283 citations · 285 across the 4 of their papers we have counts for
6 papers
GUIDE: Generative Utility Inference and Decision Engine
Anagha Tiwari, Alexander G. Gray, Nick Feamster +3
Measuring the preferences of human users remains a fundamental challenge of AI alignment. Existing elicitation approaches struggle to efficiently discover multidimensional preferen…
Transformers Learn Faster with Semantic Focus
Parikshit Ram, Kenneth L. Clarkson, Tim Klinger +2
Various forms of sparse attention have been explored to mitigate the quadratic computational and memory cost of the attention mechanism in transformers. We study sparse transformer…
Neural Reasoning Networks: Efficient Interpretable Neural Networks With Automatic Textual Explanations
Stephen Carrow, Kyle Harper Erwin, Olga Vilenskaia +5
Recent advances in machine learning have led to a surge in adoption of neural networks for various tasks, but lack of interpretability remains an issue for many others in which an…
What makes Models Compositional? A Theoretical View: With Supplement
Parikshit Ram, Tim Klinger, Alexander G. Gray
Compositionality is thought to be a key component of language, and various compositional benchmarks have been developed to empirically probe the compositional generalization of exi…
Compositional Program Generation for Few-Shot Systematic Generalization
Tim Klinger, Luke Liu, Soham Dan +3
Compositional generalization is a key ability of humans that enables us to learn new concepts from only a handful examples. Neural machine learning models, including the now ubiqui…
Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI
Dakuo Wang, Justin D. Weisz, Michael Muller +6
The rapid advancement of artificial intelligence (AI) is changing our lives in many ways. One application domain is data science. New techniques in automating the creation of AI, k…