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Rotating Features for Object Discovery
In this paper, we present Rotating Features, a generalization of complex-valued features to higher dimensions, and a new evaluation procedure for extracting objects from distributed representations.
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer Bohan Zhou 1 Ke Li
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.
CE-NAS: An End-to-End Carbon-Efficient Neural Architecture Search Framework
This work presents a novel approach to neural architecture search (NAS) that aims to increase carbon efficiency for the model design process.
Learning From Biased Soft Labels Hua Yuan 1,2, Y u Shi
The proposed indicators adequately measure the effectiveness of the soft labels generated in this process.
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.
What Planning Problems Can A Relational Neural Network Solve?
Goal-conditioned policies are generally understood to be "feed-forward" circuits,
