227 citations · 786 across the 29 of their papers we have counts for
9 papers · 1 filter
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…
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…
Implicit Bias of Linear RNNs
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +2
Contemporary wisdom based on empirical studies suggests that standard recurrent neural networks (RNNs) do not perform well on tasks requiring long-term memory. However, precise rea…
Generalization Error of Generalized Linear Models in High Dimensions
Melikasadat Emami, Mojtaba Sahraee-Ardakan, Parthe Pandit +2
At the heart of machine learning lies the question of generalizability of learned rules over previously unseen data. While over-parameterized models based on neural networks are no…
Inference in Multi-Layer Networks with Matrix-Valued Unknowns
Parthe Pandit, Mojtaba Sahraee-Ardakan, Sundeep Rangan +2
We consider the problem of inferring the input and hidden variables of a stochastic multi-layer neural network from an observation of the output. The hidden variables in each layer…