7 citations · 20 across the 6 of their papers we have counts for
6 papers
Fast Adversarial Training with Smooth Convergence
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Fast adversarial training (FAT) is beneficial for improving the adversarial robustness of neural networks. However, previous FAT work has encountered a significant issue known as c…
Temporal Knowledge Graph Reasoning Triggered by Memories
Mengnan Zhao, Lihe Zhang, Yuqiu Kong +1
Inferring missing facts in temporal knowledge graphs is a critical task and has been widely explored. Extrapolation in temporal reasoning tasks is more challenging and gradually at…
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…
What is the Largest Sparsity Pattern that Can Be Recovered by 1-Norm Minimization?
Mustafa D. Kaba, Mengnan Zhao, Rene Vidal +2
Much of the existing literature in sparse recovery is concerned with the following question: given a sparsity pattern and a corresponding regularizer, derive conditions on the dict…
Sparse Recovery over Graph Incidence Matrices
Mengnan Zhao, M. Devrim Kaba, René Vidal +2
Classical results in sparse recovery guarantee the exact reconstruction of -sparse signals under assumptions on the dictionary that are either too strong or NP-hard to check. Mo…