Text-to-SQL parsing tackles the problem of mapping natural language questions to executable SQL queries.
Lifelong learning (LL) is vital for advanced task-oriented dialogue (ToD) systems.
However, non-Cartesian trajectories such as the radial trajectory need to be transformed onto a Cartesian grid in each iteration of the network training, slowing down the training process and posing inconvenience and delay during training.
The goal-oriented document-grounded dialogue aims at responding to the user query based on the dialogue context and supporting document.
Spiking Neural Networks (SNNs) are developed as a promising alternative to Artificial Neural networks (ANNs) due to their more realistic brain-inspired computing models.
The bandpass filter gain of a channel is adapted dynamically to the input amplitude so that the average output spike rate stays within a defined range.
The pruned networks running on Spartus hardware achieve weight sparsity levels of up to 96% and 94% with negligible accuracy loss on the TIMIT and the Librispeech datasets.
Lower leg prostheses could improve the life quality of amputees by increasing comfort and reducing energy to locomote, but currently control methods are limited in modulating behaviors based upon the human's experience.
This paper presents a Gated Recurrent Unit (GRU) based recurrent neural network (RNN) accelerator called EdgeDRNN designed for portable edge computing.
Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.
This system has a labor-efficient sketching interface, that allows the user to draw freehand imprecise yet expressive 2D lines representing the contours of facial features.