5 citations · 12 across the 21 of their papers we have counts for
5 papers · 2 filters
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…
Mamba4Cast: Efficient Zero-Shot Time Series Forecasting with State Space Models
Sathya Kamesh Bhethanabhotla, Omar Swelam, Julien Siems +2
This paper introduces Mamba4Cast, a zero-shot foundation model for time series forecasting. Based on the Mamba architecture and inspired by Prior-data Fitted Networks (PFNs), Mamba…
Large Language Models Engineer Too Many Simple Features For Tabular Data
Jaris Küken, Lennart Purucker, Frank Hutter
Tabular machine learning problems often require time-consuming and labor-intensive feature engineering. Recent efforts have focused on using large language models (LLMs) to capital…
Regularized Neural Ensemblers
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker +3
Ensemble methods are known for enhancing the accuracy and robustness of machine learning models by combining multiple base learners. However, standard approaches like greedy or ran…
Is Mamba Capable of In-Context Learning?
Riccardo Grazzi, Julien Siems, Simon Schrodi +2
State of the art foundation models such as GPT-4 perform surprisingly well at in-context learning (ICL), a variant of meta-learning concerning the learned ability to solve tasks du…