9 papers
Reasoning-Aware Training for Time Series Forecasting
Md Atik Ahamed, Mihir Parmar, Palash Goyal +4
Time Series Foundation Models (TSFMs) excel at numerical forecasting but operate as black boxes lacking qualitative reasoning. Conversely, applying LLMs directly to temporal data i…
TFRBench: A Reasoning Benchmark for Evaluating Forecasting Systems
Md Atik Ahamed, Mihir Parmar, Palash Goyal +7
We introduce TFRBench, the first benchmark designed to evaluate the reasoning capabilities of forecasting systems. Traditionally, time-series forecasting has been evaluated solely…
CrossLLM-Mamba: Multimodal State Space Fusion of LLMs for RNA Interaction Prediction
Rabeya Tus Sadia, Qiang Ye, Qiang Cheng
Accurate prediction of RNA-associated interactions is essential for understanding cellular regulation and advancing drug discovery. While Biological Large Language Models (BioLLMs)…
RefiDiff: Progressive Refinement Diffusion for Efficient Missing Data Imputation
Md Atik Ahamed, Qiang Ye, Qiang Cheng
Missing values in high-dimensional, mixed-type datasets pose significant challenges for data imputation, particularly under Missing Not At Random (MNAR) mechanisms. Existing method…
MolSnap: Snap-Fast Molecular Generation with Latent Variational Mean Flow
Md Atik Ahamed, Qiang Ye, Qiang Cheng
Molecular generation conditioned on textual descriptions is a fundamental task in computational chemistry and drug discovery. Existing methods often struggle to simultaneously ensu…
TSCMamba: Mamba Meets Multi-View Learning for Time Series Classification
Md Atik Ahamed, Qiang Cheng
Multivariate time series classification (TSC) is critical for various applications in fields such as healthcare and finance. While various approaches for TSC have been explored, im…