7 citations · 7 across the 2 of their papers we have counts for
4 papers
Adaptive Sampling for Deep Learning via Efficient Nonparametric Proxies
Shabnam Daghaghi, Benjamin Coleman, Benito Geordie +1
Data sampling is an effective method to improve the training speed of neural networks, with recent results demonstrating that it can even break the neural scaling laws. These resul…
Accelerating SLIDE Deep Learning on Modern CPUs: Vectorization, Quantizations, Memory Optimizations, and More
Shabnam Daghaghi, Nicholas Meisburger, Mengnan Zhao +4
Deep learning implementations on CPUs (Central Processing Units) are gaining more traction. Enhanced AI capabilities on commodity x86 architectures are commercially appealing due t…
A Tale of Two Efficient and Informative Negative Sampling Distributions
Shabnam Daghaghi, Tharun Medini, Nicholas Meisburger +3
Softmax classifiers with a very large number of classes naturally occur in many applications such as natural language processing and information retrieval. The calculation of full…
SDM-Net: A Simple and Effective Model for Generalized Zero-Shot Learning
Shabnam Daghaghi, Tharun Medini, Anshumali Shrivastava
Zero-Shot Learning (ZSL) is a classification task where we do not have even a single training labeled example from a set of unseen classes. Instead, we only have prior information…