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The remarkable success of modern machine learning models on large datasets often demands extensive training time and resource consumption.
An analysis of approximations for maximizing submodular set functions - I
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Visualizing data using t-sne
van der Maaten, L. and Hinton, G · 2008
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Curriculum learning
Bengio, Y., Louradour, J., Collobert, R., and Weston, J · 2009
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Learning multiple layers of features from tiny images
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A new approach to cross-modal multimedia retrieval
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Determinantal point processes for machine learning
Kulesza, A., Taskar, B., et al · 2012
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Learning with submodular functions: A convex optimization perspective
Bach, F. R · 2013
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Self-paced learning with diversity
Jiang, L., Meng, D., Yu, S., Lan, Z., Shan, S., and Hauptmann, A. G · 2014
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Variance reduction in sgd by distributed importance sampling
Alain, G., Lamb, A., Sankar, C., Courville, A., and Bengio, Y · 2015
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Self-paced curriculum learning
Jiang, L., Meng, D., Zhao, Q., Shan, S., and Hauptmann, A. G · 2015
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Tiny imagenet visual recognition challenge
Le, Y. and Yang, X. S · 2015
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Online batch selection for faster training of neural networks
Loshchilov, I. and Hutter, F · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Submodularity in data subset selection and active learning
Wei, K., Iyer, R. K., and Bilmes, J. A · 2015
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An empirical evaluation of doc2vec with practical insights into document embedding generation
Lau, J. H. and Baldwin, T · 2016
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Rethinking atrous convolution for semantic image segmentation
Chen, L., Papandreou, G., Schroff, F., and Adam, H · 2017
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Mobilenets: Efficient convolutional neural networks for mobile vision applications
Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H · 2017
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In-datacenter performance analysis of a tensor processing unit
Jouppi, N. P., Young, C., Patil, N., Patterson, D., Agrawal, G., Bajwa, R., Bates, S., Bhatia, S., Boden, N., Borchers, A., et al · 2017
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The E2E dataset: New challenges for end-to-end generation
Novikova, J., Dusek, O., and Rieser, V · 2017
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Encoder-decoder with atrous separable convolution for semantic image segmentation
Chen, L., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H · 2018
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Active learning for convolutional neural networks: A core-set approach
Sener, O. and Savarese, S · 2018
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Minimax curriculum learning: Machine teaching with desirable difficulties and scheduled diversity
Zhou, T. and Bilmes, J. A · 2018
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Class-balanced loss based on effective number of samples
Cui, Y., Jia, M., Lin, T., Song, Y., and Belongie, S. J · 2019
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Accelerating deep learning by focusing on the biggest losers
Jiang, A. H., Wong, D. L.-K., Zhou, G., Andersen, D. G., Dean, J., Ganger, G. R., Joshi, G., Kaminsky, M., Kozuch, M. A., Lipton, Z. C., and Pillai, P · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al · 2019
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An empirical study of example forgetting during deep neural network learning
Toneva, M., Sordoni, A., des Combes, R. T., Trischler, A., Bengio, Y., and Gordon, G. J · 2019
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Fedskip: Combatting statistical heterogeneity with federated skip aggregation
Fan, Z., Wang, Y., Yao, J., Lyu, L., Zhang, Y., and Tian, Q · 2022
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Deepcore: A comprehensive library for coreset selection in deep learning
Guo, C., Zhao, B., and Bai, Y · 2022
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Lora: Low-rank adaptation of large language models
Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W · 2022
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Prioritized training on points that are learnable, worth learning, and not yet learnt
Mindermann, S., Brauner, J. M., Razzak, M. T., Sharma, M., Kirsch, A., Xu, W., Höltgen, B., Gomez, A. N., Morisot, A., Farquhar, S., and Gal, Y · 2022
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High-resolution image synthesis with latent diffusion models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., and Ommer, B · 2022
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Contrastive learning with boosted memorization
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Determinantal point processes for coresets
Tremblay, N., Barthelmé, S., and Amblard, P.-O · 2019
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Contextual diversity for active learning
Agarwal, S., Arora, H., Anand, S., and Arora, C · 2020
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Selection via proxy: Efficient data selection for deep learning
Coleman, C., Yeh, C., Mussmann, S., Mirzasoleiman, B., Bailis, P., Liang, P., Leskovec, J., and Zaharia, M · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q · 2020
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Ordered SGD: A new stochastic optimization framework for empirical risk minimization
Kawaguchi, K. and Lu, H · 2020
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Coresets for data-efficient training of machine learning models
Mirzasoleiman, B., Bilmes, J. A., and Leskovec, J · 2020
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Identifying mislabeled data using the area under the margin ranking
Pleiss, G., Zhang, T., Elenberg, E. R., and Weinberger, K. Q · 2020
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Zhou, Z., Yao, J., Wang, Y., Han, B., and Zhang, Y · 2022
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Accelerating batch active learning using continual learning techniques
Das, A. M., Bhatt, G., Bhalerao, M. M., Gao, V. R., Yang, R., and Bilmes, J · 2023
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Towards accelerated model training via bayesian data selection
Deng, Z., Cui, P., and Zhu, J · 2023
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Federated learning with bilateral curation for partially class-disjoint data
Fan, Z., Yao, J., Han, B., Zhang, Y., Wang, Y., et al · 2023
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Long-tailed partial label learning via dynamic rebalancing
Hong, F., Yao, J., Zhou, Z., Zhang, Y., and Wang, Y · 2023
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Segment anything
Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A. C., Lo, W.-Y., Dollar, P., and Girshick, R · 2023
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OpenAI · 2023
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Moderate coreset: A universal method of data selection for real-world data-efficient deep learning
Xia, X., Liu, J., Yu, J., Shen, X., Han, B., and Liu, T · 2023
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Data selection for language models via importance resampling
Xie, S. M., Santurkar, S., Ma, T., and Liang, P · 2023
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Change is hard: A closer look at subpopulation shift
Yang, Y., Zhang, H., Katabi, D., and Ghassemi, M · 2023
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Coverage-centric coreset selection for high pruning rates
Zheng, H., Liu, R., Lai, F., and Prakash, A · 2023
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Combating representation learning disparity with geometric harmonization
Zhou, Z., Yao, J., Hong, F., Zhang, Y., Han, B., and Wang, Y · 2023
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Locally estimated global perturbations are better than local perturbations for federated sharpness-aware minimization
Fan, Z., Hu, S., Yao, J., Niu, G., Zhang, Y., Sugiyama, M., and Wang, Y · 2024
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On harmonizing implicit subpopulations
Hong, F., Yao, J., Lyu, Y., Zhou, Z., Tsang, I., Zhang, Y., and Wang, Y · 2024
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Infobatch: Lossless training speed up by unbiased dynamic data pruning
Qin, Z., Wang, K., Zheng, Z., Gu, J., Peng, X., xu Zhao Pan, Zhou, D., Shang, L., Sun, B., Xie, X., and You, Y · 2024
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Spanning training progress: Temporal dual-depth scoring (tdds) for enhanced dataset pruning
Xin, Z., Jiawei, D., Yunsong, L., Weiying, X., and Zhou, J. T · 2024
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