Author name cluster

Ke Jin

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5 papers

2 author rows

EAAI Journal 2026 Journal Article

An end-to-end wavelet-based irregular transformer with gumbel sampling for spatiotemporal welding prediction

Changhui Liu
Ke Jin
Jianzhi Sun
Lin Deng
Jiewu Leng
Xin Li
Qian Li
Qingcheng Yang

Welding prediction plays a vital role in ensuring assembly precision and minimizing rework in thin-walled structures. Data-driven approaches have attracted increasing attention; however, most existing methods rely on oversimplified input representations, overlook irregular temporal dynamics, and focus solely on single-variable prediction, limiting their applicability in complex welding scenarios. Motivated by these challenges, this study develops an end-to-end Wavelet Irregular Transformer with Gumbel Sampling, designed to achieve accurate spatiotemporal prediction of welding-induced deformation and residual stress. From the artificial intelligence perspective, the model incorporates a large language model-based embedding initializer that compresses and contextualizes step-level simulation parameters, and an adaptive parameterized Gumbel keyframe extractor that dynamically identifies the most informative temporal segments. This design enables efficient learning over ultra-long welding sequences while maintaining high-fidelity temporal representations. From the engineering application perspective, a channel-aware wavelet encoder–decoder is developed to fuse multi-frequency and multi-channel features, improving spatial coherence and capturing coupled stress–strain interactions. Validation on a dedicated thin-plate welding dataset, supplemented by physical experiments, shows that the proposed method achieves superior accuracy, robustness, and computational efficiency compared with optimized encoder–decoder and sequence-modeling baselines. The proposed Wavelet Irregular Transformer with Gumbel Sampling achieves a deformation mean absolute error of 0. 033 mm and a root mean square error of 0. 045 mm on the test set, reducing the deformation mean absolute error by 92. 0% and the root mean square error by 82. 1% compared with the best uniform-sampling baseline, while requiring 76. 7% fewer billion floating-point operations than a full-sequence Transformer.

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NeurIPS Conference 2025 Conference Paper

D$^2$GS: Dense Depth Regularization for LiDAR-free Urban Scene Reconstruction

Kejing Xia
Jidong Jia
Ke Jin
Yucai BAI
Li Sun
Dacheng Tao
Youjian Zhang

Recently, Gaussian Splatting (GS) has shown great potential for urban scene reconstruction in the field of autonomous driving. However, current urban scene reconstruction methods often depend on multimodal sensors as inputs, $\textit{i. e. }$ LiDAR and images. Though the geometry prior provided by LiDAR point clouds can largely mitigate ill-posedness in reconstruction, acquiring such accurate LiDAR data is still challenging in practice: i) precise spatiotemporal calibration between LiDAR and other sensors is required, as they may not capture data simultaneously; ii) reprojection errors arise from spatial misalignment when LiDAR and cameras are mounted at different locations. To avoid the difficulty of acquiring accurate LiDAR depth, we propose ${D}^2GS$, a LiDAR-free urban scene reconstruction framework. In this work, we obtain geometry priors that are as effective as LiDAR while being denser and more accurate. $\textbf{First}$, we initialize a dense point cloud by back-projecting multi-view metric depth predictions. This point cloud is then optimized by a Progressive Pruning strategy to improve the global consistency. $\textbf{Second}$, we jointly refine Gaussian geometry and predicted dense metric depth via a Depth Enhancer. Specifically, we leverage diffusion priors from a depth foundation model to enhance the depth maps rendered by Gaussians. In turn, the enhanced depths provide stronger geometric constraints during Gaussian training. $\textbf{Finally}$, we improve the accuracy of ground geometry by constraining the shape and normal attributes of Gaussians within road regions. Extensive experiments on the Waymo dataset demonstrate that our method consistently outperforms state-of-the-art methods, producing more accurate geometry even when compared with those using ground-truth LiDAR data.

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ICLR Conference 2025 Conference Paper

McEval: Massively Multilingual Code Evaluation

Linzheng Chai
Shukai Liu
Jian Yang 0030
Yuwei Yin
Ke Jin
Jiaheng Liu
Tao Sun 0016
Ge Zhang 0009

Code large language models (LLMs) have shown remarkable advances in code understanding, completion, and generation tasks. Programming benchmarks, comprised of a selection of code challenges and corresponding test cases, serve as a standard to evaluate the capability of different LLMs in such tasks. However, most existing benchmarks primarily focus on Python and are still restricted to a limited number of languages, where other languages are translated from the Python samples degrading the data diversity. To further facilitate the research of code LLMs, we propose a massively multilingual code benchmark covering 40 programming languages (McEval) with 16K test samples, which substantially pushes the limits of code LLMs in multilingual scenarios. The benchmark contains challenging code completion, understanding, and generation evaluation tasks with finely curated massively multilingual instruction corpora McEval-Instruct. In addition, we introduce an effective multilingual coder mCoder trained on McEval-Instruct to support multilingual programming language generation. Extensive experimental results on McEval show that there is still a difficult journey between open-source models and closed-source LLMs in numerous languages. The instruction corpora and evaluation benchmark are available at https://github.com/MCEVAL/McEval.

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IROS Conference 2023 Conference Paper

Data-Driven Based Cascading Orientation and Translation Estimation for Inertial Navigation

Xiangyu Deng
Shenyue Wang
Chunxiang Shan
Jinjie Lu
Ke Jin
Jijunnan Li
Yandong Guo

Recently, data-driven approaches have brought both opportunities and challenges for Inertial Navigation Systems. In this paper, we propose a novel data-driven method which is composed of cascading orientation and translation estimation with IMU-only measurements. For robust orientation estimation, we combine a CNN-based neural network with an EKF to eliminate orientation errors caused by sensor noises. We additionally propose a hybrid CNN-Transformer-based neural network which exploits both spatial and long-term temporal information to regress accurate translations. Specifically, we conduct detailed evaluations on datasets acquired by iPhone and Android devices. The result demonstrates that our method outperforms state-of-the-art methods in both orientation and translation errors.

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AIJ Journal 2022 Journal Article

VoCSK: Verb-oriented commonsense knowledge mining with taxonomy-guided induction

Jingping Liu
Tao Chen
Chao Wang
Jiaqing Liang
Lihan Chen
Yanghua Xiao
Yunwen Chen
Ke Jin

Commonsense knowledge acquisition is one of the fundamental issues in realizing human-level AI. However, commonsense knowledge is difficult to obtain because it is a human consensus and rarely explicitly appears in texts or other data. In this paper, we focus on the automatic acquisition of a typical kind of implicit verb-oriented commonsense knowledge (e. g. , “person eats food”), which is the concept-level knowledge of verb phrases. For this purpose, we propose a taxonomy-guided induction method to mine verb-oriented commonsense knowledge from verb phrases with the help of a probabilistic taxonomy. First, we design an entropy-based triplet filter to cope with noisy verb phrases. Then, we propose a joint model based on the minimum description length principle and a neural language model to generate verb-oriented commonsense knowledge. Besides, we introduce two strategies to accelerate the computation, including the simulated annealing-based approximate solution and the verb phrase clustering method. Finally, we conduct extensive experiments to prove that our solution is more effective than competitors in mining verb-oriented commonsense knowledge. We construct a commonsense knowledge base called VoCSK, containing 259 verbs and 18, 406 verb-oriented commonsense knowledge. To verify the usefulness of VoCSK, we utilize the knowledge in this KB to improve the model performance on two downstream applications.

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