Search Results for author: Wen-jie Lu

Found 5 papers, 1 papers with code

Falcon: Fast Spectral Inference on Encrypted Data

no code implementations NeurIPS 2020 Qian Lou, Wen-jie Lu, Cheng Hong, Lei Jiang

We observed that HENNs have to pay significant computing overhead on rotations, and each of rotations is $\sim 10\times$ more expensive than homomorphic multiplications between ciphertext and plaintext.

Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection

no code implementations29 Jul 2020 Tianming Wang, Wen-jie Lu, Huan Yu, Dikai Liu

In this paper, we propose a transfer learning framework that adapts a control policy for excessive disturbance rejection of an underwater robot under dynamics model mismatch.

Transfer Reinforcement Learning

Privacy-preserving collaborative machine learning on genomic data using TensorFlow

1 code implementation11 Feb 2020 Cheng Hong, Zhicong Huang, Wen-jie Lu, Hunter Qu, Li Ma, Morten Dahl, Jason Mancuso

Machine learning (ML) methods have been widely used in genomic studies.

Cryptography and Security

A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances

no code implementations1 Nov 2019 Wen-jie Lu, Dikai Liu

This paper proposes an Attention-based Abstraction (A${}^2$) approach to extract a finite-state automaton, referred to as a Key Moore Machine Network (KMMN), to capture the switching mechanisms exhibited by the DOB-net in dealing with multiple such POMDPs.

DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances

no code implementations10 Jul 2019 Tianming Wang, Wen-jie Lu, Zheng Yan, Dikai Liu

This paper presents an observer-integrated Reinforcement Learning (RL) approach, called Disturbance OBserver Network (DOB-Net), for robots operating in environments where disturbances are unknown and time-varying, and may frequently exceed robot control capabilities.

reinforcement Learning

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