Search Results for author:
Found 6 papers, 2 papers with code
DOB-Net: Actively Rejecting Unknown Excessive Time-Varying Disturbances
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.
A2: Extracting Cyclic Switchings from DOB-nets for Rejecting Excessive Disturbances
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.
Privacy-preserving collaborative machine learning on genomic data using TensorFlow
Machine learning (ML) methods have been widely used in genomic studies.
Cryptography and Security
Modular Transfer Learning with Transition Mismatch Compensation for Excessive Disturbance Rejection
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.
Falcon: Fast Spectral Inference on Encrypted Data
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.
MPCViT: Searching for Accurate and Efficient MPC-Friendly Vision Transformer with Heterogeneous Attention
Secure multi-party computation (MPC) enables computation directly on encrypted data and protects both data and model privacy in deep learning inference.
