The ability to decentralize knowledge graphs (KG) is important to exploit the full potential of the Semantic Web and realize the Web 3.0 vision. However, decentralization also renders KGs more prone to attacks with adverse effects on data integrity and query verifiability.
When generating multi-entity scenes, stable diffusion and its derivative models frequently encounter issues of entity overlap or fusion, primarily due to cross-attention leakage. To mitigate these challenges, we propose performing differentiation and binarization on cross-attention maps to accurately locate entities within non-overlapping areas.
FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather Forecasting The first data-driven global weather forecasting model running at the 0.09◦ horizontal resolution. FengWu-GHR introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a pretrained low-resolution model.
This paper introduces a novel method called FedPIN (Personalized Invariant Federated Learning with Shortcut-Averse Information-Theoretic Regularization) to address the out-of-distribution (OOD) generalization problem in personalized federated learning (PFL). By leveraging causal models and information-theoretic constraints, this approach aims to extract personalized invariant features while avoiding the pitfalls of spurious correlations.
With the rapid advancement of giant models, the paradigm of pre-training models followed by fine-tuning for specific downstream tasks has become increasingly popular. In response to the challenges faced by adapter-based fine-tuning due to insufficient data, and the scalability and inflexibility issues of existing federated fine-tuning solutions, we introduce Tomtit.
In this paper, we propose SwapPrompt, a novel framework that can effectively leverage the self-supervised contrastive learning to facilitate the test-time prompt adaptation. SwapPrompt employs a dual prompts paradigm, i.e., an online prompt and a target prompt that averaged from the online prompt to retain historical information.
PROTORE works by incorporating CLIP’s language-contrastive knowledge to identify the prototype of negative concepts, extract the negative features from outputs using the prototype as a prompt, and further refine the attention maps by retrieving negative features.
Serverless ML inference is an emerging cloud computing paradigm for low-cost, easy-to-manage inference services. In serverless ML inference, each call is executed in a container; however, the cold start of containers results in long inference delays.
Transformer model empowered architectures have become a pillar of cloud services that keeps reshaping our society. However, the dynamic query loads and heterogeneous user requirements severely challenge current transformer serving systems, which rely on pre-training multiple variants of a foundation model to accommodate varying service demands.
The recent success of pre-trained language models (PLMs) such as BERT has resulted in the development of various beneficial database middlewares, including natural language query interfaces and entity matching. This shift has been greatly facilitated by the extensive external knowledge of PLMs.