Author name cluster

Fei Xia

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

10 papers

2 author rows

AAAI Conference 2026 Conference Paper

AutoGameUI: Constructing High-Fidelity GameUI via Multimodal Correspondence Matching

Zhongliang Tang
Qingrong Cheng
Mengchen Tan
Yongxiang Zhang
Fei Xia

Game UI development is essential to the game industry. However, the traditional workflow requires substantial manual effort to integrate pairwise UI and UX designs into a cohesive game user interface (GameUI). The inconsistency between the aesthetic UI design and the functional UX design typically results in mismatches and inefficiencies. To address the issue, we present an automatic system, AutoGameUI, for efficiently and accurately constructing GameUI. The system centers on a two-stage multimodal learning pipeline to obtain the optimal correspondences between UI and UX designs. The first stage learns the comprehensive representations of UI and UX designs from multimodal perspectives. The second stage incorporates grouped cross-attention modules with constrained integer programming to estimate the optimal correspondences through top-down hierarchical matching. The optimal correspondences enable the automatic GameUI construction. We create the GAMEUI dataset, comprising pairwise UI and UX designs from real-world games, to train and validate the proposed method. Besides, an interactive web tool is implemented to ensure high-fidelity effects and facilitate human-in-the-loop construction. Extensive experiments on the GAMEUI and RICO datasets demonstrate the effectiveness of our system in maintaining consistency between the constructed GameUI and the original designs. When deployed in the workflow of several mobile games, AutoGameUI achieves a 3× improvement in time efficiency, conveying significant practical value for game UI development.

PDF Details DOI

EAAI Journal 2025 Journal Article

Health state estimation of retired batteries based on physical constraints

Fei Xia
Qianwen Dong
Lin Xia
Zhenyi An
Ziyang Xia
Chunyang Gong

With the growing use of retired lithium-ion batteries, accurately monitoring their health status has become increasingly important. This study proposed a method to estimate the health of retired batteries by embedding their capacity degradation characteristics directly into the loss function of a Bidirectional Long Short-Term Memory (BiLSTM) network, combined with a Physically Informed Neural Network (PINN) model. The model is developed by incorporating the dynamics of the solid electrolyte interface (SEI) membrane, which evolves as the lithium-ion poles of the retired battery move. By combining these dynamics with the governing equations of motion, a partial differential equation (PDE) is derived. This approach integrates physical constraints, data-driven learning, and PDEs into a composite loss function. The proposed method is validated on two different datasets under varying operating temperatures. The results show that the PINN-BiLSTM model achieves a Root Mean Square Percentage Error (RMSPE) of 0. 024, representing a 9. 67 % improvement over the PINN-LSTM. This adaptive PINN method offers highly accurate health state predictions across temperature variations, thus supporting the sustainable use of retired batteries in secondary applications and helping to mitigate energy scarcity.

Details DOI

NeurIPS Conference 2024 Conference Paper

DrivingDojo Dataset: Advancing Interactive and Knowledge-Enriched Driving World Model

Yuqi Wang
Ke Cheng
Jiawei He
Qitai Wang
Hengchen Dai
Yuntao Chen
Fei Xia
Zhaoxiang Zhang

Driving world models have gained increasing attention due to their ability to model complex physical dynamics. However, their superb modeling capability is yet to be fully unleashed due to the limited video diversity in current driving datasets. We introduce DrivingDojo, the first dataset tailor-made for training interactive world models with complex driving dynamics. Our dataset features video clips with a complete set of driving maneuvers, diverse multi-agent interplay, and rich open-world driving knowledge, laying a stepping stone for future world model development. We further define an action instruction following (AIF) benchmark for world models and demonstrate the superiority of the proposed dataset for generating action-controlled future predictions.

PDF Details DOI

ICLR Conference 2024 Conference Paper

Mastering Symbolic Operations: Augmenting Language Models with Compiled Neural Networks

Yixuan Weng
Minjun Zhu
Fei Xia
Bin Li 0083
Shizhu He
Kang Liu 0001
Jun Zhao 0001

Language models' (LMs) proficiency in handling deterministic symbolic reasoning and rule-based tasks remains limited due to their dependency implicit learning on textual data. To endow LMs with genuine rule comprehension abilities, we propose "Neural Comprehension" - a framework that synergistically integrates compiled neural networks (CoNNs) into the standard transformer architecture. CoNNs are neural modules designed to explicitly encode rules through artificially generated attention weights. By incorporating CoNN modules, the Neural Comprehension framework enables LMs to accurately and robustly execute rule-intensive symbolic tasks. Extensive experiments demonstrate the superiority of our approach over existing techniques in terms of length generalization, efficiency, and interpretability for symbolic operations. Furthermore, it can be applied to LMs across different model scales, outperforming tool-calling methods in arithmetic reasoning tasks while maintaining superior inference efficiency. Our work highlights the potential of seamlessly unifying explicit rule learning via CoNNs and implicit pattern learning in LMs, paving the way for true symbolic comprehension capabilities. The code is released at: \url{https://github.com/wengsyx/Neural-Comprehension}.

Details

NeurIPS Conference 2024 Conference Paper

PEACE: A Dataset of Pharmaceutical Care for Cancer Pain Analgesia Evaluation and Medication Decision

Yutao Dou
Huimin Yu
Wei Li
Jingyang Li
Fei Xia
Jian Xiao

Over half of cancer patients experience long-term pain management challenges. Recently, interest has grown in systems for cancer pain treatment effectiveness assessment (TEA) and medication recommendation (MR) to optimize pharmacological care. These systems aim to improve treatment effectiveness by recommending personalized medication plans based on comprehensive patient information. Despite progress, current systems lack multidisciplinary treatment (MDT) team assessments of treatment and the patient's perception of medication, crucial for effective cancer pain management. Moreover, managing cancer pain medication requires multiple adjustments to the treatment plan based on the patient's evolving condition, a detail often missing in existing datasets. To tackle these issues, we designed the PEACE dataset specifically for cancer pain medication research. It includes detailed pharmacological care records for over 38, 000 patients, covering demographics, clinical examination, treatment outcomes, medication plans, and patient self-perceptions. Unlike existing datasets, PEACE records not only long-term and multiple follow-ups both inside and outside hospitals but also includes patients' self-assessments of medication effects and the impact on their lives. We conducted a proof-of-concept study with 13 machine learning algorithms on the PEACE dataset for the TEA (classification task) and MR (regression task). These experiments provide valuable insights into the potential of the PEACE dataset for advancing personalized cancer pain management. The dataset is accessible at: [https: //github. com/YTYTYD/PEACE].

PDF Details DOI

ICRA Conference 2023 Conference Paper

A Contextual Bandit Approach for Learning to Plan in Environments with Probabilistic Goal Configurations

Sohan Rudra
Saksham Goel
Anirban Santara
Claudio Gentile
Laurent Perron
Fei Xia
Vikas Sindhwani
Carolina Parada

Object-goal navigation (Object-nav) entails searching, recognizing and navigating to a target object. Object-nav has been extensively studied by the Embodied-AI community, but most solutions are often restricted to considering static objects (e. g. , television, fridge, etc.), We propose a modular framework for object-nav that is able to efficiently search indoor environments for not just static objects but also movable objects (e. g. fruits, glasses, phones, etc.) that frequently change their positions due to human intervention. Our contextual-bandit agent efficiently explores the environment by showing optimism in the face of uncertainty and learns a model of the likelihood of spotting different objects from each navigable location. The likelihoods are used as rewards in a weighted minimum latency solver to deduce a trajectory for the robot. We evaluate our algorithms in two simulated environments and a real-world setting, to demonstrate high sample efficiency and reliability.

Details

NeurIPS Conference 2023 Conference Paper

Grounded Decoding: Guiding Text Generation with Grounded Models for Embodied Agents

Wenlong Huang
Fei Xia
Dhruv Shah
Danny Driess
Andy Zeng
Yao Lu
Pete Florence
Igor Mordatch

Recent progress in large language models (LLMs) has demonstrated the ability to learn and leverage Internet-scale knowledge through pre-training with autoregressive models. Unfortunately, applying such models to settings with embodied agents, such as robots, is challenging due to their lack of experience with the physical world, inability to parse non-language observations, and ignorance of rewards or safety constraints that robots may require. On the other hand, language-conditioned robotic policies that learn from interaction data can provide the necessary grounding that allows the agent to be correctly situated in the real world, but such policies are limited by the lack of high-level semantic understanding due to the limited breadth of the interaction data available for training them. Thus, if we want to make use of the semantic knowledge in a language model while still situating it in an embodied setting, we must construct an action sequence that is both likely according to the language model and also realizable according to grounded models of the environment. We frame this as a problem similar to probabilistic filtering: decode a sequence that both has high probability under the language model and high probability under a set of grounded model objectives. We demonstrate how such grounded models can be obtained across three simulation and real-world domains, and that the proposed decoding strategy is able to solve complex, long-horizon embodiment tasks in a robotic setting by leveraging the knowledge of both models.

PDF Details

NeurIPS Conference 2022 Conference Paper

Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

Jason Wei
Xuezhi Wang
Dale Schuurmans
Maarten Bosma
Brian Ichter
Fei Xia
Ed Chi
Quoc V Le

We explore how generating a chain of thought---a series of intermediate reasoning steps---significantly improves the ability of large language models to perform complex reasoning. In particular, we show how such reasoning abilities emerge naturally in sufficiently large language models via a simple method called chain of thought prompting, where a few chain of thought demonstrations are provided as exemplars in prompting. Experiments on three large language models show that chain of thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. The empirical gains can be striking. For instance, prompting a 540B-parameter language model with just eight chain of thought exemplars achieves state of the art accuracy on the GSM8K benchmark of math word problems, surpassing even finetuned GPT-3 with a verifier.

PDF Details

NeurIPS Conference 2017 Conference Paper

NeuralFDR: Learning Discovery Thresholds from Hypothesis Features

Fei Xia
Martin Zhang
James Zou
David Tse

As datasets grow richer, an important challenge is to leverage the full features in the data to maximize the number of useful discoveries while controlling for false positives. We address this problem in the context of multiple hypotheses testing, where for each hypothesis, we observe a p-value along with a set of features specific to that hypothesis. For example, in genetic association studies, each hypothesis tests the correlation between a variant and the trait. We have a rich set of features for each variant (e. g. its location, conservation, epigenetics etc. ) which could inform how likely the variant is to have a true association. However popular testing approaches, such as Benjamini-Hochberg's procedure (BH) and independent hypothesis weighting (IHW), either ignore these features or assume that the features are categorical. We propose a new algorithm, NeuralFDR, which automatically learns a discovery threshold as a function of all the hypothesis features. We parametrize the discovery threshold as a neural network, which enables flexible handling of multi-dimensional discrete and continuous features as well as efficient end-to-end optimization. We prove that NeuralFDR has strong false discovery rate (FDR) guarantees, and show that it makes substantially more discoveries in synthetic and real datasets. Moreover, we demonstrate that the learned discovery threshold is directly interpretable.

PDF Details

IJCAI Conference 2013 Conference Paper

Generalized Relational Topic Models with Data Augmentation

Ning Chen
Jun Zhu
Fei Xia
Bo Zhang

Relational topic models have shown promise on analyzing document network structures and discovering latent topic representations. This paper presents three extensions: 1) unlike the common link likelihood with a diagonal weight matrix that allows the-same-topic interactions only, we generalize it to use a full weight matrix that captures all pairwise topic interactions and is applicable to asymmetric networks; 2) instead of doing standard Bayesian inference, we perform regularized Bayesian inference with a regularization parameter to deal with the imbalanced link structure issue in common real networks; and 3) instead of doing variational approximation with strict mean-field assumptions, we present a collapsed Gibbs sampling algorithm for the generalized relational topic models without making restricting assumptions. Experimental results demonstrate the significance of these extensions on improving the prediction performance, and the time efficiency can be dramatically improved with a simple fast approximation method.

PDF Details DOI