8 accepted papers
"High computational overhead is a troublesome problem for diffusion models. Recent studies have leveraged post-training quantization (PTQ) to compress diffusion models. However, most of them only focus on unconditional models, leaving the quantization of widely-used pretrained text-to-image models,…
Although Transformer architectures have been successfully applied to graph data with the advent of Graph Transformer, current design of Graph Transformer still heavily relies on human labor and expertise knowledge to decide proper neural architectures and suitable graph encoding strategies at each T…
Current auxiliary learning methods mainly adopt the methodology of reweighing losses for the manually collected auxiliary data and tasks. However, these methods heavily rely on domain knowledge during data collection, which may be hardly available in reality. Therefore, current methods will become l…
Existing auxiliary learning approaches only consider the relationships between the target task and the auxiliary tasks, ignoring the fact that data samples within an auxiliary task could contribute differently to the target task, which results in inefficient auxiliary information usage and non-robus…
Graph Neural Architecture Search (GNAS) has become a powerful method in automatically discovering suitable Graph Neural Network (GNN) architectures for different tasks. However, existing approaches fail to handle large-scale graphs because current performance estimation strategies in GNAS are comput…
Auxiliary learning is a widely adopted practice in deep learning, which aims to improve the model performance on the primary task by exploiting the beneficial information in the auxiliary loss. Existing auxiliary learning methods only focus on balancing the auxiliary loss and the primary loss, ignor…
Self-attention mechanisms have been widely adopted in many machine learning areas, including Natural Language Processing (NLP) and Graph Representation Learning (GRL), etc. However, existing works heavily rely on hand-crafted design to obtain customized attention mechanisms. In this paper, we automa…
We define a unified information-based measure to provide quantitative explanations on how intermediate layers of deep Natural Language Processing (NLP) models leverage information of input words. Our method advances existing explanation methods by addressing issues in coherency and generality. Expla…