Data analysis, statistics, and data engineering
UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...
Hi all, I’m curious about the current review dynamics for ICML 2026, especially after the rebuttal phase. For those who are reviewers (or...
MIT Professor Rafael Gómez-Bombarelli discusses the transformative potential of AI in scientific research, emphasizing its role in materi...
![[2601.21315] Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
This paper presents a novel distributionally robust learning framework for multi-source unsupervised domain adaptation, addressing challe...
This article systematically compares various explainability methods for detecting hardware trojans, focusing on their effectiveness in pr...
The paper introduces PhysE-Inv, a novel physics-encoded inverse modeling framework designed to improve Arctic snow depth prediction by in...
This paper presents a novel framework for inverting Self-Organizing Maps (SOMs) to recover original inputs from activation patterns, intr...
This paper presents a self-augmented mixture-of-experts model aimed at improving Quality of Service (QoS) prediction by leveraging iterat...
This article presents a two-stage multitask learning framework for analyzing EEG signals, focusing on denoising and representation learni...
HeurekaBench introduces a benchmarking framework for AI co-scientists, enabling rigorous evaluation of LLM-based systems through realisti...
This paper presents a reinforcement learning framework for adaptive precision tuning of linear solvers, enhancing computational efficienc...
The paper presents a novel defense mechanism against adversarial attacks in machine learning using a soft-gated fractional mixture-of-exp...
This article introduces ECHO, a benchmark for evaluating long-range graph propagation in graph neural networks (GNNs), addressing a criti...
The paper presents ML-Tool-Bench, a benchmark for evaluating tool-augmented planning in machine learning tasks, addressing the limitation...
The paper introduces SelfAI, a self-directed framework designed for long-horizon scientific discovery, emphasizing efficient exploration ...
The paper presents PoTable, a novel approach to table reasoning that integrates systematic thinking through a plan-then-execute mechanism...
This article presents RectiCast, a novel framework for improving precipitation nowcasting by addressing distribution shifts in deep learn...
FlowCast introduces a novel probabilistic model for precipitation nowcasting using Conditional Flow Matching, improving accuracy and effi...
The paper introduces the Stuart-Landau Oscillatory Graph Neural Network (SLGNN), a novel architecture that addresses oversmoothing and va...
The paper presents an innovative approach using an adaptive Runge-Kutta method for spatiotemporal prediction, enhancing model accuracy in...
This article presents the non-conservative generalized Schrödinger bridge (NCGSB), a novel framework for modeling stochastic processes th...
This paper explores the theoretical framework behind normalization layers in neural networks, demonstrating their role in controlling cap...
This article presents a novel generative AI model, FM-Cast, which enhances the probabilistic forecasting of Sudden Stratospheric Warmings...
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