List of papers we cover during our weekly paper reading session. For past links/notes, check out the (private) wiki.
This README is automatically generated, don’t edit it by hand. You will need
Emacs and Cask to generate this. Once installed, run cask install to setup
dependencies. To add new entries, put them in library.bib file under appropriate
section and run make.
- Carbonell, J. G., Learning by analogy: formulating and generalizing plans from past experience, In (Eds.), Machine learning (pp. 137–161) (1983). : Springer. (cite:carbonell1983learning)
- Mohri, M., Pereira, F., & Riley, M., Weighted finite-state transducers in speech recognition, Computer Speech \& Language, 16(1), 69–88 (2002). (cite:mohri2002weighted)
- Ueffing, N., Bisani, M., & Vozila, P., Improved models for automatic punctuation prediction for spoken and written text., In , Interspeech (pp. 3097–3101) (2013). : . (cite:ueffing2013improved)
- Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X., Large-scale long-tailed recognition in an open world, arXiv preprint arXiv:1904.05160, (), (2019). (cite:liu2019large)
- Iyer, A., Jonnalagedda, M., Parthasarathy, S., Radhakrishna, A., & Rajamani, S. K., Synthesis and machine learning for heterogeneous extraction, In , Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation (pp. 301–315) (2019). : . (cite:iyer2019synthesis)
- Dehak, N., Kenny, P. J., Dehak, R'eda, Dumouchel, P., & Ouellet, P., Front-end factor analysis for speaker verification, IEEE Transactions on Audio, Speech, and Language Processing, 19(4), 788–798 (2010). (cite:dehak2010front)
- Dehak, N., Dehak, R., Kenny, P., Br"ummer, Niko, Ouellet, P., & Dumouchel, P., Support vector machines versus fast scoring in the low-dimensional total variability space for speaker verification, In , Tenth Annual conference of the international speech communication association (pp. ) (2009). : . (cite:dehak2009support)
- Sutton, C., & McCallum, A., An introduction to conditional random fields for relational learning, In (Eds.), Introduction to Statistical Relational Learning (pp. ) (2006). : . (cite:sutton06introduction)
- Mendis, C., Droppo, J., Maleki, S., Musuvathi, M., Mytkowicz, T., & Zweig, G., Parallelizing wfst speech decoders, In , 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 5325–5329) (2016). : . (cite:mendis2016parallelizing)
- Russo, D. J., Van Roy, B., Kazerouni, A., Osband, I., Wen, Z., & others, , A tutorial on thompson sampling, Foundations and Trends{\textregistered} in Machine Learning, 11(1), 1–96 (2018). (cite:russo2018tutorial)
- Gravano, A., Jansche, M., & Bacchiani, M., Restoring punctuation and capitalization in transcribed speech, In , 2009 IEEE International Conference on Acoustics, Speech and Signal Processing (pp. 4741–4744) (2009). : . (cite:gravano2009restoring)
- Mintz, M., Bills, S., Snow, R., & Jurafsky, D., Distant supervision for relation extraction without labeled data, In , Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 2-Volume 2 (pp. 1003–1011) (2009). : . (cite:mintz2009distant)
- Beygelzimer, A., Daum'e, Hal, Langford, J., & Mineiro, P., Learning reductions that really work, Proceedings of the IEEE, 104(1), 136–147 (2016). (cite:beygelzimer2016learning)
- Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., …, Hidden technical debt in machine learning systems, In , Advances in neural information processing systems (pp. 2503–2511) (2015). : . (cite:sculley2015hidden)
- Wu, Y., Schuster, M., Chen, Z., Le, Q. V., Norouzi, M., Macherey, W., Krikun, M., …, Google’s neural machine translation system: bridging the gap between human and machine translation, arXiv preprint arXiv:1609.08144, (), (2016). (cite:wu2016google)
- Ghahramani, Z., Unsupervised learning, In , Summer School on Machine Learning (pp. 72–112) (2003). : . (cite:ghahramani2003unsupervised)
- Hundman, K., Constantinou, V., Laporte, C., Colwell, I., & Soderstrom, T., Detecting spacecraft anomalies using lstms and nonparametric dynamic thresholding, In , Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining (pp. 387–395) (2018). : . (cite:hundman2018detecting)