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54 papers · 1 filter
Deep Reinforcement Learning and the Deadly Triad
Hado van Hasselt, Yotam Doron, Florian Strub +3
We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, b…
From pixels to percepts: Highly robust edge perception and contour following using deep learning and an optical biomimetic tactile sensor
Nathan F. Lepora, Alex Church, Conrad De Kerckhove +2
Deep learning has the potential to have the impact on robot touch that it has had on robot vision. Optical tactile sensors act as a bridge between the subjects by allowing techniqu…
Neighbourhood Consensus Networks
Ignacio Rocco, Mircea Cimpoi, Relja Arandjelović +3
We address the problem of finding reliable dense correspondences between a pair of images. This is a challenging task due to strong appearance differences between the corresponding…
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles
Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue +3
While deep learning has led to remarkable results on a number of challenging problems, researchers have discovered a vulnerability of neural networks in adversarial settings, where…
CGMH: Constrained Sentence Generation by Metropolis-Hastings Sampling
Ning Miao, Hao Zhou, Lili Mou +2
In real-world applications of natural language generation, there are often constraints on the target sentences in addition to fluency and naturalness requirements. Existing languag…
Systematic Generalization: What Is Required and Can It Be Learned?
Dzmitry Bahdanau, Shikhar Murty, Michael Noukhovitch +3
Numerous models for grounded language understanding have been recently proposed, including (i) generic models that can be easily adapted to any given task and (ii) intuitively appe…