Merge pull request #4 from kevinli5941/main

Project submission: 2023-11-02-guided-transfer-learning

assets/bibliography/2023-11-02-guided-transfer-learning.bib

@@ -1,7 +1,131 @@
+@article{Nikoli2017,
+  title = {Why deep neural nets cannot ever match biological intelligence and what to do about it?},
+  volume = {14},
+  ISSN = {1751-8520},
+  url = {http://dx.doi.org/10.1007/s11633-017-1093-8},
+  DOI = {10.1007/s11633-017-1093-8},
+  number = {5},
+  journal = {International Journal of Automation and Computing},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Nikolić,  Danko},
+  year = {2017},
+  month = jul,
+  pages = {532–541}
+}
+
 @article{gtl,
   title={Guided Transfer Learning},
   author={Danko Nikolić, Davor Andrić, Vjekoslave Nikolić},
   journal={arXiv preprint, arXiv:2303.16154},
   year={2023},
   url={https://arxiv.org/pdf/2303.16154.pdf}
 }
+
+@inproceedings{NIPS2016_90e13578,
+ author = {Vinyals, Oriol and Blundell, Charles and Lillicrap, Timothy and kavukcuoglu, koray and Wierstra, Daan},
+ booktitle = {Advances in Neural Information Processing Systems},
+ editor = {D. Lee and M. Sugiyama and U. Luxburg and I. Guyon and R. Garnett},
+ pages = {},
+ publisher = {Curran Associates, Inc.},
+ title = {Matching Networks for One Shot Learning},
+ url = {https://proceedings.neurips.cc/paper_files/paper/2016/file/90e1357833654983612fb05e3ec9148c-Paper.pdf},
+ volume = {29},
+ year = {2016}
+}
+
+@misc{woodward2017active,
+      title={Active One-shot Learning}, 
+      author={Mark Woodward and Chelsea Finn},
+      journal={arXiv preprint, arXiv:1702.06559},
+      year={2017},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG}
+}
+
+@article{omniglot,
+author = {Brenden M. Lake  and Ruslan Salakhutdinov  and Joshua B. Tenenbaum },
+title = {Human-level concept learning through probabilistic program induction},
+journal = {Science},
+volume = {350},
+number = {6266},
+pages = {1332-1338},
+year = {2015},
+doi = {10.1126/science.aab3050},
+URL = {https://www.science.org/doi/abs/10.1126/science.aab3050},
+eprint = {https://www.science.org/doi/pdf/10.1126/science.aab3050},
+abstract = {Not only do children learn effortlessly, they do so quickly and with a remarkable ability to use what they have learned as the raw material for creating new stuff. Lake et al. describe a computational model that learns in a similar fashion and does so better than current deep learning algorithms. The model classifies, parses, and recreates handwritten characters, and can generate new letters of the alphabet that look “right” as judged by Turing-like tests of the model's output in comparison to what real humans produce. Science, this issue p. 1332 Combining the capacity to handle noise with probabilistic learning yields humanlike performance in a computational model. People learning new concepts can often generalize successfully from just a single example, yet machine learning algorithms typically require tens or hundreds of examples to perform with similar accuracy. People can also use learned concepts in richer ways than conventional algorithms—for action, imagination, and explanation. We present a computational model that captures these human learning abilities for a large class of simple visual concepts: handwritten characters from the world’s alphabets. The model represents concepts as simple programs that best explain observed examples under a Bayesian criterion. On a challenging one-shot classification task, the model achieves human-level performance while outperforming recent deep learning approaches. We also present several “visual Turing tests” probing the model’s creative generalization abilities, which in many cases are indistinguishable from human behavior.}}
+
+@article{scbert,
+  title = {scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data},
+  volume = {4},
+  ISSN = {2522-5839},
+  url = {http://dx.doi.org/10.1038/s42256-022-00534-z},
+  DOI = {10.1038/s42256-022-00534-z},
+  number = {10},
+  journal = {Nature Machine Intelligence},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Yang,  Fan and Wang,  Wenchuan and Wang,  Fang and Fang,  Yuan and Tang,  Duyu and Huang,  Junzhou and Lu,  Hui and Yao,  Jianhua},
+  year = {2022},
+  month = sep,
+  pages = {852–866}
+}
+
+@article{recount3,
+  title = {recount3: summaries and queries for large-scale RNA-seq expression and splicing},
+  volume = {22},
+  ISSN = {1474-760X},
+  url = {http://dx.doi.org/10.1186/s13059-021-02533-6},
+  DOI = {10.1186/s13059-021-02533-6},
+  number = {1},
+  journal = {Genome Biology},
+  publisher = {Springer Science and Business Media LLC},
+  author = {Wilks,  Christopher and Zheng,  Shijie C. and Chen,  Feng Yong and Charles,  Rone and Solomon,  Brad and Ling,  Jonathan P. and Imada,  Eddie Luidy and Zhang,  David and Joseph,  Lance and Leek,  Jeffrey T. and Jaffe,  Andrew E. and Nellore,  Abhinav and Collado-Torres,  Leonardo and Hansen,  Kasper D. and Langmead,  Ben},
+  year = {2021},
+  month = nov 
+}
+
+@data{OSD-105,
+author = {Galazka J; Globus R},
+publisher = {NASA Open Science Data Repository},
+title = {Rodent Research-1 (RR1) NASA Validation Flight: Mouse tibialis anterior muscle transcriptomic, proteomic, and epigenomic data},
+year = {2017},
+doi = {10.26030/xgw6-6t64},
+url = {https://osdr.nasa.gov/bio/repo/data/studies/OSD-105},
+version = {4}
+}
+
+@data{OSD-104,
+author = {Galazka J; Globus R},
+publisher = {NASA Open Science Data Repository},
+title = {Rodent Research-1 (RR1) NASA Validation Flight: Mouse soleus muscle transcriptomic and epigenomic data},
+year = {2017},
+doi = {10.26030/em9r-w619},
+url = {https://osdr.nasa.gov/bio/repo/data/studies/OSD-104},
+version = {4}
+}
+
+@article{hldsshard,
+  author       = {Liran Shen and
+                  Qingbo Yin},
+  title        = {The classification for High-dimension low-sample size data},
+  journal      = {CoRR},
+  volume       = {abs/2006.13018},
+  year         = {2020},
+  url          = {https://arxiv.org/abs/2006.13018},
+  eprinttype    = {arXiv},
+  eprint       = {2006.13018},
+  timestamp    = {Wed, 01 Jul 2020 15:21:23 +0200},
+  biburl       = {https://dblp.org/rec/journals/corr/abs-2006-13018.bib},
+  bibsource    = {dblp computer science bibliography, https://dblp.org}
+}
+
+@inproceedings{hldsshard2,
+  author    = {Bo Liu and Ying Wei and Yu Zhang and Qiang Yang},
+  title     = {Deep Neural Networks for High Dimension, Low Sample Size Data},
+  booktitle = {Proceedings of the Twenty-Sixth International Joint Conference on
+               Artificial Intelligence, {IJCAI-17}},
+  pages     = {2287--2293},
+  year      = {2017},
+  doi       = {10.24963/ijcai.2017/318},
+  url       = {https://doi.org/10.24963/ijcai.2017/318},
+}