K. Giam

Molecular Biology · 2020

A researcher in the Science corpus: 1 paper; trusts 2 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom K. Giam trusts

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Morten Nielsen (in Molecular Biology)
  2. Ole Lund (in Molecular Biology)

Papers from K. Giam's perspective

Ordered by how strongly the researchers K. Giam trusts endorse each paper. Same corpus, a different order for every researcher.

  1. Long Short-Term Memory
  2. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
  3. Rectified Linear Units Improve Restricted Boltzmann Machines
  4. Dropout: a simple way to prevent neural networks from overfitting
  5. Human-level control through deep reinforcement learning
  6. Distributed Representations of Words and Phrases and their Compositionality (Neural Information Processing Systems)
  7. Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation
  8. Rectifier Nonlinearities Improve Neural Network Acoustic Models
  9. A Community Resource Benchmarking Predictions of Peptide Binding to MHC-I Molecules (PLoS Comput. Biol.)
  10. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Their papers