本篇文章基于上述两篇，故论文编号沿用上两篇的编号
[41] Premise Selection for Theorem Proving by Deep Graph Embedding
Mingzhe Wang, Yihe Tang, Jian Wang, Jia Deng
https://papers.nips.cc/paper/6871-premise-selection-for-theorem-proving-by-deep-graph-embedding.pdf
本文将深层图嵌入模型用于定理证明中的前提选择。
[42] A Bayesian Data Augmentation Approach for Learning Deep Models
Toan Tran, Trung Pham, Gustavo Carneiro,
Lyle Palmer, Ian Reid
https://papers.nips.cc/paper/6872-a-bayesian-data-augmentation-approach-for-learning-deep-models.pdf
一、对深度模型（GAN的扩展）提出一种基于贝叶斯方法的数据扩展方法。
整体流程如下
各种网络结构比较如下
相关代码地址
https: //github.com/toantm/keras-bda
https://github.com/lukedeo/keras-acgan
[43] Convolutional Gaussian Processes
Mark van der Wilk, Carl Edward
Rasmussen, James Hensman
https://papers.nips.cc/paper/6877-convolutional-gaussian-processes.pdf
1.1 这篇文章将卷积融入到高斯过程中。
代码地址
https://github.com/markvdw/convgp
[44] Deep Recurrent Neural Network-Based Identification of Precursor
microRNAs
Seunghyun Park, Seonwoo Min, Hyun-Soo Choi, Sungroh Yoon
https://papers.nips.cc/paper/6882-deep-recurrent-neural-network-based-identification-of-precursor-micrornas.pdf
这篇文章将深层RNN用于前体微RNA识别。
网络结构如下
各方法效果比较如下
代码地址
https://github.com/eleventh83/deepMiRGene
[45] Deep Voice 2: Multi-Speaker Neural Text-to-Speech
Andrew Gibiansky, Sercan Arik, Gregory Diamos, John Miller, Kainan Peng, Wei Ping, Jonathan Raiman, Yanqi Zhou
https://papers.nips.cc/paper/6889-deep-voice-2-multi-speaker-neural-text-to-speech.pdf
这篇论文基于Deep Voice 1提出Deep Voice2，主要处理的问题是将多个说活人的文本转成语音。
网络结构如下
各模型效果比较如下
[46] Deep Lattice Networks and Partial Monotonic Functions
Seungil You, David Ding, Kevin Canini,
Jan Pfeifer, Maya Gupta
https://papers.nips.cc/paper/6891-deep-lattice-networks-and-partial-monotonic-functions.pdf
这篇文章主要关于深层格子网络和局部单调函数
网络结构示例如下
各模型结果比较如下
[47] Continual Learning with Deep
Generative Replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, Jiwon Kim
https://papers.nips.cc/paper/6892-continual-learning-with-deep-generative-replay.pdf
模型示例如下
[48] Hierarchical Attentive Recurrent
Tracking
Adam Kosiorek, Alex Bewley,
Ingmar Posner
https://papers.nips.cc/paper/6898-hierarchical-attentive-recurrent-tracking.pdf
这篇论文主要关于分层注意力递归模型，用于视频中的单个物体追踪。
网络结构如下
各算法效果比较
代码地址
https://github.com/akosiorek/hart
[49] Shallow Updates for Deep
Reinforcement Learning
Nir Levine, Tom Zahavy, Daniel J. Mankowitz, Aviv Tamar, Shie Mannor
https://papers.nips.cc/paper/6906-shallow-updates-for-deep-reinforcement-learning.pdf
这篇论文将线性最小二乘跟深度强化学习结合。
算法流程如下
代码地址
https://github.com/Shallow-Updates-for-Deep-RL
[50] Net-Trim: Convex Pruning of Deep Neural Networks with Performance
Guarantee
Alireza Aghasi, Afshin Abdi, Nam Nguyen,
Justin Romberg
https://papers.nips.cc/paper/6910-net-trim-convex-pruning-of-deep-neural-networks-with-performance-guarantee.pdf
这篇文章主要讨论如何通过凸优化对深度神经网络进行剪枝，即减少连接权重，同时保证模型效果。
算法流程如下
代码地址
https://github.com/DNNToolBox/Net-Trim-v1
[51] Wasserstein Learning of Deep
Generative Point Process Models
SHUAI XIAO, Mehrdad Farajtabar,
Xiaojing Ye, Junchi Yan, Le Song,
Hongyuan Zha
https://papers.nips.cc/paper/6917-wasserstein-learning-of-deep-generative-point-process-models.pdf
这篇论文将点过程跟深层生成模型融合。
算法步骤如下
代码地址
https://github.com/xiaoshuai09/Wasserstein-Learning-For-Point-Process
[52] Bayesian Compression for
Deep Learning
Christos Louizos, Karen Ullrich,
Max Welling
https://papers.nips.cc/paper/6921-bayesian-compression-for-deep-learning.pdf
这篇论文将贝叶斯压缩用于深度学习中。
各方法对比如下
[53] VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Akash Srivastava, Lazar Valkoz, Chris Russell, Michael U. Gutmann, Charles Sutton
https://papers.nips.cc/paper/6923-veegan-reducing-mode-collapse-in-gans-using-implicit-variational-learning.pdf
这篇论文讨论利用隐含变分学习来减轻GAN中的模式丢失问题。
训练流程示例如下
各算法效果对比如下
[54] Deep Sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Ruslan R. Salakhutdinov, Alexander J. Smola
https://papers.nips.cc/paper/6931-deep-sets.pdf
这篇论文基于集合提出一种新的深度学习算法。
各算法效果对比如下
[55] Spherical convolutions and their application in molecular modelling
Wouter Boomsma, Jes Frellsen
https://papers.nips.cc/paper/6935-spherical-convolutions-and-their-application-in-molecular-modelling.pdf
这篇论文研究了如何在球面上做卷积
球体卷积示例如下
各算法效果对比如下
代码地址
https://github.com/deepfold
[56] Concrete Dropout
Yarin Gal, Jiri Hron, Alex Kendall
https://papers.nips.cc/paper/6949-concrete-dropout.pdf
这篇论文提出了一种新的dropout策略。
各策略效果对比如下
代码地址
https://github.com/yaringal/ConcreteDropout
[57] Bayesian GAN
Yunus Saatci, Andrew G. Wilson
https://papers.nips.cc/paper/6953-bayesian-gan.pdf
这篇文章基于随机梯度哈密尔顿蒙特卡洛方法提出了贝叶斯GAN。
算法流程如下
各算法效果对比如下
代码地址
https://github.com/andrewgordonwilson/bayesgan
[58] Sparse convolutional coding for neuronal assembly detection
Sven Peter, Elke Kirschbaum,
Martin Both, Lee Campbell,
Brandon Harvey, Conor Heins,
Daniel Durstewitz, Ferran Diego, Fred A. Hamprecht
https://papers.nips.cc/paper/6958-sparse-convolutional-coding-for-neuronal-assembly-detection.pdf
这篇文章提出了一种稀疏卷积编码网络。
卷积编码示例如下
各算法效果对比如下
代码地址
https://github.com/sccfnad/Sparse-convolutional-coding-for-neuronal-assembly-detection
[59] Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
Federico Monti, Michael Bronstein, Xavier Bresson
https://papers.nips.cc/paper/6960-geometric-matrix-completion-with-recurrent-multi-graph-neural-networks.pdf
这篇论文主要讨论图上的几何深度学习，结合了多图卷积神经网络和循环神经网络。
网络结构如下
算法流程如下
各算法效果对比如下
代码地址
https://github.com/fmonti/mgcnn
[60] Interpolated Policy Gradient: Merging On-Policy and Off-Policy
Gradient Estimation for Deep
Reinforcement Learning
Shixiang Gu, Tim Lillicrap, Richard E. Turner, Zoubin Ghahramani, Bernhard Schölkopf, Sergey Levine
https://papers.nips.cc/paper/6974-interpolated-policy-gradient-merging-on-policy-and-off-policy-gradient-estimation-for-deep-reinforcement-learning.pdf
这篇论文将on-policy和off-policy结合用于深度强化学习。
算法流程如下
</div>
<script nonce="1931263480" type="text/javascript">
var first_sceen__time = (+new Date());
if ("" == 1 && document.getElementById('js_content')) {
document.getElementById('js_content').addEventListener("selectstart",function(e){ e.preventDefault(); });
}
(function(){
if (navigator.userAgent.indexOf("WindowsWechat") != -1){
var link = document.createElement('link');
var head = document.getElementsByTagName('head')[0];
link.rel = 'stylesheet';
link.type = 'text/css';
link.href = "//res.wx.qq.com/mmbizwap/zh_CN/htmledition/style/page/appmsg_new/winwx3ec991.css";
head.appendChild(link);
}
})();
</script>
<div class="ct_mpda_wrp" id="js_sponsor_ad_area" style="display:none;"></div>
<div class="reward_area tc reward_area_primary" id="js_reward_area" style="display:none;">
<div class="reward-avatar" style="display: none;" id="js_reward_avatar">
<img src="" alt="" id="js_reward_author_head">
</div>
<div class="reward-author" style="display: none;" id="js_reward_author">FrankLearningMachine</div>
<p>
<a class="reward_button" id="js_reward_link" href="##"><span id="js_reward_link_text">赞赏</span></a>
</p>
<div id="js_reward_inner" class="reward_area_inner" style="display: none; width: 792px;">
<p class="weui-loadmore weui-loadmore_line reward_user_tips" id="js_reward_total_parent">
<span class="weui-loadmore__tips"><a href="##" id="js_reward_total"></a> 人<span id="js_reward_total_text">赞赏</span></span>
</p>
<div id="js_reward_list" class="reward_user_list"></div>
</div>
</div>
<div class="reward_qrcode_area reward_area tc" id="js_reward_qrcode" style="display:none;">
<p class="tips_global">长按二维码向我转账</p>
<p class="reward_tips"></p>
<span class="reward_qrcode_img_wrp"><img class="reward_qrcode_img" id="js_reward_qrcode_img"></span>
<p class="tips_global">受苹果公司新规定影响，微信 iOS 版的赞赏功能被关闭，可通过二维码转账支持公众号。</p>
</div>