South America
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
We prove that under mild conditions there is no vanishing or exploding gradient in training SBO-RNN.
SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization
We prove that under mild conditions there is no vanishing or exploding gradient in training SBO-RNN.
Graph Neural Networks with Local Graph Parameters
In order to nicely formalize local graph parameters, we propose to extend vertex labels with homomorphism counts of small graph patterns.
Graph Neural Networks with Local Graph Parameters
In order to nicely formalize local graph parameters, we propose to extend vertex labels with homomorphism counts of small graph patterns.
The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning
In particular, the clearest example is the specialization of the ventral ("what") and dorsal
Learning to Synthesize Programs as Interpretable and Generalizable Policies Dweep Trivedi Jesse Zhang 1 Shao-Hua Sun 1 Joseph J. Lim 1 1 University of Southern California
Y et, these works either employ limited policy representations (e.g.
DARE: Disentanglement-Augmented Rationale Extraction
Code is released at https://github.com/yuelinan/DARE .
Ron Yosef Y uval Elovici
In this work, we introduce WinoGA ViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used as a dynamic evaluation benchmark.