www.epicpeople.org/theory-in-design-research/
In Praise of Theory in Design Research: How Levi-Strauss Redefined Workflow
Points: 81 | Comments: 33 | Author: kawera
arxiv.org/abs/1211.1813v3
Pro cdh-descent for cyclic homology and $K$-theory
In this paper we prove that cyclic homology, topological cyclic homology, and algebraic $K$-theory satisfy a pro Mayer--Vietoris property with respect to abstract blow-up squares of varieties, in both zero and finite characteristic. This may be inter...
arxiv.org/abs/1602.01682v2
The non-linear coupled spin 2 - spin 3 Cotton equation in three dimensions
In the context of three-dimensional conformal higher spin theory we derive, in the frame field formulation, the full non-linear spin 3 Cotton equation coupled to spin 2. This is done by solving the corresponding Chern-Simons gauge theory system of eq...
arxiv.org/abs/0905.3828v3
Quantum Theory of Ur Objects and General Relativity
The quantum theory of ur objects postulates that all existing physical objects and their properties are constructed from fundamental objects called ur objects being described by an element of a two dimensional complex Hilbert space. This approach i...
arxiv.org/abs/nlin/0111009v1
Complex Systems As Fundamental Theory Of Sports Coaching?
We argue that traditional Western science cannot adequately describe sports and other types of human behavior. Then we make an argument why a complex adaptive systems approach has the potential to provide the foundation for such a theory. We claim...
arxiv.org/abs/astro-ph/0311436v1
Quasar Atmospheres: Toward a "Low" Theory for Quasars
After 40 years we have no "low" theory of quasars to predict the atomic absorption and emission features so abundant in quasar spectra. Here I take the first step, using results selected for their diagnostic power to build an "observational paradig...
arxiv.org/abs/2108.12302v2
Manipulating the Dynamics of a Fermi Resonance with Light. A Direct Optimal Cont…
Direct optimal control theory for quantum dynamical problems presents itself as an interesting alternative to the traditional indirect optimal control. The method relies on the first discretize and then optimize paradigm, where a discretization of th...
arxiv.org/abs/0911.3077v1
Dimension theory for multimodal maps
This paper is devoted to the study of dimension theory, in particular multifractal analysis, for multimodal maps. We describe the Lyapunov spectrum, generalising previous results by Todd. We also study the multifractal spectrum of pointwise dimensi...
arxiv.org/abs/2307.13807v1
Sports Betting: an application of neural networks and modern portfolio theory to…
This paper presents a novel approach for optimizing betting strategies in sports gambling by integrating Von Neumann-Morgenstern Expected Utility Theory, deep learning techniques, and advanced formulations of the Kelly Criterion. By combining neural...
arxiv.org/abs/1211.0188v1
Galactic rotation curves in conformal gravity
We review some recent work by Mannheim and O'Brien on the systematics of galactic rotation curves in the conformal gravity theory. In this work the conformal theory was applied to a comprehensive, high quality sample of spiral galaxies whose rotation...
arxiv.org/abs/2005.08387v2
Spectral theory of the frame flow on hyperbolic 3-manifolds (with an appendix by…
We study the spectral theory and the resolvent of the vector field generating the frame flow of closed hyperbolic 3-dimensional manifolds on some family of anisotropic Sobolev spaces. We show the existence of a spectral gap and prove resolvent estima...
github.com/control-theory/gonzo
control-theory/gonzo
Gonzo! The Go based TUI log analysis tool (⭐ 2490)
www.youtube.com/@melrobbins
Mel Robbins - YouTube
Mel Robbins is the creator and host of The Mel Robbins Podcast, one of the most successful podcasts in the world, and a #1 New York Times bestselling author....
research.google/research-areas/algorithms-and-theory
Algorithms and Theory
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Google NotebookLM | AI Research Tool & Thinking Partner
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arxiv.org/abs/2306.13057v1
SQ Lower Bounds for Learning Bounded Covariance GMMs
We study the complexity of learning mixtures of separated Gaussians with common unknown bounded covariance matrix. Specifically, we focus on learning Gaussian mixture models (GMMs) on $\mathbb{R}^d$ of the form $P= \sum_{i=1}^k w_i \mathcal{N}(\bolds...
arxiv.org/abs/2403.04744v1
SQ Lower Bounds for Non-Gaussian Component Analysis with Weaker Assumptions
We study the complexity of Non-Gaussian Component Analysis (NGCA) in the Statistical Query (SQ) model. Prior work developed a general methodology to prove SQ lower bounds for this task that have been applicable to a wide range of contexts. In particu...
arxiv.org/abs/2305.08350v1
Uniform-PAC Guarantees for Model-Based RL with Bounded Eluder Dimension
Recently, there has been remarkable progress in reinforcement learning (RL) with general function approximation. However, all these works only provide regret or sample complexity guarantees. It is still an open question if one can achieve stronger pe...
arxiv.org/abs/2206.07690v2
ELUDE: Generating interpretable explanations via a decomposition into labelled a…
Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them difficult to understand. In an effort to make them more interpretable, sever...
arxiv.org/abs/2104.06970v3
Understanding the Eluder Dimension
We provide new insights on eluder dimension, a complexity measure that has been extensively used to bound the regret of algorithms for online bandits and reinforcement learning with function approximation. First, we study the relationship between the...