Asia
Thermostat-assisted continuously-tempered Hamiltonian Monte Carlo for Bayesian learning
Rui Luo, Jianhong Wang, Yaodong Yang, Jun WANG, Zhanxing Zhu
Neural Information Processing Systems http://nips.cc/
Multi-Agent First Order Constrained Optimization in Policy Space
In the realm of multi-agent reinforcement learning (MARL), achieving high performance is crucial for a successful multi-agent system.
NeuralFuse: Learning to Recover the Accuracy of Access-Limited Neural Network Inference in Low-Voltage Regimes Hao-Lun Sun
Energy-efficient computing is of primary importance to the effective deployment of deep neural networks (DNNs), particularly in edge devices and in on-chip AI systems. Increasing DNN computation's energy efficiency and lowering its carbon footprint require iterative efforts from both chip designers and algorithm developers.
Semi-Implicit Graph Variational Auto-Encoders
Arman Hasanzadeh, Ehsan Hajiramezanali, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, Xiaoning Qian
Neural Information Processing Systems http://nips.cc/
EgoChoir: Capturing 3D H uman-O bject Interaction Regions from Egocentric Views Y uhang Y ang
For the egocentric HOI, in addition to perceiving semantics e.g., "what" interaction is occurring, capturing "where" the interaction specifically manifests in 3D space is
SEGA: Variance Reduction via Gradient Sketching
Filip Hanzely, Konstantin Mishchenko, Peter Richtarik
Neural Information Processing Systems http://nips.cc/
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Ximei Wang, Ying Jin, Mingsheng Long, Jianmin Wang, Michael I. Jordan
Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning.
