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Found 12 papers, 4 papers with code
On Attack Detection and Identification for the Cyber-Physical System using Lifted System Model
Motivated by the safety and security issues related to cyber-physical systems with potentially multi-rate, delayed, and nonuniformly sampled measurements, we investigate the attack detection and identification using the lifted system model in this paper.
DistPro: Searching A Fast Knowledge Distillation Process via Meta Optimization
Specifically, given a pair of student and teacher networks, DistPro first sets up a rich set of KD connection from the transmitting layers of the teacher to the receiving layers of the student, and in the meanwhile, various transforms are also proposed for comparing feature maps along its pathway for the distillation.
Cooperative Task and Motion Planning for Multi-Arm Assembly Systems
Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs.
SABLAS: Learning Safe Control for Black-box Dynamical Systems
Control certificates based on barrier functions have been a powerful tool to generate probably safe control policies for dynamical systems.
Learning Density Distribution of Reachable States for Autonomous Systems
State density distribution, in contrast to worst-case reachability, can be leveraged for safety-related problems to better quantify the likelihood of the risk for potentially hazardous situations.
Knowledge Transfer via Dense Cross-Layer Mutual-Distillation
Knowledge Distillation (KD) based methods adopt the one-way Knowledge Transfer (KT) scheme in which training a lower-capacity student network is guided by a pre-trained high-capacity teacher network.
Verification and Parameter Synthesis for Stochastic Systems using Optimistic Optimization
As a consequence, we obtain theoretical regret bounds on sample efficiency of our solution that depends on key problem parameters like smoothness, near-optimality dimension, and batch size.
Learning Two-View Correspondences and Geometry Using Order-Aware Network
First, to capture the local context of sparse correspondences, the network clusters unordered input correspondences by learning a soft assignment matrix.
Deeply-supervised Knowledge Synergy
Convolutional Neural Networks (CNNs) have become deeper and more complicated compared with the pioneering AlexNet.
SnapQuant: A Probabilistic and Nested Parameterization for Binary Networks
During the training phase, we generate binary weights on-the-fly since what we actually maintain is the policy network, and all the binary weights are used in a burn-after-reading style.
Learn-Memorize-Recall-Reduce A Robotic Cloud Computing Paradigm
The rise of robotic applications has led to the generation of a huge volume of unstructured data, whereas the current cloud infrastructure was designed to process limited amounts of structured data.
Enabling Embedded Inference Engine with ARM Compute Library: A Case Study
Our conclusion is that, on embedded devices, we most likely will use very simple deep learning models for inference, and with well-developed building blocks such as ACL, it may be better in both performance and development time to build the engine from scratch.
