Theory of Practice
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
How Proposal Novelty, Topical Diversity, and Theory-Practice Balance Shape Scholarly Outcomes in Funded Education Research
arXiv:2606.01127v1 Announce Type: new Abstract: Education research occupies a distinctive position in public science because it is expected to advance scholarly knowledge while also informing learning, teaching, participation, and workforce development. This study examines how the intellectual characteristics of NSF-funded education proposals are associated with the subsequent academic performance of funded scholars. Linking 8,715 NSF education awards from 1990 to 2020 with 84,519...
WildCat: Near-Linear Attention in Theory and Practice
arXiv:2602.10056v2 Announce Type: replace Abstract: We introduce WildCat, a high-accuracy, low-cost approach to compressing the attention mechanism in neural networks. While attention is a staple of modern network architectures, it is also notoriously expensive to deploy due to resource requirements that scale quadratically with the input sequence length $n$. WildCat avoids these quadratic costs by only attending over a small weighted coreset. Crucially, we select the coreset using a fast...
Kernel Methods in the Deep Ritz framework: Theory and practice
Announce Type: replace Abstract: In this contribution, kernel approximations are applied as ansatz functions within the Deep Ritz method. This allows to approximate weak solutions of elliptic partial differential equations with weak enforcement of boundary conditions using Nitsche's method. A priori error estimates are proven in different norms leveraging both standard results for weak solutions of elliptic equations and well-established convergence results for kernel methods.
Representations for multidimensional down-down deconvolution of ocean-bottom seismic data: theory and practical implications
Announce Type: replace Abstract: Multidimensional up-down deconvolution effectively eliminates surface-related multiples from ocean-bottom seismic data. Recently, several down-down deconvolution methods have been introduced as attractive alternatives. Whereas multidimensional up-down deconvolution fully accounts for lateral variations of the medium parameters, the underlying theory of some of the down-down deconvolution methods is essentially based on the assumption that the medium is...
Small-angle solution scattering: from fundamental theory to practical approximations
arXiv:2606.05007v1 Announce Type: new Abstract: Small-angle scattering (SAS) is widely used in structural biology, soft matter, and colloidal science to probe molecular structures in solution. SAS rests on a single physical principle: wave interference from a distribution of scatterers, averaged over orientations. Yet the theoretical foundations of SAS are spread across the literature, often based on differing notation, definitions, and implicit assumptions.
Model-Agnostic Signal Discovery with Machine Learning: Bridging the Gap Between Theory and Practice
arXiv:2605.31103v1 Announce Type: new Abstract: Searches for new phenomena in complex scientific data are predominantly model-dependent, optimized for specific hypotheses, and therefore limited in their coverage of the space of possible signals. Recently, new AI-based model-agnostic search strategies, many of which have been pioneered in high-energy physics, have been proposed which provide a complementary paradigm, prioritizing broad exploration over tailored analyses.
Deep Learning as the Disciplined Construction of Tame Objects
arXiv:2509.18025v2 Announce Type: replace-cross Abstract: One can see deep-learning models as compositions of functions within the so-called tame geometry. In this expository note, we give an overview of some topics at the interface of tame geometry (also known as o-minimality), optimization theory, and deep learning theory and practice. To do so, we gradually introduce the concepts and tools used to build convergence guarantees for stochastic gradient descent in a general nonsmooth...
Principles and Practice of Deep Representation Learning: or a Mathematical Theory of Memory
new Abstract: In the current era of deep learning and especially generative models, there is significant investment in training very large generative models. Thus far, such models have been "black boxes" that are difficult to understand in the sense that they have opaque internal mechanisms, leading to difficulties in interpretability, reliability, and control. Naturally, this lack of understanding has led to both hype and fear.
Towards Guaranteed Optimal PID Tuning for Uncertain Nonlinear Systems
arXiv:2606.04787v1 Announce Type: new Abstract: Despite the widespread use of PID controllers in engineering practice, designing optimal PID parameters has long been regarded as a challenging problem in both theory and practice, particularly when faced with uncertain nonlinear dynamical systems. Based on the authors' PID control theory established recently for MIMO nonlinear uncertain systems (Zhao and Guo, 2022), which provides a concrete PID parameter set for global stability of PID...
Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalization
arXiv:2606.02300v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse domains, yet personalizing their outputs to individual users remains an open challenge. Existing approaches predominantly adopt a flat behavioral paradigm, aggregating user behaviors without an explicit account of how they are organized into deeper behavioral structures. In this work, we draw on Pierre Bourdieu's Theory of Practice to propose PHF...