4 papers
Learning State-Tracking from Code Using Linear RNNs
Julien Siems, Riccardo Grazzi, Korbinian Pöppel +3
Over the last years, state-tracking tasks, particularly permutation composition, have become a testbed to understand the limits of sequence models architectures like Transformers a…
DeltaProduct: Improving State-Tracking in Linear RNNs via Householder Products
Julien Siems, Timur Carstensen, Arber Zela +3
Linear Recurrent Neural Networks (linear RNNs) have emerged as competitive alternatives to Transformers for sequence modeling, offering efficient training and linear-time inference…
Convergence Properties of Stochastic Hypergradients
Riccardo Grazzi, Massimiliano Pontil, Saverio Salzo
Bilevel optimization problems are receiving increasing attention in machine learning as they provide a natural framework for hyperparameter optimization and meta-learning. A key st…
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
Riccardo Grazzi, Julien Siems, Arber Zela +3
Linear Recurrent Neural Networks (LRNNs) such as Mamba, RWKV, GLA, mLSTM, and DeltaNet have emerged as efficient alternatives to Transformers for long sequences. However, both Tran…