526互联

unsupervised ego-motion learning motion

Heuristic-Guided Reinforcement Learning

**发表时间：**2021 (NeurIPS 2021) **文章要点：**这篇文章提出了一个Heuristic-Guided Reinforcement Learning (HuRL)的框架，用domain knowledge或者offline data构建heuristic，将问题变成一个sho ......
Heuristic-Guided Reinforcement Heuristic Learning Guided

Medicine River ————-Learning journals 9

Dear dairy. 2020 6 May Hey, Harlan, long time no see. How have you been lately? I've been quite busy lately. I hope you don't blame me for not coming ......
Medicine Learning journals River

LLL(Life Long Learning)&灾难性遗忘(Catastrophic Forgetting)

LLL(Life Long Learning)&灾难性遗忘(Catastrophic Forgetting) https://www.youtube.com/watch?v=Y9Jay_vxOsM Life Long Learning 通常机器学习中，单个模型只解决单个或少数几个任务。对于新的任务， ......
灾难性 Catastrophic Forgetting 灾难 Learning

Error:All flavors must now belong to a named flavor dimension. Learn more at

{ https://blog.csdn.net/qq_15807167/article/details/79528063 } 这是plugin 3.0.0之后有一种自动匹配消耗库的机制，便于debug variant 自动消耗一个库，然后就是必须要所有的flavor 都属于同一个维 defaultC ......
dimension flavors belong flavor Error

Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ NeurIPS 2020 ......
Reinforcement Perturbations Observations Adversarial Learning

李宏毅meta learning笔记

学习如何学习 其实就是学习模型本身，模型的超参数 定义一个function，输入是一堆训练任务，输出是一个模型，这个和传统的机器学习没有本质不同 所以也是分成三步， 定义学什么，和相应的学习模型，meta learning本身也是有meta的。。。。。。 定义loss函数 用优化算法求解，但是这个L ......
learning 笔记 meta

Learning A Single Network for Scale-Arbitrary Super-Resolution

Learning A Single Network for Scale-Arbitrary Super-Resolution abstract 现有的single image SR网络是为具有特定整数比例因子（例如，×2/3/4）的图像开发的，无法处理非整数和非对称 SR。 在本文中，作者建议从特定 ......
Super-Resolution Scale-Arbitrary Resolution Arbitrary Learning

Teachable Reinforcement Learning via Advice Distillation

**发表时间：**2021 (NeurIPS 2021) **文章要点：**这篇文章提出了一种学习policy的监督范式，大概思路就是先结构化advice，然后先学习解释advice，再从advice中学policy。这个advice来自于外部的teacher，相当于一种human-in-the-l ......
Reinforcement Distillation Teachable Learning Advice

迁移学习（VMT）《Virtual Mixup Training for Unsupervised Domain Adaptation》

论文信息 论文标题：Virtual Mixup Training for Unsupervised Domain Adaptation论文作者：Takeru Miyato, S. Maeda, Masanori Koyama, S. Ishii论文来源：2019 CVPR论文地址：download  ......
Unsupervised Adaptation Training Virtual Domain

论文阅读-sparse gpu kernels for deep learning

论文地址：https://ieeexplore.ieee.org/document/9355309 源码地址：https://github.com/google-research/sputnik 背景 深度神经网络由大量的矩阵乘法运算和卷积运算组成，这些运算中使用的矩阵可以转化成稀疏矩阵，同时不损失 ......
learning kernels sparse 论文 deep

Deep Dynamics Models for Learning Dexterous Manipulation

**发表时间：**2019 (CoRL 2019) **文章要点：**文章提出了一个online planning with deep dynamics models (PDDM)的算法来学习Dexterous multi-fingered hands，大概意思就是学习拟人的灵活的手指操控技巧。大概 ......
Manipulation Dexterous Dynamics Learning Models

2、题目：The Informed Design Teaching and Learning Matrix

期刊信息 （1）作者：Crismond, David P. （2）期刊：Journal of Engineering Education, 2012, 101(4): 738–797 （3）DOI：10.1002/j.2168-9830.2012.tb01127.x （4）ISSN：10694730 ......
Informed Teaching Learning 题目 Design

论文阅读笔记《Residual Physics Learning and System Identification for Sim to real Transfer of Policies on Buoyancy Assisted Legged Robots》

Residual Physics Learning and System Identification for Sim to real Transfer of Policies on Buoyancy Assisted Legged Robots 发表于2023年。论文较新，未找到发表期刊。 基于浮 ......
Identification Residual Learning Buoyancy Assisted

论文阅读笔记《Stochastic Grounded Action Transformation for Robot Learning in Simulation》

Stochastic Grounded Action Transformation for Robot Learning in Simulation 发表于IROS 2020(CCF C) 模拟中机器人学习的随机接地动作转换 Desai S, Karnan H, Hanna J P, et al. ......
Transformation Stochastic Simulation Grounded Learning

论文阅读笔记《Grounded Action Transformation for Robot Learning in Simulation》

Grounded Action Transformation for Robot Learning in Simulation 发表于AAAI 2017 仿真机器人学习中的接地动作变换 Hanna J, Stone P. Grounded action transformation for robo ......
Transformation Simulation Grounded Learning 笔记

Representation Learning for Attributed Multiplex Heterogeneous Network

Cen Y., Zou X., Zhang J., Yang H., Zhou J. and Tang J. Representation learning for attributed multiplex heterogeneous network. KDD, 2019. 概 本文在 Attrib ......
Representation Heterogeneous Attributed Multiplex Learning

2022AAAI_Semantically Contrastive Learning for Low-light Image Enhancement(SCL_LLE)

1. motivation 利用语义对比学习 2. network (1) 输入的是低光图像首先经过图像增强的网络（Zero-DCE), 再将它传入语义分割网络中 （2）语义分割网络用的是DeepLabv3+ ......
AAAI_Semantically Semantically Contrastive Enhancement Low-light

MEMORY REPLAY WITH DATA COMPRESSION FOR CONTINUAL LEARNING--阅读笔记

MEMORY REPLAY WITH DATA COMPRESSION FOR CONTINUAL LEARNING--阅读笔记 摘要： 在这项工作中，我们提出了使用数据压缩（MRDC）的内存重放，以降低旧的训练样本的存储成本，从而增加它们可以存储在内存缓冲区中的数量。观察到压缩数据的质量和数量之间 ......
COMPRESSION CONTINUAL LEARNING 笔记 MEMORY

Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Images

Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Images abstract 为了解决STF中的生成heterogeneous images问题: 为此，本 ......
Spatio-Temporal-Spectral Deep-Learning-Based Heterogeneous Integrated Learning

迁移学习（MEnsA）《MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds》

论文信息 论文标题：MEnsA: Mix-up Ensemble Average for Unsupervised Multi Target Domain Adaptation on 3D Point Clouds论文作者：Ashish Sinha, Jonghyun Choi论文来源：2023 C ......
MEnsA Unsupervised Adaptation Ensemble Average

Medicine River-------------Learning Journals 8

htttp://www.enotes.com ......
Medicine Learning Journals River

Adversarial Robust Deep Reinforcement Learning Requires Redefining Robustness

郑重声明：原文参见标题，如有侵权，请联系作者，将会撤销发布！ ......
Reinforcement Adversarial Redefining Robustness Learning

Learning Off-Policy with Online Planning

**发表时间：**2021（CoRL 2021） **文章要点：**这篇文章提出Off-Policy with Online Planning (LOOP)算法，将H-step lookahead with a learned model和terminal value function learne ......
Off-Policy Learning Planning Policy Online

论文解读（VAT）《Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning》

论文信息 论文标题：Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning论文作者：Takeru Miyato, S. Maeda, Masanori Koya ......
Supervised Semi-Supervised Regularization Adversarial Training

论文解读（PGD）《Towards deep learning models resistant to adversarial attacks》

论文信息 论文标题：Towards deep learning models resistant to adversarial attacks论文作者：Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, Ad ......
adversarial resistant learning Towards attacks

猛读论文13 |【CVPR 2022 UDA】Unleashing Potential of Unsupervised Pre-Training with Intra-Identity Regularization for Person Re-Identification

动机 解决（1）对比学习管道中的增强通常会扭曲人物图像中的判别线索（2）细粒度的局部特征人物图像尚未得到充分探索。 思路 方法 ......
Re-Identification Intra-Identity Identification Regularization Pre-Training

基于RL(Q-Learning)的迷宫寻路算法

强化学习是一种机器学习方法，旨在通过智能体在与环境交互的过程中不断优化其行动策略来实现特定目标。与其他机器学习方法不同，强化学习涉及到智能体对环境的观测、选择行动并接收奖励或惩罚。因此，强化学习适用于那些需要自主决策的复杂问题，比如游戏、机器人控制、自动驾驶等。强化学习可以分为基于价值的方法和基于策 ......
迷宫 算法 Q-Learning Learning RL

迁移学习（PAT）《Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation》

论文信息 论文标题：Pairwise Adversarial Training for Unsupervised Class-imbalanced Domain Adaptation论文作者：Weili Shi, Ronghang Zhu, Sheng Li论文来源：KDD 2022论文地址：dow ......
Class-imbalanced Unsupervised Adversarial Adaptation imbalanced

1、题目：Engineering Design Thinking, Teaching, and Learning

期刊信息 （1）作者：Dym，Clive L.，Agogino，Alice M.，Eris，Ozgur，Frey，Daniel D.，Leifer，Larry J. （2）期刊：Journal of Engineering Education：94-1-103-120，01/2005 （3）DOI： ......
Engineering Thinking Teaching Learning 题目

M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalities

摘要 提出SimCLR，用于视觉表征的对比学习，简化了最近提出的对比自监督学习算法，为了理解是什么使对比预测任务能够学习有用的表示，系统研究了提出框架的主要组成部分，发现： （1）数据增强的组成在定义有效的预测任务中起着关键的作用 （2）在表示和对比损失之间引入一个可学习的非线性变换，大大提高了已学 ......
Representation Segmentation Multimodal Modalities Learning
共510篇  :14/17页 首页上一页14下一页尾页