341 citations · 677 across the 17 of their papers we have counts for
17 papers
Mask The Bias: Improving Domain-Adaptive Generalization of CTC-based ASR with Internal Language Model Estimation
Nilaksh Das, Monica Sunkara, Sravan Bodapati +4
End-to-end ASR models trained on large amount of data tend to be implicitly biased towards language semantics of the training data. Internal language model estimation (ILME) has be…
NeuroMapper: In-browser Visualizer for Neural Network Training
Zhiyan Zhou, Kevin Li, Haekyu Park +4
We present our ongoing work NeuroMapper, an in-browser visualization tool that helps machine learning (ML) developers interpret the evolution of a model during training, providing…
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning
Nilaksh Das, Duen Horng Chau
As automatic speech recognition (ASR) systems are now being widely deployed in the wild, the increasing threat of adversarial attacks raises serious questions about the security an…
SkeleVision: Towards Adversarial Resiliency of Person Tracking with Multi-Task Learning
Nilaksh Das, Sheng-Yun Peng, Duen Horng Chau
Person tracking using computer vision techniques has wide ranging applications such as autonomous driving, home security and sports analytics. However, the growing threat of advers…
Concept Evolution in Deep Learning Training: A Unified Interpretation Framework and Discoveries
Haekyu Park, Seongmin Lee, Benjamin Hoover +7
We present ConceptEvo, a unified interpretation framework for deep neural networks (DNNs) that reveals the inception and evolution of learned concepts during training. Our work add…
NeuroCartography: Scalable Automatic Visual Summarization of Concepts in Deep Neural Networks
Haekyu Park, Nilaksh Das, Rahul Duggal +4
Existing research on making sense of deep neural networks often focuses on neuron-level interpretation, which may not adequately capture the bigger picture of how concepts are coll…