130 citations · 525 across the 45 of their papers we have counts for
8 papers · 1 filter
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
Andrew Jesson, Chris Lu, Gunshi Gupta +4
This paper proposes a step toward approximate Bayesian inference in on-policy actor-critic deep reinforcement learning. It is implemented through three changes to the Asynchronous…
Arbitrary Order Meta-Learning with Simple Population-Based Evolution
Chris Lu, Sebastian Towers, Jakob Foerster
Meta-learning, the notion of learning to learn, enables learning systems to quickly and flexibly solve new tasks. This usually involves defining a set of outer-loop meta-parameters…
Structured State Space Models for In-Context Reinforcement Learning
Chris Lu, Yannick Schroecker, Albert Gu +4
Structured state space sequence (S4) models have recently achieved state-of-the-art performance on long-range sequence modeling tasks. These models also have fast inference speeds…
Adversarial Cheap Talk
Chris Lu, Timon Willi, Alistair Letcher +1
Adversarial attacks in reinforcement learning (RL) often assume highly-privileged access to the victim's parameters, environment, or data. Instead, this paper proposes a novel adve…
SenseFi: A Library and Benchmark on Deep-Learning-Empowered WiFi Human Sensing
Jianfei Yang, Xinyan Chen, Dazhuo Wang +4
WiFi sensing has been evolving rapidly in recent years. Empowered by propagation models and deep learning methods, many challenging applications are realized such as WiFi-based hum…
Deep Inertial Odometry with Accurate IMU Preintegration
Rooholla Khorrambakht, Chris Xiaoxuan Lu, Hamed Damirchi +2
Inertial Measurement Units (IMUs) are interceptive modalities that provide ego-motion measurements independent of the environmental factors. They are widely adopted in various auto…
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
Changhao Chen, Chris Xiaoxuan Lu, Bing Wang +2
Dynamical models estimate and predict the temporal evolution of physical systems. State Space Models (SSMs) in particular represent the system dynamics with many desirable properti…
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Changhao Chen, Yishu Miao, Chris Xiaoxuan Lu +3
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent. Howe…