227 citations · 786 across the 29 of their papers we have counts for
36 papers
Estimation of embedding vectors in high dimensions
Golara Ahmadi Azar, Melika Emami, Alyson Fletcher +1
Embeddings are a basic initial feature extraction step in many machine learning models, particularly in natural language processing. An embedding attempts to map data tokens to a l…
A Deep Learning Sequential Decoder for Transient High-Density Electromyography in Hand Gesture Recognition Using Subject-Embedded Transfer Learning
Golara Ahmadi Azar, Qin Hu, Melika Emami +3
Hand gesture recognition (HGR) has gained significant attention due to the increasing use of AI-powered human-computer interfaces that can interpret the deep spatiotemporal dynamic…
ViT-MDHGR: Cross-day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding
Qin Hu, Golara Ahmadi Azar, Alyson Fletcher +2
Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) biosignals have been extensively investigated for myoelectric control of prosthetic devices, neurorobotics, and more…
Local Convergence of Gradient Descent-Ascent for Training Generative Adversarial Networks
Evan Becker, Parthe Pandit, Sundeep Rangan +1
Generative Adversarial Networks (GANs) are a popular formulation to train generative models for complex high dimensional data. The standard method for training GANs involves a grad…
Instability and Local Minima in GAN Training with Kernel Discriminators
Evan Becker, Parthe Pandit, Sundeep Rangan +1
Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood…
Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions
Mojtaba Sahraee-Ardakan, Melikasadat Emami, Parthe Pandit +2
Empirical observation of high dimensional phenomena, such as the double descent behaviour, has attracted a lot of interest in understanding classical techniques such as kernel meth…