8 citations · 19 across the 5 of their papers we have counts for
6 papers
Exploring Exploration: Comparing Children with RL Agents in Unified Environments
Eliza Kosoy, Jasmine Collins, David M. Chan +6
Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently and that this exploration allows them to learn. In turn, this ear…
A Dataset and Benchmarks for Multimedia Social Analysis
Bofan Xue, David Chan, John Canny
We present a new publicly available dataset with the goal of advancing multi-modality learning by offering vision and language data within the same context. This is achieved by obt…
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations
Daniel Seita, David Chan, Roshan Rao +3
Learning from demonstrations is a popular tool for accelerating and reducing the exploration requirements of reinforcement learning. When providing expert demonstrations to human s…
Leveraging Class Similarity to Improve Deep Neural Network Robustness
Pooran Singh Negi, David chan, Mohammad Mahoor
Traditionally artificial neural networks (ANNs) are trained by minimizing the cross-entropy between a provided groundtruth delta distribution (encoded as one-hot vector) and the AN…
Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics
Biye Jiang, David M. Chan, Tianhao Zhang +1
The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are wi…
Rapid Randomized Restarts for Multi-Agent Path Finding Solvers
Liron Cohen, Glenn Wagner, T. K. Satish Kumar +2
Multi-Agent Path Finding (MAPF) is an NP-hard problem well studied in artificial intelligence and robotics. It has many real-world applications for which existing MAPF solvers use…