Recurrent neural
networks
Links to this note
- Adaptive
Computation Time
- Backward RNN
- Echo-state
networks
- Hopfield Networks
- Language modeling
- Neural
architecture search
- Notes on:
Adapting to Unseen Environments through Explicit Representation of
Context by Tutum, C., & Miikkulainen, R. (2020)
- Notes on:
Legendre Memory Units: Continuous-Time Representation in Recurrent
Neural Networks by Voelker, A., Kajić, I., & Eliasmith, C.
(2019)
- Notes on:
Modeling systems with internal state using evolino by Wierstra, D.,
Gomez, F. J., & Schmidhuber, J. (2005)
- Notes on:
Neural Architecture Search with Reinforcement Learning by Zoph, B.,
& Le, Q. V. (2017)
- Notes on:
Neural Circuit Policies Enabling Auditable Autonomy by Lechner, M.,
Hasani, R., Amini, A., Henzinger, T. A., Rus, D., & Grosu, R.
(2020)
- Notes
on: Pretrained Transformers as Universal Computation Engines by Lu,
K., Grover, A., Abbeel, P., & Mordatch, I. (2021)
- Notes on:
The geometry of integration in text classification RNNs by Aitken,
K., Ramasesh, V. V., Garg, A., Cao, Y., Sussillo, D., &
Maheswaranathan, N. (2020)
- Notes
on: Transformers are RNNs: Fast Autoregressive Transformers with
Linear Attention by Katharopoulos, A., Vyas, A., Pappas, N., &
Fleuret, F. (2020)
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