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

π Deep Learning Paper of the Week

# DLPOTW: DISCOVERING SYMBOLIC MODELS FROM DEEP LEARNING WITH INDUCTIVE BIASES

π 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.