1 citations · 1 across the 6 of their papers we have counts for
6 papers
Provenance Networks: End-to-End Exemplar-Based Explainability
Ali Kayyam, Anusha Madan Gopal, M. Anthony Lewis
We introduce provenance networks, a novel class of neural models designed to provide end-to-end, training-data-driven explainability. Unlike conventional post-hoc methods, provenan…
Quantifying Compositionality of Classic and State-of-the-Art Embeddings
Zhijin Guo, Chenhao Xue, Zhaozhen Xu +4
For language models to generalize correctly to novel expressions, it is critical that they exploit access compositional meanings when this is justified. Even if we don't know what…
Behavioural vs. Representational Systematicity in End-to-End Models: An Opinionated Survey
Ivan Vegner, Sydelle de Souza, Valentin Forch +2
A core aspect of compositionality, systematicity is a desirable property in ML models as it enables strong generalization to novel contexts. This has led to numerous studies propos…
Grounded learning for compositional vector semantics
Martha Lewis
Categorical compositional distributional semantics is an approach to modelling language that combines the success of vector-based models of meaning with the compositional power of…
Compositional Fusion of Signals in Data Embedding
Zhijin Guo, Zhaozhen Xu, Martha Lewis +1
Embeddings in AI convert symbolic structures into fixed-dimensional vectors, effectively fusing multiple signals. However, the nature of this fusion in real-world data is often unc…
EXTRACT: Explainable Transparent Control of Bias in Embeddings
Zhijin Guo, Zhaozhen Xu, Martha Lewis +1
Knowledge Graphs are a widely used method to represent relations between entities in various AI applications, and Graph Embedding has rapidly become a standard technique to represe…