The key idea is to model feature interactions with cross features selectively and dynamically, by first transforming the input features into exponential space, and then determining the interaction order and interaction weights adaptively for each cross feature.
We present results from user studies in which Duoquest demonstrates a 62. 5% absolute increase in query construction accuracy over a state-of-the-art NLI and comparable accuracy to a PBE system on a more limited workload supported by the PBE system.
Deep learning has recently become very popular on account of its incredible success in many complex data-driven applications, such as image classification and speech recognition.
We show that programs for mapping entity mentions to their structures can be automatically generated using human-comprehensible labels.
In many complex applications such as healthcare, subject matter experts (e. g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results.
We propose a novel generic inverted index framework on the GPU (called GENIE), aiming to reduce the programming complexity of the GPU for parallel similarity search of different data types.