most citedHeart rate and respiratory rate prediction from noisy real-world smartphone based on Deep Learning methods

1 citations · 1 across the 12 of their papers we have counts for

collaborators

26 papers

cs.LG2026

Certificate-Guided Pruning for Stochastic Lipschitz Optimization

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

We study black-box optimization of Lipschitz functions under noisy evaluations. Existing adaptive discretization methods implicitly avoid suboptimal regions but do not provide expl…

cs.CL2026

Adaptive Constraint Propagation: Scaling Structured Inference for Large Language Models via Meta-Reinforcement Learning

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Large language models increasingly require structured inference, from JSON schema enforcement to multi-lingual parsing, where outputs must satisfy complex constraints. We introduce…

cs.LG2026

CalPro: Prior-Aware Evidential--Conformal Prediction with Structure-Aware Guarantees for Protein Structures

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Deep protein structure predictors such as AlphaFold provide confidence estimates (e.g., pLDDT) that are often miscalibrated and degrade under distribution shifts across experimenta…

cs.LG2026

Beyond Variance: Knowledge-Aware LLM Compression via Fisher-Aligned Subspace Diagnostics

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Post-training activation compression is essential for deploying Large Language Models (LLMs) on resource-constrained hardware. However, standard methods like Singular Value Decompo…

cs.CV2025

LLM-Guided Probabilistic Fusion for Label-Efficient Document Layout Analysis

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Document layout understanding remains data-intensive despite advances in semi-supervised learning. We present a framework that enhances semi-supervised detection by fusing visual p…

cs.CV2025

Temporal Zoom Networks: Distance Regression and Continuous Depth for Efficient Action Localization

Ibne Farabi Shihab, Sanjeda Akter, Anuj Sharma

Temporal action localization requires both precise boundary detection and computational efficiency. Current methods apply uniform computation across all temporal positions, wasting…