Technology
OnFeatureLearninginthePresenceofSpurious Correlations
Ontheotherhand,wefindthatstrong regularization is not necessary for learning high-quality feature representations.
CanLanguageModels LearntoSkipSteps?
Yet they are still far from true intelligence, which opens up intriguing opportunities to explore the parallels of humans and modelbehaviors.
Overcoming Language Priors in Visual Question Answering with Adversarial Regularization
Sainandan Ramakrishnan, Aishwarya Agrawal, Stefan Lee
Neural Information Processing Systems http://nips.cc/
Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation
Samuel Ainsworth, Matt Barnes, Siddhartha Srinivasa
Neural Information Processing Systems http://nips.cc/
Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks
In this paper, we present a convex optimization framework to compute guaranteed upper bounds on the Lipschitz constant of DNNs both accurately and efficiently.
Neural Guided Constraint Logic Programming for Program Synthesis
Lisa Zhang, Gregory Rosenblatt, Ethan Fetaya, Renjie Liao, William Byrd, Matthew Might, Raquel Urtasun, Richard Zemel
We take integrating with a symbolic system further: we use its internal representation as input to the neural model.