347 citations · 684 across the 26 of their papers we have counts for
8 papers · 1 filter
Active Learning of Non-semantic Speech Tasks with Pretrained Models
Harlin Lee, Aaqib Saeed, Andrea L. Bertozzi
Pretraining neural networks with massive unlabeled datasets has become popular as it equips the deep models with a better prior to solve downstream tasks. However, this approach ge…
On Out-of-Distribution Detection for Audio with Deep Nearest Neighbors
Zaharah Bukhsh, Aaqib Saeed
Out-of-distribution (OOD) detection is concerned with identifying data points that do not belong to the same distribution as the model's training data. For the safe deployment of p…
COCOA: Cross Modality Contrastive Learning for Sensor Data
Shohreh Deldari, Hao Xue, Aaqib Saeed +2
Self-Supervised Learning (SSL) is a new paradigm for learning discriminative representations without labelled data and has reached comparable or even state-of-the-art results in co…
Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels
Vasileios Tsouvalas, Aaqib Saeed, Tanir Ozcelebi +1
Federated Learning (FL) is a distributed machine learning paradigm that enables learning models from decentralized private datasets, where the labeling effort is entrusted to the c…
Automatic Sleep Scoring from Large-scale Multi-channel Pediatric EEG
Harlin Lee, Aaqib Saeed
Sleep is particularly important to the health of infants, children, and adolescents, and sleep scoring is the first step to accurate diagnosis and treatment of potentially life-thr…
Distilled Non-Semantic Speech Embeddings with Binary Neural Networks for Low-Resource Devices
Harlin Lee, Aaqib Saeed
This work introduces BRILLsson, a novel binary neural network-based representation learning model for a broad range of non-semantic speech tasks. We train the model with knowledge…