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Found 18 papers, 6 papers with code
Les Houches Lectures on Community Ecology: From Niche Theory to Statistical Mechanics
We then shift our focus by analyzing these same models in "high-dimensions" (i. e. in the limit where the number of species and resources in the ecosystem becomes large) and discuss how such complex ecosystems can be analyzed using methods from the statistical physics of disordered systems such as the cavity method and Random Matrix Theory.
A universal niche geometry governs the response of ecosystems to environmental perturbations
How ecosystems respond to environmental perturbations is a fundamental question in ecology, made especially challenging due to the strong coupling between species and their environment.
Geometry of ecological coexistence and niche differentiation
A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence.
Emergent competition shapes the ecological properties of multi-trophic ecosystems
Ecosystems are commonly organized into trophic levels -- organisms that occupy the same level in a food chain (e. g., plants, herbivores, carnivores).
Bias-variance decomposition of overparameterized regression with random linear features
We show that the linear random features model exhibits three phase transitions: two different transitions to an interpolation regime where the training error is zero, along with an additional transition between regimes with large bias and minimal bias.
Cross-feeding shapes both competition and cooperation in microbial ecosystems
Recent work suggests that cross-feeding -- the secretion and consumption of metabolic biproducts by microbes -- is essential for understanding microbial ecology.
The Geometry of Over-parameterized Regression and Adversarial Perturbations
Classical regression has a simple geometric description in terms of a projection of the training labels onto the column space of the design matrix.
Understanding Species Abundance Distributions in Complex Ecosystems of Interacting Species
Niche and neutral theory are two prevailing, yet much debated, ideas in ecology proposed to explain the patterns of biodiversity.
Arnol'd Tongues in Oscillator Systems with Nonuniform Spatial Driving
We find that the spatial distribution of the drive signal controls the frequency ranges over which oscillators synchronize to the drive and that boundary conditions strongly influence synchronization to external drives for the CGLE.
Pattern Formation and Solitons
Tregs self-organize into a "computing ecosystem" and implement a sophisticated optimization algorithm for mediating immune response
Regulatory T cells (Tregs) play a crucial role in mediating immune response.
Memorizing without overfitting: Bias, variance, and interpolation in over-parameterized models
In both models, increasing the number of fit parameters leads to a phase transition where the training error goes to zero and the test error diverges as a result of the variance (while the bias remains finite).
Machine Learning as Ecology
Machine learning methods have had spectacular success on numerous problems.
Diverse communities behave like typical random ecosystems
In 1972, Robert May triggered a worldwide research program studying ecological communities using random matrix theory.
A high-bias, low-variance introduction to Machine Learning for physicists
The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists.
Comment on "Why does deep and cheap learning work so well?" [arXiv:1608.08225]
", Lin and Tegmark claim to show that the mapping between deep belief networks and the variational renormalization group derived in [arXiv:1410. 3831] is invalid, and present a "counterexample" that claims to show that this mapping does not hold.
Bayesian feature selection with strongly-regularizing priors maps to the Ising Model
Identifying small subsets of features that are relevant for prediction and/or classification tasks is a central problem in machine learning and statistics.
An exact mapping between the Variational Renormalization Group and Deep Learning
Here, we show that deep learning is intimately related to one of the most important and successful techniques in theoretical physics, the renormalization group (RG).
Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation
Feature selection, identifying a subset of variables that are relevant for predicting a response, is an important and challenging component of many methods in statistics and machine learning.
