1 citations · 1 across the 4 of their papers we have counts for
4 papers
Practical Computational Power of Linear Transformers and Their Recurrent and Self-Referential Extensions
Kazuki Irie, Róbert Csordás, Jürgen Schmidhuber
Recent studies of the computational power of recurrent neural networks (RNNs) reveal a hierarchy of RNN architectures, given real-time and finite-precision assumptions. Here we stu…
Accelerating Neural Self-Improvement via Bootstrapping
Kazuki Irie, Jürgen Schmidhuber
Few-shot learning with sequence-processing neural networks (NNs) has recently attracted a new wave of attention in the context of large language models. In the standard N-way K-sho…
Improving Baselines in the Wild
Kazuki Irie, Imanol Schlag, Róbert Csordás +1
We share our experience with the recently released WILDS benchmark, a collection of ten datasets dedicated to developing models and training strategies which are robust to domain s…
Training and Generating Neural Networks in Compressed Weight Space
Kazuki Irie, Jürgen Schmidhuber
The inputs and/or outputs of some neural nets are weight matrices of other neural nets. Indirect encodings or end-to-end compression of weight matrices could help to scale such app…