Learning from data with missing values is a common challenge in real-world applications. Existing approaches for handling data incompleteness often involve imputation, which can introduce errors that propagate into downstream tasks or impose assumptions that limit the support for heterogeneous feature types. To address these issues, we propose Missing Feature Attention Network ( MFAN ), an end-to-end label prediction model that directly consumes incomplete data without requiring imputation. MFAN flexibly accommodates both continuous and categorical features through learnable embeddings, and leverages a transformer encoder with self-attention to capture the correlation among features as well as the correlation between features and missingness . This attention-based mechanism allows missing features to benefit from relationships learned among observed features, leading to enhanced hidden representations and robust prediction performance. Additionally, we introduce auxiliary self-supervised pre-training tasks that further guide the attention mechanism in modeling missingness. Experimental results on eight regression and seven classification datasets demonstrate MFAN ’s superiority over state-of-the-art end-to-end methods and imputation-based approaches. Comprehensive ablation studies confirm the effectiveness of each MFAN component, underscoring the importance of explicitly modeling correlations among observed and missing features.

Nowadays, numerous AI systems employ deep neural network models with excessive number of parameters to obtain superior performance on real-world applications. Such systems relies on high-performance GPUs to achieve real-time inference, especially for the ones that involve convolutional operations. This prohibits the practical deployment on resourcescarce edge devices. To reduce model size and inference time, model pruning for deep neural network models has been an active thread of research in the recent years. Among a significant amount of literature towards different model pruning strategies, ℓ1regularization is commonly considered as a simple solution for sparse models. However, a fundamental issue is seldom addressed: "why ℓ1regularization can be effective for model pruning". In this work, we provide a theoretical explanation showing that, for a specific type of neural network models with ℓ1regularization, tasks of higher accuracy result in higher pruning ratio. Hence, network pruning could be exceptionally effective on high accuracy tasks. Based on the theoretical analysis, we demonstrate the effectiveness of ℓ1regularization learning on two iconic computer vision tasks: (a) face detection and (b) image segmentation. The experiment results show that, with suitable ℓ1-regularized optimizations, even for a compact model like MobileNetV2, the model size can be reduced by an order of magnitude without significant losses on the accuracy. Furthermore, we show that with proper implementation of sparse convolution, the obtained sparse neural network models can achieve multiple times speed-ups not only in FLOPs, but also in actual inference time.

Nowadays, World Wide Web connects people to each other in many ways ubiquitously. Followed along with the convenience and usability, millions of malware infect various devices of numerous users through the web every day. In contrast, traditional anti-malware systems detect such malware by scanning file systems and provide secure environments for users. However, some malware might not be detected by traditional scanning-based detection systems due to hackers' obfuscation techniques. Also, scanning-based approaches cannot caution users for uninfected malware with high risks. In this paper, we aim to build a personalized malware warning system. Different from traditional scanning-based approaches, we focus on discovering the potential malware which has not been detected for each user. If users and the system know the potentially infected malware in advance, they can be alert against the corresponding risks. We propose a novel approach to learn the implicit feedback from detection logs and give a personalized risk ranking of malware for each user. Finally, the experiments on real-world detection datasets demonstrate the proposed algorithm outperforms traditional popularity-based algorithms.