Jul 6, 2015 · This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or ...

2D Grid LSTM differs from traditional stacked LSTM by adding LSTM cells along the depth dimension of the network as well as the temporal dimension. That is, ...

The Grid LSTM is used to define a novel two-dimensional translation model, the Reencoder, and it is shown that it outperforms a phrase-based reference system.

The 2D Grid. LSTM simply extends this architecture by proposing that the LSTM memory cells and gates should extend to the vertical depth dimension as well as ...

This paper introduces Grid Long Short-Term Memory, a network of LSTM cells arranged in a multidimensional grid that can be applied to vectors, sequences or ...

We introduce Grid LSTM, a network that is arranged in a grid of one or more dimensions. The network has LSTM cells along any or all of the dimensions of the ...

LSTM. 3. Grid LSTM. 4. Experiments. 5. Conclusion. 3 / 32. Page 4. LSTM. Long Short-Term Memory (LSTM) networks have gates that control access to memory cells.

In this paper, we extend stacked long short-term memory (LSTM) RNNs by using grid LSTM blocks that formulate computation along not only the temporal dimension, ...

Harder to capture longer term interactions. Solution: “Long” “short-term” memory cells, controlled by gates that allow information to pass unmodified over ...

In this paper, we propose a GLDNN-based (grid long short-term memory, deep neural network) endpointer model and show that it provides significant improvements ...