3 citations · 3 across the 2 of their papers we have counts for
4 papers
SLM: Learning a Discourse Language Representation with Sentence Unshuffling
Haejun Lee, Drew A. Hudson, Kangwook Lee +1
We introduce Sentence-level Language Modeling, a new pre-training objective for learning a discourse language representation in a fully self-supervised manner. Recent pre-training…
Learning by Abstraction: The Neural State Machine
Drew A. Hudson, Christopher D. Manning
We introduce the Neural State Machine, seeking to bridge the gap between the neural and symbolic views of AI and integrate their complementary strengths for the task of visual reas…
GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering
Drew A. Hudson, Christopher D. Manning
We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have develope…
Compositional Attention Networks for Machine Reasoning
Drew A. Hudson, Christopher D. Manning
We present the MAC network, a novel fully differentiable neural network architecture, designed to facilitate explicit and expressive reasoning. MAC moves away from monolithic black…