This review synthesizes four recent studies spanning these strands, together with the broader literature on multi-agent orchestration, retrieval-augmented generation (RAG), large-language-model (LLM)-based knowledge-graph construction, and explainable AI, to build a unified picture of agentic AI as applied to intellige...
Subina S. B., A. B., Sania Jackson et al.· International Journal for Re...· 0 citations
Androgenetic alopecia (AGA) is a common non-scarring hair disorder with significant psychosocial impact. Conventional therapies have limited efficacy and require prolonged use, leading to increasing interest in regenerative treatments, such as platelet-rich plasma (PRP) and growth factor concentrate (GFC). This systema...
Shallu Bansal, Suhani Sharma, Palak Chouhan et al.· Journal of Cranio-Maxillofac...· 0 citations
Evidence concerning hemoglobin target attainment, high-density lipoprotein cholesterol, and long-term safety remains insufficient, and comparative clinical superiority of roxadustat-based over ESA-based strategies in PD is insufficient.
Pathology-based AI shows strong potential for glomerular disease classification, but current head-to-head evidence is insufficient to establish superiority over pathologists, particularly senior pathologists.
Min Tong, Ying-Ying Jiang, Rong-Xin Zhu et al.· Renal Failure· 0 citations
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Oxidative stress, mitochondrial dysfunction, gut-brain interactions, inflammatory responses, inflammatory responses, and machine learning-based prediction now represent major directions in this field of blood-based biomarker research.
Lei Xu, Jia-Ying Zhang, Hong-Biao Huang et al.· IBRO Neuroscience Reports· 0 citations
Improved reporting and use of TMFs may promote implementation effectiveness across HIV implementation efforts and offer insight into how D&I TMFs have been integrated in HIV implementation research.
N. Stadnick, Carrie Geremia, Hetsi Modi et al.· HIV Research & Clinical Prac...· 0 citations
Generative models are increasingly used to support decision-making in complex systems, where interventions may be joint and high-dimensional, and outcomes are high-dimensional. However, using generators for these decision-making settings are challenged by three problems. First, they are often trained on noisy logs with...
Raphael C Kim, Jingsen Zhu, Ramin Zabih et al.· 0 citations
Allocation stability is often used to justify Gaussian inference from bandit data, but when is it necessary? In this paper, we address this question for a two-armed, fixed-horizon variance-aware UCB policy with bounded reward distributions that may vary with the horizon. We find a sharp criterion in terms of the reward...
Yingying Fan, Yuxuan Han, Jinchi Lv et al.· 0 citations
Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems. Spatial-temporal graph neural networks (STGNNs) have recently achieved strong performance in point forecasting by jointly modeling temporal dynamics and relational dependencies across interconne...
Keivan Faghih Niresi, Alice Cicirello, Olga Fink· 0 citations
Theoretical studies of machine learning models commonly consider different limiting regimes in which the learning dynamics of gradient descent becomes theoretically tractable. It is, however, desirable to have a systematically obtained picture of qualitatively different extreme learning regimes for a particular type of...
We analyze gradient descent with Polyak (1964) heavy-ball momentum (HB) whose fixed momentum hyperparameter $\beta \in (0, 1)$ provides exponential decay of memory. Building on Kovachki and Stuart (2021), we prove that on an exponentially attractive invariant manifold the algorithm is exactly plain gradient descent wit...
We present Causal Posterior Estimation (CPE), a novel method for Bayesian inference in simulator models, where evaluating the likelihood function is intractable or computationally expensive, but generating outputs given parameter values is straightforward. CPE approximates the posterior distribution using flow matching...
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
