DEEP LEARNING PAPER OF THE WEEK
DLPOTW
DLPOTW: BEYOND ACCURACY: BEHAVIORAL TESTING OF NLP MODELS WITH CHECKLIST
DEEP LEARNING PAPER OF THE WEEK
DLPOTW: OBJECT-CENTRIC LEARNING WITH SLOT ATTENTION
๐ Deep Learning Paper of the Week
DLPOTW: DISCOVERING SYMBOLIC MODELS FROM DEEP LEARNING WITH INDUCTIVE BIASES
๐ DEEP LEARNING PAPER OF THE WEEK Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho Abstract "We develop a general approach to distill symbolic representations of a learned deep model by introducing strong inductive biases. We focus on Graph Neural Networks (GNNs). The technique works as follows: we first encourage sparse latent representations when we train … Continue reading DLPOTW: DISCOVERING SYMBOLIC MODELS FROM DEEP LEARNING WITH INDUCTIVE BIASES
DLPOTW: UNSUPERVISED LEARNING OF PROBABLY SYMMETRIC DEFORMABLE 3D OBJECTS FROM IMAGES IN THE WILD
DEEP LEARNING PAPER OF THE WEEK | CVPR 2020 BEST PAPER AWARD
DLPOTW: UNSUPERVISED TRANSLATION OF PROGRAMMING LANGUAGES
Deep Learning paper of the week.