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Found 7 papers, 1 papers with code
Dimensional reduction of gradient-like stochastic systems with multiplicative noise via Fokker-Planck diffusion maps
Dimensional reduction techniques have long been used to visualize the structure and geometry of high dimensional data.
Biomedical knowledge graph-enhanced prompt generation for large language models
KG-RAG consistently enhanced the performance of LLMs across various prompt types, including one-hop and two-hop prompts, drug repurposing queries, biomedical true/false questions, and multiple-choice questions (MCQ).
Cell Population Growth Kinetics in the Presence of Stochastic Heterogeneity of Cell Phenotype
Recent studies at individual cell resolution have revealed phenotypic heterogeneity in nominally clonal tumor cell populations.
Explaining Deep Learning Models -- A Bayesian Non-parametric Approach
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
Explaining Deep Learning Models - A Bayesian Non-parametric Approach
The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
Image Matters: Visually modeling user behaviors using Advanced Model Server
So we propose to model user preference jointly with user behavior ID features and behavior images.
Towards Interrogating Discriminative Machine Learning Models
Our results indicate that the proposed approach not only outperforms the state-of-the-art technique in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of a learning model.
