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Found 10 papers, 4 papers with code
Opportunities and Risks of LLMs for Scalable Deliberation with Polis
In this paper, we explore the opportunities and risks associated with applying Large Language Models (LLMs) towards challenges with facilitating, moderating and summarizing the results of Polis engagements.
Open High-Resolution Satellite Imagery: The WorldStrat Dataset -- With Application to Super-Resolution
We hereby hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop from free public low-resolution Sentinel2 imagery the same power of analysis allowed by costly private high-resolution imagery.
Objects of violence: synthetic data for practical ML in human rights investigations
We introduce a machine learning workflow to search for, identify, and meaningfully triage videos and images of munitions, weapons, and military equipment, even when limited training data exists for the object of interest.
HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion
Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.
Ranked #6 on
Multi-Frame Super-Resolution
on PROBA-V
DECoVaC: Design of Experiments with Controlled Variability Components
Reproducible research in Machine Learning has seen a salutary abundance of progress lately: workflows, transparency, and statistical analysis of validation and test performance.
A large-scale crowdsourced analysis of abuse against women journalists and politicians on Twitter
We report the first, to the best of our knowledge, hand-in-hand collaboration between human rights activists and machine learners, leveraging crowd-sourcing to study online abuse against women on Twitter.
Approximate Hubel-Wiesel Modules and the Data Structures of Neural Computation
This paper describes a framework for modeling the interface between perception and memory on the algorithmic level of analysis.
Weight Uncertainty in Neural Networks
We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo
Here we discuss how to construct the perturbation kernels that are required in ABC SMC approaches, in order to construct a set of distributions that start out from a suitably defined prior and converge towards the unknown posterior.
Computation
