74 citations · 182 across the 5 of their papers we have counts for
5 papers
Scaling Up Models and Data with and
Adam Roberts, Hyung Won Chung, Anselm Levskaya +40
Recent neural network-based language models have benefited greatly from scaling up the size of training datasets and the number of parameters in the models themselves. Scaling can…
Compositional Generalization in Semantic Parsing: Pre-training vs. Specialized Architectures
Daniel Furrer, Marc van Zee, Nathan Scales +1
While mainstream machine learning methods are known to have limited ability to compositionally generalize, new architectures and techniques continue to be proposed to address this…
Intention as Commitment toward Time
Marc van Zee, Dragan Doder, Leendert van der Torre +3
In this paper we address the interplay among intention, time, and belief in dynamic environments. The first contribution is a logic for reasoning about intention, time and belief,…
Measuring Compositional Generalization: A Comprehensive Method on Realistic Data
Daniel Keysers, Nathanael Schärli, Nathan Scales +11
State-of-the-art machine learning methods exhibit limited compositional generalization. At the same time, there is a lack of realistic benchmarks that comprehensively measure this…
AGM-Style Revision of Beliefs and Intentions from a Database Perspective (Preliminary Version)
Marc van Zee, Dragan Doder
We introduce a logic for temporal beliefs and intentions based on Shoham's database perspective. We separate strong beliefs from weak beliefs. Strong beliefs are independent from i…