Abstract: We propose to learn the time-varying stochastic computational resource usage of software as a graph-structured Schrödinger bridge problem (SBP). In general, learning the computational ...
Researchers at Meta FAIR and the University of Edinburgh have developed a new technique that can predict the correctness of a large language model's (LLM) reasoning and even intervene to fix its ...
ABSTRACT: The accurate prediction of backbreak, a crucial parameter in mining operations, has a significant influence on safety and operational efficiency. The occurrence of this phenomenon is ...
Deep neural networks (DNNs), which power modern artificial intelligence (AI) models, are machine learning systems that learn hidden patterns from various types of data, be it images, audio or text, to ...
VFF-Net introduces three new methodologies: label-wise noise labelling (LWNL), cosine similarity-based contrastive loss (CSCL), and layer grouping (LG), addressing the challenges of applying a forward ...
Abstract: We introduce an interpretable and scalable graph-based system designed to predict biases in instructional achievement across diverse educational data. Aimed at large-scale learning platforms ...
BEIJING, Apr. 23, 2025––WiMi Hologram Cloud Inc. (NASDAQ: WIMI) ("WiMi" or the "Company"), a leading global Hologram Augmented Reality ("AR") Technology provider, announced the development of a ...
ABSTRACT: Graph burning is a model for describing the spread of influence in social networks and the generalized burning number b r ( G ) of graph G is a parameter to measure the speed of information ...
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