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...
![[2602.14456] Traceable Latent Variable Discovery Based on Multi-Agent Collaboration](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
The paper presents TLVD, a novel causal modeling framework that integrates large language models with traditional causal discovery algori...
The paper introduces Selective Spectral Decay (S2D), a method to improve quantization in neural networks by addressing activation outlier...
This article presents a unified framework for evaluating the robustness of machine-learning interpretability, specifically in the context...
This article presents an information-theoretic framework analyzing the role of data augmentation in machine learning, focusing on its imp...
This paper presents a multi-class online fuzzy classifier designed for dynamic environments, extending traditional two-class fuzzy classi...
The paper presents GraFSTNet, a novel framework for cellular traffic prediction that integrates spatio-temporal modeling with time-freque...
This paper presents a novel framework for rumor detection on social networks, utilizing Large Language Models (LLMs) to enhance the ident...
This paper presents PhysioSER, a novel approach for speech emotion recognition that integrates physiological insights into vocal represen...
The paper presents Boltz as a competitive baseline for atom-level representation learning in molecular tasks, particularly in ADMET prope...
This article discusses a global audit of Large Language Models (LLMs) focusing on geographic and socioeconomic biases in AI governance, h...
This paper presents a framework for integrating unstructured text into causal inference, demonstrating its effectiveness against traditio...
The paper presents Radial-VCReg, a novel approach to self-supervised learning that enhances representation learning by addressing the lim...
The paper presents DELTAiF, a transfer learning framework that enhances LSTM-based demand forecasting for household hot water consumption...
The paper presents the Multi-Agent Debate (MAD) framework for tabular anomaly detection, leveraging multiple ML detectors and a large lan...
This paper discusses the need for explicit bias consideration in evaluating Large Language Models (LLMs) used in finance, identifying fiv...
The paper presents Robust Multi-Task Boosting using Clustering and Local Ensembling (RMB-CLE), a framework that enhances multi-task learn...
This paper explores optimal batch size scheduling in deep learning, revealing that task difficulty influences the effectiveness of batch ...
The paper introduces TS-Haystack, a benchmark for evaluating Time Series Language Models (TSLMs) on long-context retrieval tasks, address...
The paper discusses the development of a Deep Research AI agent, Bioptic Agent, designed for drug asset scouting, particularly in non-U.S...
This paper evaluates the effectiveness of malicious prompt classifiers under true distribution shifts, revealing significant performance ...
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