most citedThe Local Elasticity of Neural Networks

9 citations · 12 across the 4 of their papers we have counts for

collaborators

6 papers

cs.LG20202 cited

Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity

Shuxiao Chen, Hangfeng He, Weijie J. Su

As a popular approach to modeling the dynamics of training overparametrized neural networks (NNs), the neural tangent kernels (NTK) are known to fall behind real-world NNs in gener…

cs.LG2020

Towards Understanding the Dynamics of the First-Order Adversaries

Zhun Deng, Hangfeng He, Jiaoyang Huang +1

An acknowledged weakness of neural networks is their vulnerability to adversarial perturbations to the inputs. To improve the robustness of these models, one of the most popular de…

cs.CL20201 cited

Understanding Spatial Relations through Multiple Modalities

Soham Dan, Hangfeng He, Dan Roth

Recognizing spatial relations and reasoning about them is essential in multiple applications including navigation, direction giving and human-computer interaction in general. Spati…

cs.LG20199 cited

The Local Elasticity of Neural Networks

Hangfeng He, Weijie J. Su

This paper presents a phenomenon in neural networks that we refer to as \textit{local elasticity}. Roughly speaking, a classifier is said to be locally elastic if its prediction at…

cs.CL2019

QuASE: Question-Answer Driven Sentence Encoding

Hangfeng He, Qiang Ning, Dan Roth

Question-answering (QA) data often encodes essential information in many facets. This paper studies a natural question: Can we get supervision from QA data for other tasks (typical…

cs.LG2019

Partial Or Complete, That's The Question

Qiang Ning, Hangfeng He, Chuchu Fan +1

For many structured learning tasks, the data annotation process is complex and costly. Existing annotation schemes usually aim at acquiring completely annotated structures, under t…