5 accepted papers
The Matthew effect is a notorious issue in Recommender Systems (RSs), i.e., the rich get richer and the poor get poorer, wherein popular items are overexposed while less popular ones are regularly ignored. Most methods examine Matthew effect in static or nearly-static recommendation scenarios. Howev…
The Matthew effect is a big challenge in Recommender Systems (RSs), where popular items tend to receive increasing attention, while less popular ones are often overlooked, perpetuating existing disparities. Although many existing methods attempt to mitigate Matthew effect in the static or quasi-stat…
Realtime speech denoising has been long studied. Almost all existing methods process the incoming data stream using a sliding window of fixed-size. Yet, we show that the use of fixed-size sliding window may lead to an accumulating lag, especially in presence of other background computing processes t…
We introduce a deep learning model for speech denoising, a long-standing challenge in audio analysis arising in numerous applications. Our approach is based on a key observation about human speech: there is often a short pause between each sentence or word. In a recorded speech signal, those pauses…
We propose a novel imaging method for near-complete, surround, 3D reconstruction of geometrically complex objects, in a single shot. The key idea is to augment a time-of-flight (ToF) based 3D sensor with a multi-mirror system, called a light-trap. The shape of the trap is chosen so that light rays e…