Journal papers

  • Kadmon J and Sompolinsky H (in prep) Deep networks with random representations
  • Bahri Y, Kadmon J, Pennington J, Schoenholz S, Sohl-Dickstein J and Ganguli S (Annual Reviews in Condensed Matter, in press) Statistical mechanics of deep learning
  • Marshel, Kim, Machado, Quirin, Kadmon, Benson, Raja, Chibukhchyan, Ramakrishnan, Inoue, Shane, McKnight, Susumu, Kato, Ganguli and Deisseroth (Science, 2019) Layer-specific neural ensembles contributing to ignition and plasticity of perception.
  • Wagner MJ, Kim TH, Kadmon J, Nguyen ND, Ganguli S, Schnitzer MJ and Luo L (Cell, 2019), Cortex-cerebellum dynamics in the execution and learning of a motor task.
  • Kadmon J and Ganguli S (NeurIPS, 2018) Statistical Mechanics of Low-Rank Tensor Decomposition.
  • Kadmon J and Sompolinsky H (NeurIPS, 2016), Optimal architectures in a solvable model of deep networks.
  • Kadmon J and Sompolinsky, H., (Physical Review X, 2015). Transition to chaos in random neuronal networks. Physical Review X
  • Kadmon J, Ishay JS and Bergman DJ (Physical Review E, 2009). Properties of ultrasonic acoustic resonances for exploitation in comb construction by social hornets and honeybees.
  • Tsfadia Y, Friedman R, Kadmon J, SelzerA, Nachliel E and Gutman M FEBS letters, (2007) Molecular dynamics simulations of palmitate entry into the hydrophobic pocket of the fatty acid binding protein.

Conference abstracts

  • Timchek J, Kadmon J, Ganguli S, Boahen K, Harnessing noise and disorder to rescue optimal predictive coding, Cosyne 2019
  • Kadmon J and Sompolinsky H, Analytical study of simple deep networks with recursive learning. Cosyne 2018
  • Kadmon J and Sompolinsky H, Sensory processing by deep-networks with layer-localized learning. Cosyne 2017
  • Kadmon J and Sompolinsky H, Optimal architecture in a solvable model for deep networks. Bernstein Conference 2016
  • Kadmon J and Sompolinsky H, Chaos in random neuronal networks of Inhibitory and Excitatory neurons, NCCD 2015
  • Kadmon J and Sompolinsky H. Dynamic mean-field for cortical circuits. ISFN 2014