5 accepted papers
Protein language models often struggle to capture biological functions due to their lack of factual knowledge (e.g., gene descriptions). Existing solutions leverage protein knowledge graphs (PKGs) as auxiliary pre-training objectives, but lack explicit integration of task-oriented knowledge, making…
Electroencephalography (EEG) signals record the electrical activity of the brain and have significant applications in neuroscience and medicine. However, accurately reconstructing EEG signals has been a challenge due to potential signal missing and noise interference during signal acquisition. The p…
Recently, textual information has been proven to positively affect recommendation systems. However, most of the existing methods only focus on representation learning of textual information in ratings, while potential selection bias induced by the textual information is ignored. In this work, we pro…
Spectral graph neural networks (GNNs) have shown great advantages in various graph-related fields. The core of spectral GNNs is the graph convolution operator based on the graph spectral theory. To design learnable graph convolution operators, polynomials offer an efficient way. However, existing po…
Tensor completion aims at filling the missing or unobserved en-tries based on partially observed tensors. However, utilization of the observed tensors often raises serious privacy concerns in many practical scenarios. To address this issue, we propose a solid and unified framework that contains seve…