69 citations · 300 across the 25 of their papers we have counts for
5 papers · 1 filter
Equivariance Discovery by Learned Parameter-Sharing
Raymond A. Yeh, Yuan-Ting Hu, Mark Hasegawa-Johnson +1
Designing equivariance as an inductive bias into deep-nets has been a prominent approach to build effective models, e.g., a convolutional neural network incorporates translation eq…
EH-DNAS: End-to-End Hardware-aware Differentiable Neural Architecture Search
Qian Jiang, Xiaofan Zhang, Deming Chen +2
In hardware-aware Differentiable Neural Architecture Search (DNAS), it is challenging to compute gradients of hardware metrics to perform architecture search. Existing works rely o…
High-Throughput Synchronous Deep RL
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Deep reinforcement learning (RL) is computationally demanding and requires processing of many data points. Synchronous methods enjoy training stability while having lower data thro…
Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning
Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing
Existing semi-supervised learning (SSL) algorithms use a single weight to balance the loss of labeled and unlabeled examples, i.e., all unlabeled examples are equally weighted. But…
PIC: Permutation Invariant Critic for Multi-Agent Deep Reinforcement Learning
Iou-Jen Liu, Raymond A. Yeh, Alexander G. Schwing
Sample efficiency and scalability to a large number of agents are two important goals for multi-agent reinforcement learning systems. Recent works got us closer to those goals, add…