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Section: Events
Date: March 31, 2022
Time: 10:00 AM ET (New York Time)
Title: Revisiting MIMO from a Circuits Perspective
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Section: Events
Date: April 12, 2022
Time: 9:00 AM ET (New York Time)
Title: Model-Driven Deep Learning for MIMO Detection
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Section: Events
Date: March 25, 2022
Time: 9:00 AM ET (New York Time)
Title: Fractional Programming for Communication Systems
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Section: Events
Date: February 24, 2022
Time: 10:00 AM ET (New York Time)
Title: Massive MIMO Systems with One-bit Spatial Sigma-Delta ADCs
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Section: Publications & Resources
Brain data are inherently large scale, multidimensional, and noisy. Indeed, advances in imaging and sensor technology allow recordings of ever-increasing spatio-temporal resolution. Multidimensional, as time series data are recorded at multiple locations (electrodes, voxels), from multiple subjects, under various conditions.
Section: Events
Date: 6 December 2022
Time: 3:00 PM (Central European Time (CET))
Title: Artificial Intelligence for Applications in Neurology
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Section: Events
Date: 13 December 2022
Time: 10:00 AM ET (New York Time)
Title: Deep Learning for All-in-Focus Imaging
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Section: Publications & Resources
Recent years, face recognition has made a remarkable breakthrough due to the emergence of deep learning. However, compared with frontal face recognition, many deep face recognition models still suffer serious performance degradation when handling profile faces. To address this issue, we propose a novel Frontal-Centers Guided Loss (FCGFace) to obtain highly discriminative features for face recognition. Most existing discriminative feature learning approaches project features from the same class into a separated latent subspace.
Section: Publications & Resources
Audio pattern recognition is an important research topic in the machine learning area, and includes several tasks such as audio tagging, acoustic scene classification, music classification, speech emotion classification and sound event detection. In this blog, we introduce pretrained audio neural networks (PANNs) trained on the large-scale AudioSet dataset. These PANNs are transferred to other audio related tasks. We investigate the performance and computational complexity of PANNs modeled by a variety of convolutional neural networks. We propose an architecture called Wavegram-Logmel-CNN using both log-mel spectrogram and waveform as input feature.
Section: Events
Publication: IEEE Journal of Selected Topics in Signal Processing
Manuscript Due: 1 March 2023
Publication Date: July 2024
CFP Document
