62 citations · 95 across the 19 of their papers we have counts for
9 papers · 1 filter
Space Expansion of Feature Selection for Designing more Accurate Error Predictors
Shayan Tabatabaei Nikkhah, Mehdi Kamal, Ali Afzali-Kusha +1
Approximate computing is being considered as a promising design paradigm to overcome the energy and performance challenges in computationally demanding applications. If the case wh…
Gradient Agreement as an Optimization Objective for Meta-Learning
Amir Erfan Eshratifar, David Eigen, Massoud Pedram
This paper presents a novel optimization method for maximizing generalization over tasks in meta-learning. The goal of meta-learning is to learn a model for an agent adapting rapid…
A Meta-Learning Approach for Custom Model Training
Amir Erfan Eshratifar, Mohammad Saeed Abrishami, David Eigen +1
Transfer-learning and meta-learning are two effective methods to apply knowledge learned from large data sources to new tasks. In few-class, few-shot target task settings (i.e. whe…
NullaNet: Training Deep Neural Networks for Reduced-Memory-Access Inference
Mahdi Nazemi, Ghasem Pasandi, Massoud Pedram
Deep neural networks have been successfully deployed in a wide variety of applications including computer vision and speech recognition. However, computational and storage complexi…
Deploying Customized Data Representation and Approximate Computing in Machine Learning Applications
Mahdi Nazemi, Massoud Pedram
Major advancements in building general-purpose and customized hardware have been one of the key enablers of versatility and pervasiveness of machine learning models such as deep ne…
VIBNN: Hardware Acceleration of Bayesian Neural Networks
Ruizhe Cai, Ao Ren, Ning Liu +5
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probabi…