Data analysis, statistics, and data engineering
I'm building a crowdsourced photo collection platform (contributors take photos with smartphones, we auto-label with YOLO/CLIP + enrich w...
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...
![[2602.13521] Arming Data Agents with Tribal Knowledge](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper introduces Tk-Boost, a framework enhancing NL2SQL agents by integrating tribal knowledge to correct misconceptions during datab...
The paper presents a novel approach to multi-objective learning that adapts to changing data distributions, enhancing prediction accuracy...
This paper introduces the Variance-Reduced Unlearning (VRU) algorithm, which improves the $( ext{ε, δ})$-unlearning process by directly i...
BHyGNN+ introduces a self-supervised learning framework for heterophilic hypergraphs, enhancing representation learning without needing l...
This article explores the role of Edge AI in biodiversity monitoring, analyzing 82 studies to assess system types, architectural trade-of...
This paper presents a theoretical analysis demonstrating that additive control variates outperform self-normalisation techniques in off-p...
This paper explores coverage guarantees for pseudo-calibrated conformal prediction methods under distribution shifts, proposing a new alg...
The paper presents ToolSelect, a novel method for selecting task-specialized models in healthcare systems, demonstrating superior perform...
This paper explores the learning dynamics of Reinforcement Learning with Verifiable Rewards (RLVR), focusing on its effectiveness in over...
The paper introduces Goldilocks RL, a novel approach in reinforcement learning that adjusts task difficulty to enhance reasoning capabili...
This paper presents a new method for comparing clustering results that accommodates overlaps and outliers, addressing a gap in existing e...
The paper presents BEACONS, a framework for creating bounded-error neural solvers for partial differential equations (PDEs), enhancing re...
This article explores the metabolic cost of information processing in Poisson variational autoencoders, emphasizing the energy constraint...
The paper introduces Protect$^*$, a neuro-symbolic framework that enhances retrosynthesis by integrating Large Language Models with chemi...
This article presents a novel approach to Multi-Source Bayesian Optimization (MSBO) by integrating causality principles, resulting in a n...
This paper explores the stability of nonlinear dynamics in gradient descent (GD) and stochastic gradient descent (SGD), revealing that li...
This paper explores the use of graph neural networks (GNNs) for predicting algorithm performance in combinatorial auctions, focusing on i...
The paper presents a novel neural architecture for solving differential equations using Horner polynomials, emphasizing minimal parameter...
The paper presents a novel nonparametric algorithm for re-calibrating predictive distributions in regression, addressing the challenge of...
This paper explores the concept of scale redundancy in positively homogeneous neural networks, introducing gauge-adapted coordinates and ...
Get the latest news, tools, and insights delivered to your inbox.
Daily or weekly digest • Unsubscribe anytime