24 citations · 34 across the 18 of their papers we have counts for
5 papers · 1 filter
On the Limits of Learning to Actively Learn Semantic Representations
Omri Koshorek, Gabriel Stanovsky, Yichu Zhou +2
One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation…
A Logic-Driven Framework for Consistency of Neural Models
Tao Li, Vivek Gupta, Maitrey Mehta +1
While neural models show remarkable accuracy on individual predictions, their internal beliefs can be inconsistent across examples. In this paper, we formalize such inconsistency a…
On Measuring and Mitigating Biased Inferences of Word Embeddings
Sunipa Dev, Tao Li, Jeff Phillips +1
Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them. We use this observa…
Augmenting Neural Networks with First-order Logic
Tao Li, Vivek Srikumar
Today, the dominant paradigm for training neural networks involves minimizing task loss on a large dataset. Using world knowledge to inform a model, and yet retain the ability to p…
Learning In Practice: Reasoning About Quantization
Annie Cherkaev, Waiming Tai, Jeff Phillips +1
There is a mismatch between the standard theoretical analyses of statistical machine learning and how learning is used in practice. The foundational assumption supporting the theor…