · 2022
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Why do tree-based models still outperform deep learning on typical tabular data?
Grinsztajn, L., Oyallon, E., and Varoquaux, G · 2022
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Learning backward compatible embeddings
Hu, W., Bansal, R., Cao, K., Rao, N., Subbian, K., and Leskovec, J · 2022
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Text and code embeddings by contrastive pre-training
Neelakantan, A., Xu, T., Puri, R., Radford, A., Han, J. M., Tworek, J., Yuan, Q., Tezak, N., Kim, J. W., Hallacy, C., et al · 2022
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Tabular data: Deep learning is not all you need
Shwartz-Ziv, R. and Armon, A · 2022
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Relational deep learning: Graph representation learning on relational databases
Fey, M., Hu, W., Huang, K., Lenssen, J. E., Ranjan, R., Robinson, J., Ying, R., You, J., and Leskovec, J · 2023
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Xtab: Cross-table pretraining for tabular transformers
Zhu, B., Shi, X., Erickson, N., Li, M., Karypis, G., and Shoaran, M · 2023
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Tabr: Tabular deep learning meets nearest neighbors
Gorishniy, Y., Rubachev, I., Kartashev, N., Shlenskii, D., Kotelnikov, A., and Babenko, A · 2024
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