Conventional neural networks with adaptive activation functions or ELU all predicted very accurately within 15 min, the training zone, but as time reached beyond, they all incorrectly predicted the behavior. Proven by the increased adaptation of PINN, the method has numerous advantages over ...
The data-science revolution is poised to transform the way photonic systems are simulated and designed. Photonic systems are, in many ways, an ideal substrate for machine learning: the objective of much of computational electromagnetics is the capture of
[ICLR 2021] On the Bottleneck of Graph Neural Networks and its Practical Implications openreview.net/forum? github.com/tech-srl/bot) [ICLR 2021] Adaptive Universal Generalized PageRank Graph Neural Network openreview.net/forum? github.com/jianhao2016/ [ICLR 2021] Simple Spectral Graph Convolution ...
强化学习旨在学习如何做,即如何根据情况采取动作,从而实现数值奖励信号最大化。学习者不会接到动作 指令,而是必须自行尝试去发现回报最高的动作方案。 —Sutton and Barto, 强化学习:简介 1.2 为什么要强化学习 传统控制问题设计控制器必须满足多项要求,包括多个状态值和参考值的反馈,各个环路之间的交互,设计和调优困难...
Graph-based multi-agent reinforcement learning for large-scale UAVs swarm system control 2024, Aerospace Science and Technology Show abstract Neural network based adaptive finite-time distributed estimation for an uncertain leader 2024, Information Sciences Citation Excerpt : For example, NNs have been wi...
在上述优化下,FP8 模式下单个 GPU 每个 prompt 的生成吞吐量最高可达 32 token/s,相比 BF16 提升 1.8 倍。其中,FP8 本身带来 1.4 倍加速,另外 0.4 倍收益源自内存占用减少,使研究者能够启用 vLLM 的 cudagraph 特性,进一步提升系统性能。 用于偏好优化的强化学习 ...
Neural networks, with their outstanding ability to derive meaning from complex or imperfect data, can be applied for extracting patterns and detecting trends that are too difficult to notice by humans or computer techniques. Other advantages of ANNs are adaptive learning, self-organization, real time...
MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning (SIGKDD, 2024) TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning (IPDPS, 2024) [paper][code] Mayfly: a Neural Data Structure for Graph Stream Summarization (ICLR, 2024, Spotlight...
Discourse-Aware Neural Extractive Text Summarization, ACL'20,Jiacheng Xu, Zhe Gan, Yu Cheng, Jingjing Liu Learning Robust Node Representations on Graphs,Xu Chen, Ya Zhang, Ivor Tsang, Yuangang Pan Adaptive Graph Diffusion Networks with Hop-wise Attention,Chuxiong Sun, Guoshi Wu ...
代表性模型有 :Neural Network for graphs (NN4G) 和 Contextual Graph Markov Model (CGMM). 3. Building Blocks 「块」(blocks)是局部图学习模型的主要组成部分。这里从构造块的角度来简单介绍基于局部和迭代处理的 DGN 是如何利用可用信息的。 3.1. Neighborhood Aggregation (邻域聚合) ① 构造基本的领域聚合...