arXiv subjects

Tim Sainburg

Publications and source records attributed to Tim Sainburg.

6 recordsLinked to original sources

arXiv2026

The Cartesian Cut in Agentic AI

LLMs gain competence by predicting words in human text, which often reflects how people perform tasks. Consequently, coupling an LLM to an engineered runtime turns prediction into control: outputs trigger interventions that enact goal-oriented behavior. We argue that a central design lever is where control resides in these systems. Brains embed prediction within layered feedback controllers calibrated by the consequences of action. By contrast, LLM agents implement Cartesian agency: a learned core coupled to an engineered runtime via a symbolic interface that externalizes control state and policies. The split enables bootstrapping, modularity, and governance, but can induce sensitivity and bottlenecks. We outline bounded services, Cartesian agents, and integrated agents as contrasting approaches to control that trade off autonomy, robustness, and oversight.

cs.AI↗

arXiv2024

Noisereduce: Domain General Noise Reduction for Time Series Signals

Extracting signals from noisy backgrounds is a fundamental problem in signal processing across a variety of domains. In this paper, we introduce Noisereduce, an algorithm for minimizing noise across a variety of domains, including speech, bioacoustics, neurophysiology, and seismology. Noisereduce uses spectral gating to estimate a frequency-domain mask that effectively separates signals from noise. It is fast, lightweight, requires no training data, and handles both stationary and non-stationary noise, making it both a versatile tool and a convenient baseline for comparison with domain-specific applications. We provide a detailed overview of Noisereduce and evaluate its performance on a variety of time-domain signals.

eess.SP↗

arXiv2022

American postdoctoral salaries do not account for growing disparities in cost of living

The National Institute of Health (NIH) sets postdoctoral (postdoc) trainee stipend levels that many American institutions and investigators use as a basis for postdoc salaries. Although salary standards are held constant across universities, the cost of living in those universities' cities and towns vary widely. Across non-postdoc jobs, more expensive cities pay workers higher wages that scale with an increased cost of living. This work investigates the extent to which postdoc wages account for cost-of-living differences. More than 27,000 postdoc salaries across all US universities are analyzed alongside measures of regional differences in cost of living. We find that postdoc salaries do not account for cost-of-living differences, in contrast with the broader labor market in the same cities and towns. Despite a modest increase in income in high cost of living areas, real (cost of living adjusted) postdoc salaries differ by 29% ($15k 2021 USD) between the least and most expensive areas. Cities that produce greater numbers of tenure-track faculty relative to students such as Boston, New York, and San Francisco are among the most impacted by this pay disparity. The postdoc pay gap is growing and is well-positioned to incur a greater financial burden on economically disadvantaged groups and contribute to faculty hiring disparities in women and racial minorities.

econ.GN↗

arXiv2021

A Primer on Deep Learning for Causal Inference

This review systematizes the emerging literature for causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction on how deep learning can be used to estimate/predict heterogeneous treatment effects and extend causal inference to settings where confounding is non-linear, time varying, or encoded in text, networks, and images. To maximize accessibility, we also introduce prerequisite concepts from causal inference and deep learning. The survey differs from other treatments of deep learning and causal inference in its sharp focus on observational causal estimation, its extended exposition of key algorithms, and its detailed tutorials for implementing, training, and selecting among deep estimators in Tensorflow 2 available at github.com/kochbj/Deep-Learning-for-Causal-Inference.

cs.LG↗

arXiv2020

Parametric UMAP embeddings for representation and semi-supervised learning

UMAP is a non-parametric graph-based dimensionality reduction algorithm using applied Riemannian geometry and algebraic topology to find low-dimensional embeddings of structured data. The UMAP algorithm consists of two steps: (1) Compute a graphical representation of a dataset (fuzzy simplicial complex), and (2) Through stochastic gradient descent, optimize a low-dimensional embedding of the graph. Here, we extend the second step of UMAP to a parametric optimization over neural network weights, learning a parametric relationship between data and embedding. We first demonstrate that Parametric UMAP performs comparably to its non-parametric counterpart while conferring the benefit of a learned parametric mapping (e.g. fast online embeddings for new data). We then explore UMAP as a regularization, constraining the latent distribution of autoencoders, parametrically varying global structure preservation, and improving classifier accuracy for semi-supervised learning by capturing structure in unlabeled data. Google Colab walkthrough: https://colab.research.google.com/drive/1WkXVZ5pnMrm17m0YgmtoNjM_XHdnE5Vp?usp=sharing

cs.LG↗

arXiv2019

Generative adversarial interpolative autoencoding: adversarial training on latent space interpolations encourage convex latent distributions

We present a neural network architecture based upon the Autoencoder (AE) and Generative Adversarial Network (GAN) that promotes a convex latent distribution by training adversarially on latent space interpolations. By using an AE as both the generator and discriminator of a GAN, we pass a pixel-wise error function across the discriminator, yielding an AE which produces non-blurry samples that match both high- and low-level features of the original images. Interpolations between images in this space remain within the latent-space distribution of real images as trained by the discriminator, and therfore preserve realistic resemblances to the network inputs. Code available at https://github.com/timsainb/GAIA

cs.LG↗