Object-based reasoning in VQA
Mikyas T. Desta, Larry Chen, et al.
WACV 2018
We propose a new architecture called Memory-Augmented Encoder-Solver (MAES) that enables transfer learning to solve complex working memory tasks adapted from cognitive psychology. It uses dual recurrent neural network controllers, inside the encoder and solver, respectively, that interface with a shared memory module and is completely differentiable. We study different types of encoders in a systematic manner and demonstrate a unique advantage of multi-task learning in obtaining the best possible encoder. We show by extensive experimentation that the trained MAES models achieve task-size generalization, i.e., they are capable of handling sequential inputs 50 times longer than seen during training, with appropriately large memory modules. We demonstrate that the performance achieved by MAES far outperforms existing and well-known models such as the LSTM, NTM and DNC on the entire suite of tasks.
Mikyas T. Desta, Larry Chen, et al.
WACV 2018
Mika Göös, T. S. Jayram, et al.
ACM TOCT
T. S. Jayram, Jan Vondrák
APPROX/RANDOM 2014
T. S. Jayram, Erdal Arikan
IEEE Trans. Inf. Theory