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20212024
most citedThe Shapley Value in Machine Learning

17 citations · 56 across the 11 of their papers we have counts for

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Showing 2023Show all

7 papers · 1 filter

cs.LG2023★ 1 cited

Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent

Lauren Watson, Eric Gan, Mohan Dantam +2

Differentially private stochastic gradient descent (DP-SGD) is known to have poorer training and test performance on large neural networks, compared to ordinary stochastic gradient…

cs.LG2023★ 1 cited

Accelerated Shapley Value Approximation for Data Evaluation

Lauren Watson, Zeno Kujawa, Rayna Andreeva +3

Data valuation has found various applications in machine learning, such as data filtering, efficient learning and incentives for data sharing. The most popular current approach to…

cs.LG2023★ 1 cited

Metric Space Magnitude for Evaluating the Diversity of Latent Representations

Katharina Limbeck, Rayna Andreeva, Rik Sarkar +1

The magnitude of a metric space is a novel invariant that provides a measure of the 'effective size' of a space across multiple scales, while also capturing numerous geometrical pr…

cs.LG2023★ 8 cited

Machine learning and Topological data analysis identify unique features of human papillae in 3D scans

Rayna Andreeva, Anwesha Sarkar, Rik Sarkar

The tongue surface houses a range of papillae that are integral to the mechanics and chemistry of taste and textural sensation. Although gustatory function of papillae is well inve…

cs.AI2023★ 9 cited

Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet

Gonzalo Martínez, Lauren Watson, Pedro Reviriego +3

The rapid adoption of generative Artificial Intelligence (AI) tools that can generate realistic images or text, such as DALL-E, MidJourney, or ChatGPT, have put the societal impact…

cs.LG2023

Metric Space Magnitude and Generalisation in Neural Networks

Rayna Andreeva, Katharina Limbeck, Bastian Rieck +1

Deep learning models have seen significant successes in numerous applications, but their inner workings remain elusive. The purpose of this work is to quantify the learning process…

cs.CV2023★ 16 cited

Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

Gonzalo Martínez, Lauren Watson, Pedro Reviriego +3

In the span of a few months, generative Artificial Intelligence (AI) tools that can generate realistic images or text have taken the Internet by storm, making them one of the techn…