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Machine learning systems typically apply the same model to both easy and tough cases.
Space/time trade-offs in hash coding with allowable errors
Bloom, B. H · 1970
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Attention and cognitive control
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Controlled and automatic human information processing: Ii. perceptual learning, automatic attending and a general theory
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Heuristic and analytic processes in reasoning*
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Individual differences in reasoning: Implications for the rationality debate?
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Friends and neighbors on the web
Adamic, L. A. and Adar, E · 2003
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Maps of Bounded Rationality: Psychology for Behavioral Economics
Kahneman, D · 2003
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Network applications of bloom filters: A survey
Broder, A. and Mitzenmacher, M · 2004
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Approximate caches for packet classification
Chang, F., Feng, W.-c., and Li, K · 2004
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The political blogosphere and the 2004 us election: divided they blog
Adamic, L. A. and Glance, N · 2005
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Scalable bloom filters
Almeida, P. S., Baquero, C., Preguiça, N., and Hutchison, D · 2007
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Automating the Construction of Internet Portals with Machine Learning
Mccallum, A. K · 2008
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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Grounded Language Learning Fast and Slow
Hill, F., Tieleman, O., von Glehn, T., Wong, N., Merzic, H., and Clark, S · 2009
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A new analysis of the false positive rate of a bloom filter
Christensen, K., Roginsky, A., and Jimeno, M · 2010
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Dual-Process and Dual-System Theories of Reasoning
Frankish, K · 2010
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Thinking, fast and slow
Kahneman, D · 2011
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Stochastic blockmodels and community structure in networks
Karrer, B. and Newman, M. E · 2011
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Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E · 2011
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Finding a” kneedle” in a haystack: Detecting knee points in system behavior
Satopaa, V., Albrecht, J., Irwin, D., and Raghavan, B · 2011
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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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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NeVAE: A Deep Generative Model for Molecular Graphs
Samanta, B., De, A., Jana, G., Chattaraj, P. K., Ganguly, N., and Rodriguez, M. G · 2019
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Scaffold-based molecular design with a graph generative model
Lim, J., Hwang, S.-Y., Moon, S., Kim, S., and Youn Kim, W · 2020
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Fastbert: a self-distilling bert with adaptive inference time
Liu, W., Zhou, P., Wang, Z., Zhao, Z., Deng, H., and Ju, Q · 2020
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Romebert: Robust training of multi-exit bert
Geng, S., Gao, P., Fu, Z., and Zhang, Y · 2021
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How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering
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node2vec: Scalable Feature Learning for Networks
Grover, A. and Leskovec, J · 2016
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Variational graph auto-encoders
Kipf, T. N. and Welling, M · 2016
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Thinking Fast and Slow with Deep Learning and Tree Search
Anthony, T., Tian, Z., and Barber, D · 2017
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Designing data-intensive applications: The big ideas behind reliable, scalable, and maintainable systems
Kleppmann, M · 2017
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Fast-Slow Recurrent Neural Networks
Mujika, A., Meier, F., and Steger, A · 2017
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Attention is all you need
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Jiang, Z., Araki, J., Ding, H., and Neubig, G · 2021
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Neural language modeling for contextualized temporal graph generation
Madaan, A. and Yang, Y · 2021
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Think about it! improving defeasible reasoning by first modeling the question scenario
Madaan, A., Tandon, N., Rajagopal, D., Clark, P., Yang, Y., and Hovy, E · 2021
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HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps
Mi, L., Zhao, H., Nash, C., Jin, X., Gao, J., Sun, C., Schmid, C., Shavit, N., Chai, Y., and Anguelov, D · 2021
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ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning
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proScript: Partially Ordered Scripts Generation
Sakaguchi, K., Bhagavatula, C., Le Bras, R., Tandon, N., Clark, P., and Choi, Y · 2021
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Consistent accelerated inference via confident adaptive transformers
Schuster, T., Fisch, A., Jaakkola, T., and Barzilay, R · 2021
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Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks
Schwarzschild, A., Borgnia, E., Gupta, A., Huang, F., Vishkin, U., Goldblum, M., and Goldstein, T · 2021
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Confident adaptive language modeling
Schuster, T., Fisch, A., Gupta, J., Dehghani, M., Bahri, D., Tran, V. Q., Tay, Y., and Metzler, D · 2022
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