To reduce the embedding dimensions of dense retrieval, this paper proposes a Conditional Autoencoder (ConAE) to compress the high-dimensional embeddings to maintain the same embedding distribution and better recover the ranking features.
Ranked #1 on Information Retrieval on MS MARCO
In this paper, we study the method of adding white noise to the DNN output to mitigate such attacks, with a unique focus on the trade-off analysis of noise level and query cost.
Hyperspectral Image(HSI) classification is the most vibrant field of research in the hyperspectral community, which aims to assign each pixel in the image to one certain category based on its spectral-spatial characteristics.
The proposed temporal coding scheme maps each event's arrival time and data into SNN spike time so that asynchronously-arrived events are processed immediately without delay.
To support this claim, we show that SNNs built with nonleaky neurons can have a less-complex and less-nonlinear input-output response.
Accurate real-time object recognition from sensory data has long been a crucial and challenging task for autonomous driving.
In this paper, we integrate spiking convolutional neural network (SCNN) with temporal coding into the YOLOv2 architecture for real-time object detection.
Spiking neural network (SNN) is interesting both theoretically and practically because of its strong bio-inspiration nature and potentially outstanding energy efficiency.
However, little work has been done for game image captioning which has some unique characteristics and requirements.
Then we apply factor analysis on the performance data to identify and quantize the intelligence factors and cognitive capabilities of the CR.