2 citations · 6 across the 7 of their papers we have counts for
4 papers · 1 filter
Spectral Manifold Regularization for Stable and Modular Routing in Deep MoE Architectures
Ibrahim Delibasoglu
Mixture of Experts (MoE) architectures enable efficient scaling of neural networks but suffer from expert collapse, where routing converges to a few dominant experts. This reduces…
Scaling Transformers for Time Series Forecasting: Do Pretrained Large Models Outperform Small-Scale Alternatives?
Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz
Large pre-trained models have demonstrated remarkable capabilities across domains, but their effectiveness in time series forecasting remains understudied. This work empirically ex…
LMS-AutoTSF: Learnable Multi-Scale Decomposition and Integrated Autocorrelation for Time Series Forecasting
Ibrahim Delibasoglu, Sanjay Chakraborty, Fredrik Heintz
Time series forecasting is an important challenge with significant applications in areas such as weather prediction, stock market analysis, scientific simulations and industrial pr…
EDformer: Embedded Decomposition Transformer for Interpretable Multivariate Time Series Predictions
Sanjay Chakraborty, Ibrahim Delibasoglu, Fredrik Heintz
Time series forecasting is a crucial challenge with significant applications in areas such as weather prediction, stock market analysis, and scientific simulations. This paper intr…