Add to ORKGNeural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradicts this nature, rendering a significant share of their computations redundant. Parallel algorithms however may exploit their full computational power, therefore requiring fewer layers to be executed. This drastically reduces training times, as we observe when comparing parallel implementations of searching, sorting and finding strongly connected components to their sequential counterparts on the CLRS framework. Additionally, parallel versions achieve strongly superior predictive performance in most cases.Comment: 8 pages, 5 figures, To appear at the KLR Workshop at ICML 202
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
This session explores, through the use of formal methods, the “intuition” used in creating a paralle...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradict...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
The big-data is an oil of this century. A high amount of computational power is required to get know...
Traditional computational methods are highly structured and linear, properties which they derive fro...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Four paradigms that can be useful in developing parallel algorithms are discussed. These include com...
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy cons...
With the rapid development of big data technologies, how to dig out useful information from massive ...
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by ...
International audienceComputing in parallel means performing computation simultaneously, this genera...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
This session explores, through the use of formal methods, the “intuition” used in creating a paralle...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...
Neural algorithmic reasoners are parallel processors. Teaching them sequential algorithms contradict...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
The big-data is an oil of this century. A high amount of computational power is required to get know...
Traditional computational methods are highly structured and linear, properties which they derive fro...
It seems to be an everlasting discussion. Spending a lot of additional time and extra money to imple...
Long training times and non-ideal performance have been a big impediment in further continuing the u...
Four paradigms that can be useful in developing parallel algorithms are discussed. These include com...
Spiking neural networks (SNNs) have achieved orders of magnitude improvement in terms of energy cons...
With the rapid development of big data technologies, how to dig out useful information from massive ...
Recent advances in neural algorithmic reasoning with graph neural networks (GNNs) are propped up by ...
International audienceComputing in parallel means performing computation simultaneously, this genera...
Graph neural networks (GNNs) are among the most powerful tools in deep learning. They routinely solv...
Thesis (Master's)--University of Washington, 2018The recent success of Deep Neural Networks (DNNs) [...
This session explores, through the use of formal methods, the “intuition” used in creating a paralle...
We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to g...