2 citations · 4 across the 24 of their papers we have counts for
6 papers · 1 filter
CAT-GS: Balanced Multimodal Learning via Calibrated Gating and Fusion Surgery
Mahir Shahriar Tamim, Sharjil Khan, Md. Samiul Alim +3
End-to-end training of multimodal neural networks often exhibits unstable neural dynamics characterized by three coupled failure modes that degrade learning: (i) modality imbalance…
CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting
Awsaf Tausif Adib, Md. Shahria Sarker Shuvo, Md. Estehaar Ahmed Emon +4
Accurately modeling cross-variate dependencies remains a key challenge in multivariate time series forecasting, particularly in the presence of strong periodic patterns. Many exist…
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…
Dynamic Temperature Scheduler for Knowledge Distillation
Sibgat Ul Islam, Jawad Ibn Ahad, Fuad Rahman +3
Knowledge Distillation (KD) trains a smaller student model using a large, pre-trained teacher model, with temperature as a key hyperparameter controlling the softness of output pro…
Z-Pruner: Post-Training Pruning of Large Language Models for Efficiency without Retraining
Samiul Basir Bhuiyan, Md. Sazzad Hossain Adib, Mohammed Aman Bhuiyan +4
Large language models (LLMs) have rapidly advanced in recent years, achieving remarkable performance across a wide range of natural language processing tasks. However, this progres…
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…