32 citations
6 papers
Should attention be all we need? The epistemic and ethical implications of unification in machine learning
Nic Fishman, Leif Hancox-Li
"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms th…
Counterfactual Explanations via Latent Space Projection and Interpolation
Brian Barr, Matthew R. Harrington, Samuel Sharpe +1
Counterfactual explanations represent the minimal change to a data sample that alters its predicted classification, typically from an unfavorable initial class to a desired target…
Epistemic values in feature importance methods: Lessons from feminist epistemology
Leif Hancox-Li, I. Elizabeth Kumar
As the public seeks greater accountability and transparency from machine learning algorithms, the research literature on methods to explain algorithms and their outputs has rapidly…
Quantifying Challenges in the Application of Graph Representation Learning
Antonia Gogoglou, C. Bayan Bruss, Brian Nguyen +2
Graph Representation Learning (GRL) has experienced significant progress as a means to extract structural information in a meaningful way for subsequent learning tasks. Current app…
A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework
Oluwatobi O. Olabiyi, Anish Khazane, Erik T. Mueller
In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to multi-turn dialogue by modifying the state-of-the-art hredGAN archite…
Global Explanations of Neural Networks: Mapping the Landscape of Predictions
Mark Ibrahim, Melissa Louie, Ceena Modarres +1
A barrier to the wider adoption of neural networks is their lack of interpretability. While local explanation methods exist for one prediction, most global attributions still reduc…