1 citations · 1 across the 5 of their papers we have counts for
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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…
Ensembling Finetuned Language Models for Text Classification
Sebastian Pineda Arango, Maciej Janowski, Lennart Purucker +3
Finetuning is a common practice widespread across different communities to adapt pretrained models to particular tasks. Text classification is one of these tasks for which many pre…
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…