Causal discovery from observational data is fundamental to statistics and machine learning, yet determining causal direction without interventions necessitates structural assumptions. Existing identifiability research primarily focuses on continuous variables under additive noise models, often neglecting mixed datasets...
Sambit Mishra, Ying-Ying Wang, Christine K. Johnson et al.· 1 citation
A popular selling point of diffusion large language models (dLLMs) is their capacity for parallelism: the ability to generate sequences of text far more efficiently than autoregressive models, which require one forward pass per token. Yet among the many competing paradigms for dLLMs, from masked to uniform to Gaussian...
The analysis separates the contributions of initial conditions, fast-timescale tracking error, Markovian dependence, and timescale coupling, thereby clarifying the origin of the $\beta^2/\alpha^2$ term.
Djamel Rassem Lamouri, Dorian Baudry, Nicolas Gast· 0 citations
It is shown that the benefit belongs instead to the operating point: the relation between window length $S$ and dominant seasonal period $L$, an axis the standard protocol never varies, which exposes the regime.
Mert Onur Cakiroglu, Elham Buxton, M. Dalkilic et al.· 0 citations
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Antimicrobial resistance is expected to claim 10 million lives per year by 2050, and resource-limited regions are most affected. Raman spectroscopy is a novel pathogen diagnostic approach promising rapid and portable antibiotic resistance testing within a few hours, compared to days when using gold standard methods. Ho...
Kaitlin Zareno, Jarett Dewbury, Siamak K. Sorooshyari et al.· 0 citations
Overall, the results identify memory-scale mismatch as a concrete source of magnitude sensitivity in Adam and provide a mechanistic account of why tied momentum is dynamically distinctive.
Alberto Fernández-Hernández, Cristian Pérez-Corral, José I. Mestre et al.· 1 citation
The spherical Cauchy distribution is developed as a hyperspherical posterior that needs neither compromise and has broader angular tails and a smaller KL penalty than both alternatives, so equal local precision costs less regularization.
A Bayesian model fit to each domain's prediction-powered estimate, with an extension that borrows strength across a reporting taxonomy, and a new, approximately unbiased design-based cross-validation score for choosing among direct and smoothed estimators for estimation and validation are derived.
This work proposes Abstract Token Curriculum (ATC), a novel curriculum learning framework that elicits effective continuous intermediate representations without direct supervision or manual scratchpad design and shows ATC's effectiveness on graph reachability and arithmetic learning tasks.
Khashayar Gatmiry, Avrajit Ghosh, Parsa Mirtaheri et al.· 0 citations
This work proposes Compressed Active Subspaces (CAS), a scalable approach that first maps the model parameters to a compressed space using a structured isometric embedding and then constructs the active subspace within this reduced parameterization.
Thomas Flynn, Sanket R. Jantre, Byung-Jun Yoon et al.· 0 citations
Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test splits. Reported accuracy has been shown to shift substantially across different random splits of the same dataset, making published comparisons between architectures unre...
Naga Venkata Sai Jitin Jami, Thomas R. Altstidl, S. Hoefler et al.· 0 citations
Experiments with GRPO and GiGPO on ALFWorld, WebShop, and SearchQA show that both axes provide independent gains and that their combi- nation consistently achieves the strongest overall performance across model scales.
Ying-Xuan Zhuang, Bin-He Yu, Jing-Xiao Yang et al.· 1 citation
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.
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