6 accepted papers
In this work, C2R-KD is proposed, applying a Complex-to-Real projection to map complex domain features into the real domain. C2R-KD mitigates complex-real domain mismatch to strengthen the representational capacity of the student model and further improves the knowledge distillation model performanc
The consideration of channel correlation is crucial for improving the performance of multivariate time series forecasting. However, existing models fail to capture it in homogeneous and highly correlated channels. In this work, we introduce CompRestacking (Compression Restacking), a strikingly intui
We applied Risk-averse Reinforcement Learning (RL) to optimize investment portfolios while incorporating risk constraints. Given that portfolios must adhere to risk constraints set by investors and regulators, enforcing hard constraints is essential for practical portfolio optimization. Traditional…
This paper addresses contention window optimization for multi-access scenarios. Our investigation into state-of-the-art models revealed that a limited number of nodes dominate the communication channels. Such monopolization issues are critical in networks as they can lead to significant disruptions…
This work proposes and analyzes IncepSeqNet which is a new model combining the Inception Module with the innovative Multi-Shape Augmentation technique. IncepSeqNet excels in feature extraction from sequence signal data consisting of a number of complex numbers to achieve superior classification accu…
We present an imagification approach for multivariate time-series data tailored to constrained NN-based forecasting model training environments. Our imagification process consists of two key steps: Re-stacking and time embedding. In the Re-stacking stage, time-series data are arranged based on high…