The paper delves into the theoretical understanding of fine-tuning methods such as prompting and prefix-tuning of transformer models. It posits that these methods can universally approximate sequence-to-sequence functions, and that…
Continue readingThe paper explores the field of news recommendation, a crucial component of online information sharing. It defines the problem, summarises current methods, and introduces a new approach, the News Recommendation…
Continue readingThe paper discusses AttnLRP, a method that extends Layer-wise Relevance Propagation to handle attention layers in transformer models. It aims to provide better understanding of the reasoning process of these…
Continue readingThe article presents Graph-Mamba, a model developed to improve long-range context modeling in graph networks. Traditional attention mechanisms used in Graph Transformers often fail to scale in large graphs due…
Continue readingThis article presents MAMI, a method to improve long sequence neuron captioning. Neuron labeling is a way to visualize how neurons in a deep neural network respond to certain patterns….
Continue readingThis paper discusses the complexity of language model architectures, particularly Transformers, and their use in natural language processing tasks. The author proposes a linguistically motivated strategy for encoding the contextual…
Continue readingThe paper introduces a new Cross-Modal Information-Guided Network (CMIGNet) for point cloud registration. Traditional methods rely solely on features from points, leading to limitations such as lack of texture information…
Continue readingThis academic article discusses a new approach to Document-level Neural Machine Translation (DocNMT). The authors highlight the importance of handling discourse phenomena through document-level context information, but note that the…
Continue readingThe article discusses the importance and challenges of document-level Neural Machine Translation (DocNMT). It points out that while DocNMT is crucial for handling discourse phenomena, it suffers from computational inefficiency…
Continue readingThis paper presents AMPLIFY, a new Mixup method for data augmentation that minimizes the impact of noise and aberrant features in original samples on prediction results. Unlike common Mixup methods,…
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