9 papers
Safety stock calculation with heteroscedastic, autocorrelated and heavy-tailed forecast errors, an adaptive parametric method.e
Luis Fernández-Palacios, Manuel Ceballos, Yolanda Muñoz-Ocaña
This study develops and validates an adaptive parametric method for sizing safety stock when forecast errors are autocorrelated, heteroscedastic, asymmetric, and heavy-tailed. Usin…
BioDyad: Synchronize Biomedical Discovery and Machine Learning Engineering
Xingbo Du, Fadli Aulawi Al Ghiffari, Leonard Song +5
Agentic biomedical machine learning (ML) draws on complementary advances in biomedical evidence acquisition and executable program search. Existing systems connect aspects of these…
RSMeM: Knowledge-Enhanced Memory Evolution for Remote Sensing Agents with Systematic Evaluation
Bingxian Wu, Yu Zhang, Zonghao Guo +9
Geoscience research requires complex analysis and domain expertise, with remote sensing (RS) observations as a key foundation. However, existing RS agents built on general-purpose…
BioXArena: Benchmarking LLM Agents on Multi-Modal Biomedical Machine Learning Tasks
Loka Li, Duzhen Zhang, Xingbo Du +11
Large language model (LLM) agents increasingly automate machine-learning model building, yet biomedical benchmarks emphasize question answering, reasoning, and tool use or cover na…
UniPhy: Unifying Riemannian-Clifford Geometry and Biorthogonal Dynamics for Planetary-Scale Continuous Weather Modeling
Ruiqing Yan, Haoyu Deng, Yuhang Shao +3
While data-driven weather models have achieved remarkable deterministic accuracy, they fundamentally rely on discrete-time mappings and closed-system assumptions, failing to captur…
MemR: Memory Retrieval via Reflective Reasoning for LLM Agents
Xingbo Du, Loka Li, Duzhen Zhang +1
Memory systems have been designed to leverage past experiences in Large Language Model (LLM) agents. However, many deployed memory systems primarily optimize compression and storag…
Flexible Concept Bottleneck Model
Xingbo Du, Qiantong Dou, Lei Fan +1
Concept bottleneck models (CBMs) improve neural network interpretability by introducing an intermediate layer that maps human-understandable concepts to predictions. Recent work ha…
RecurFormer: Not All Transformer Heads Need Self-Attention
Ruiqing Yan, Linghan Zheng, Xingbo Du +3
Transformer-based large language models (LLMs) excel in modeling complex language patterns but face significant computational costs during inference, especially with long inputs du…
Unveiling and Controlling Anomalous Attention Distribution in Transformers
Ruiqing Yan, Xingbo Du, Haoyu Deng +7
With the advent of large models based on the Transformer architecture, researchers have observed an anomalous phenomenon in the Attention mechanism--there is a very high attention…