4 papers
One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data
Amrijit Biswas, Mustafa Kamal, Robin Krambroeckers +4
Transformer-based models have emerged as leading paradigms in time-series forecasting in recent years, employing self-attention mechanisms to capture long-range dependencies. Despi…
Beyond Real Weights: Hypercomplex Representations for Stable Quantization
Jawad Ibn Ahad, Maisha Rahman, Amrijit Biswas +5
Multimodal language models (MLLMs) require large parameter capacity to align high-dimensional visual features with linguistic representations, making them computationally heavy and…
LAET: A Layer-wise Adaptive Ensemble Tuning Framework for Pretrained Language Models
Jawad Ibn Ahad, Muhammad Rafsan Kabir, Robin Krambroeckers +3
Natural Language Processing (NLP) has transformed the financial industry, enabling advancements in areas such as textual analysis, risk management, and forecasting. Large language…
Temporal Window Smoothing of Exogenous Variables for Improved Time Series Prediction
Mustafa Kamal, Niyaz Bin Hashem, Robin Krambroeckers +2
Although most transformer-based time series forecasting models primarily depend on endogenous inputs, recent state-of-the-art approaches have significantly improved performance by…