Multi-view representation learning methods typically follow a consistent-and-specific pipeline that aims at extracting latent representations for an entity from its multiple observable views to facilitate downstream tasks. However, most of them overlook the complex underlying correlation between different views. To solve this issue, we delve into a well-known property of neural networks (NNs) that NNs tend to learn simple patterns first and then hard ones. In our case, view-consistent representations are simple patterns and view-specific representations are hard. To this end, we propose to disentangle view-consistency and view-specificity and learn them gradually. Specifically, we devise a novel curriculum learning approach that adjusts the whole model to learn view-consistent representations first and then progressively view-specific representations. Besides, we saddle each view with a learnable prior that allows each view-specific representation to appropriate its distribution. Moreover, we incorporate a mixture-of-experts layer and a disentangling module to further enhance the quality of the learned representations. Extensive experiments on five real-world datasets show that the proposed model outperforms its counterparts markedly. The code is available at https://github.com/XLearning-SCU/2025-IJCAI-CL2P.

Most existing multi-view representation learning methods assume view-completeness and noise-free data. However, such assumptions are strong in real-world applications. Despite advances in methods tailored to view-missing or noise problems individually, a one-size-fits-all approach that concurrently addresses both remains unavailable. To this end, we propose a holistic method, called Dual-masked Variational Autoencoders (DualVAE), which aims at learning robust multi-view representation. The DualVAE exhibits an innovative amalgamation of dual-masked prediction, mixture-of-experts learning, representation disentangling, and a joint loss function in wrapping up all components. The key novelty lies in the dual-masked (view-mask and patch-mask) mechanism to mimic missing views and noisy data. Extensive experiments on four multi-view datasets show the effectiveness of the proposed method and its superior performance in comparison to baselines. The code is available at https://github.com/XLearning-SCU/2025-IJCAI-DualVAE.

The limitations of the von Neumann architecture in terms of power consumption and throughput are increasingly evident. In-memory computing is a promising computing paradigm to alleviate this limitation. This article proposes a high-speed and low-power 10T compute-static random-access memory (CSRAM) capable of conducting rowwise search operations and executing in-memory logic functions efficiently. A self-suppressed discharge scheme is implemented to curtail the power consumption of the search operation by reducing the discharge swing of the match lines (MLs). The rowwise search scheme avoids vertical data storage, enhancing the compatibility between different operation modes. The proposed 10T SRAM architecture addresses the issue of sneak currents effectively when multiple lines are activated. Additionally, decoupled read ports eliminate compute access disturbance. To validate the design, a 4Kb array is designed with a 40-nm CMOS technology. At a supply voltage (VDD) of 1.1 V, the in-memory logic operations are capable of operating at a frequency of 752 MHz, consuming 29.2 fJ/bit. In binary content-addressable memory (BCAM) search mode, the minimum energy consumption of 0.51 fJ/bit occurs at 0.8 V and 120 MHz.