Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry

Bariscan Bozkurt, Cengiz Pehlevan, Alper T. Erdogan. Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry. In Alice Oh, Tristan Naumann, Amir Globerson, Kate Saenko, Moritz Hardt, Sergey Levine, editors, Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023. 2023. [doi]

@inproceedings{BozkurtPE23,
  title = {Correlative Information Maximization: A Biologically Plausible Approach to Supervised Deep Neural Networks without Weight Symmetry},
  author = {Bariscan Bozkurt and Cengiz Pehlevan and Alper T. Erdogan},
  year = {2023},
  url = {http://papers.nips.cc/paper_files/paper/2023/hash/6dea02c16a492682d66c6f626c306db2-Abstract-Conference.html},
  researchr = {https://researchr.org/publication/BozkurtPE23},
  cites = {0},
  citedby = {0},
  booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
  editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}