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Yu Qin

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3 papers
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3

AAAI Conference 2025 Conference Paper

Learning Production Functions for Supply Chains with Graph Neural Networks

  • Serina Chang
  • Zhiyin Lin
  • Benjamin Yan
  • Swapnil Bembde
  • Qi Xiu
  • Chi Heem Wong
  • Yu Qin
  • Frank Kloster

The global economy relies on the flow of goods over supply chain networks, with nodes as firms and edges as transactions between firms. While we may observe these external transactions, they are governed by unseen production functions, which determine how firms internally transform the input products they receive into output products that they sell. In this setting, it can be extremely valuable to infer these production functions, to better understand and improve supply chains, and to forecast future transactions more accurately. However, existing graph neural networks (GNNs) cannot capture these hidden relationships between nodes’ inputs and outputs. Here, we introduce a new class of models for this setting, by combining temporal GNNs with a novel inventory module, which learns production functions via attention weights and a special loss function. We evaluate our models extensively on real supply chains data, along with data generated from our new open-source simulator, SupplySim. Our models successfully infer production functions, outperforming the strongest baseline by 6-50% (across datasets), and forecast future transactions, outperforming the strongest baseline by 11-62%.

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AAAI Conference 2022 Conference Paper

Deconfounded Visual Grounding

  • Jianqiang Huang
  • Yu Qin
  • Jiaxin Qi
  • Qianru Sun
  • Hanwang Zhang

We focus on the confounding bias between language and location in the visual grounding pipeline, where we find that the bias is the major visual reasoning bottleneck. For example, the grounding process is usually a trivial languagelocation association without visual reasoning, e. g. , grounding any language query containing sheep to the nearly central regions, due to that most queries about sheep have groundtruth locations at the image center. First, we frame the visual grounding pipeline into a causal graph, which shows the causalities among image, query, target location and underlying confounder. Through the causal graph, we know how to break the grounding bottleneck: deconfounded visual grounding. Second, to tackle the challenge that the confounder is unobserved in general, we propose a confounder-agnostic approach called: Referring Expression Deconfounder (RED), to remove the confounding bias. Third, we implement RED as a simple language attention, which can be applied in any grounding method. On popular benchmarks, RED improves various state-of-the-art grounding methods by a significant margin. Code is available at: https: //github. com/JianqiangH/ Deconfounded VG.

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IJCAI Conference 2020 Conference Paper

Financial Risk Prediction with Multi-Round Q& A Attention Network

  • Zhen Ye
  • Yu Qin
  • Wei Xu

Financial risk is an essential indicator of investment, which can help investors to understand the market and companies better. Among the many influencing factors of financial risk, researchers find the earnings conference call is the most significant one. Predicting financial volatility after the earnings conference call has been critical to beneficiaries, including investors and company managers. However, previous work mainly focuses on the feature extraction from the word-level or document-level. The vital structure of conferences, the alternate dialogue, is ignored. In this paper, we introduced our Multi-Round Q&A Attention Network, which brings into account the dialogue form in the first place. Based on the data of earnings call transcripts, we apply our model to extract features of each round of dialogue through a bidirectional attention mechanism and predict the volatility after the earnings conference call events. The results prove that our model significantly outperforms the previous state-of-the-art methods and other baselines in three different periods.

PDF Details DOI
v2026.09.13