Search Results for author: Yun Long

Found 9 papers, 2 papers with code

Data-Free Neural Architecture Search via Recursive Label Calibration

no code implementations3 Dec 2021 Zechun Liu, Zhiqiang Shen, Yun Long, Eric Xing, Kwang-Ting Cheng, Chas Leichner

We identify that the NAS task requires the synthesized data (we target at image domain here) with enough semantics, diversity, and a minimal domain gap from the natural images.

Neural Architecture Search

MagNet: Discovering Multi-agent Interaction Dynamics using Neural Network

1 code implementation24 Jan 2020 Priyabrata Saha, Arslan Ali, Burhan A. Mudassar, Yun Long, Saibal Mukhopadhyay

We present the MagNet, a neural network-based multi-agent interaction model to discover the governing dynamics and predict evolution of a complex multi-agent system from observations.

SAFE-DNN: A Deep Neural Network with Spike Assisted Feature Extraction for Noise Robust Inference

no code implementations25 Sep 2019 Xueyuan She, Priyabrata Saha, Daehyun Kim, Yun Long, Saibal Mukhopadhyay

We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs.


Improving Robustness of ReRAM-based Spiking Neural Network Accelerator with Stochastic Spike-timing-dependent-plasticity

no code implementations11 Sep 2019 Xueyuan She, Yun Long, Saibal Mukhopadhyay

In addition, we show that the new algorithm can be used for designing a robust ReRAM based SNN accelerator that has strong resilience to device variation.

ScieNet: Deep Learning with Spike-assisted Contextual Information Extraction

no code implementations11 Sep 2019 Xueyuan She, Yun Long, Daehyun Kim, Saibal Mukhopadhyay

ScieNet integrates unsupervised learning using spiking neural network (SNN) for unsupervised contextual informationextraction with a back-end DNN trained for classification.

Autonomous Vehicles Classification +2

HybridNet: Integrating Model-based and Data-driven Learning to Predict Evolution of Dynamical Systems

no code implementations19 Jun 2018 Yun Long, Xueyuan She, Saibal Mukhopadhyay

In this paper, we present HybridNet, a framework that integrates data-driven deep learning and model-driven computation to reliably predict spatiotemporal evolution of a dynamical systems even with in-exact knowledge of their parameters.

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