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Jiajie Yu

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2 papers

2 author rows

ICML Conference 2025 Conference Paper

NEAR: Neural Electromagnetic Array Response

Yinyan Bu
Jiajie Yu
Kai Zheng
Xinyu Zhang
Piya Pal

We address the challenge of achieving angular super-resolution in multi-antenna radar systems that are widely used for localization, navigation, and automotive perception. A multi-antenna radar achieves very high resolution by computationally creating a large virtual sensing system using very few physical antennas. However, practical constraints imposed by hardware, noise, and a limited number of antennas can impede its performance. Conventional supervised learning models that rely on extensive pre-training with large datasets, often exhibit poor generalization in unseen environments. To overcome these limitations, we propose NEAR, an untrained implicit neural representation (INR) framework that predicts radar responses at unseen locations from sparse measurements, by leveraging latent harmonic structures inherent in radar wave propagation. We establish new theoretical results linking antenna array response to expressive power of INR architectures, and develop a novel physics-informed and latent geometry-aware regularizer. Our approach integrates classical signal representation with modern implicit neural learning, enabling high-resolution radar sensing that is both interpretable and generalizable. Extensive simulations and real-world experiments using radar platforms demonstrate NEAR’s effectiveness and its ability to adapt to unseen environments.

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IJCAI Conference 2018 Conference Paper

Improving Implicit Recommender Systems with View Data

Jingtao Ding
Guanghui Yu
Xiangnan He
Yuhan Quan
Yong Li
Tat-Seng Chua
Depeng Jin
Jiajie Yu

Most existing recommender systems leverage the primary feedback data only, such as the purchase records in E-commerce. In this work, we additionally integrate view data into implicit feedback based recommender systems (dubbed as Implicit Recommender Systems). We propose to model the pairwise ranking relations among purchased, viewed, and non-viewed interactions, being more effective and ﬂexible than typical pointwise matrix factorization (MF) methods. However, such a pairwise formulation poses efﬁciency challenges in learning the model. To address this problem, we design a new learning algorithm based on the element-wise Alternating Least Squares (eALS) learner. Notably, our algorithm can efﬁciently learn model parameters from the whole user-item matrix (including all missing data), with a rather low time complexity that is dependent on the observed data only. Extensive experiments on two real-world datasets demonstrate that our method outperforms several state-of-the-art MF methods by 10% ∼ 28. 4%. Our implementation is available at: https: //github. com/ dingjingtao/View_enhanced_ALS.

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