July 29, 2019

3066 words 15 mins read

Paper Group AWR 108

Social Emotion Mining Techniques for Facebook Posts Reaction Prediction. Asymmetric Variational Autoencoders. When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach. Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?. Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. …


Title	Social Emotion Mining Techniques for Facebook Posts Reaction Prediction
Authors	Florian Krebs, Bruno Lubascher, Tobias Moers, Pieter Schaap, Gerasimos Spanakis
Abstract	As of February 2016 Facebook allows users to express their experienced emotions about a post by using five so-called `reactions’. This research paper proposes and evaluates alternative methods for predicting these reactions to user posts on public pages of firms/companies (like supermarket chains). For this purpose, we collected posts (and their reactions) from Facebook pages of large supermarket chains and constructed a dataset which is available for other researches. In order to predict the distribution of reactions of a new post, neural network architectures (convolutional and recurrent neural networks) were tested using pretrained word embeddings. Results of the neural networks were improved by introducing a bootstrapping approach for sentiment and emotion mining on the comments for each post. The final model (a combination of neural network and a baseline emotion miner) is able to predict the reaction distribution on Facebook posts with a mean squared error (or misclassification rate) of 0.135. \|
Tasks	Word Embeddings
Published	2017-12-08
URL	http://arxiv.org/abs/1712.03249v1
PDF	http://arxiv.org/pdf/1712.03249v1.pdf
PWC	https://paperswithcode.com/paper/social-emotion-mining-techniques-for-facebook
Repo	https://github.com/jerryspan/FacebookR
Framework	none

Asymmetric Variational Autoencoders


Title	Asymmetric Variational Autoencoders
Authors	Guoqing Zheng, Yiming Yang, Jaime Carbonell
Abstract	Variational inference for latent variable models is prevalent in various machine learning problems, typically solved by maximizing the Evidence Lower Bound (ELBO) of the true data likelihood with respect to a variational distribution. However, freely enriching the family of variational distribution is challenging since the ELBO requires variational likelihood evaluations of the latent variables. In this paper, we propose a novel framework to enrich the variational family by incorporating auxiliary variables to the variational family. The resulting inference network doesn’t require density evaluations for the auxiliary variables and thus complex implicit densities over the auxiliary variables can be constructed by neural networks. It can be shown that the actual variational posterior of the proposed approach is essentially modeling a rich probabilistic mixture of simple variational posterior indexed by auxiliary variables, thus a flexible inference model can be built. Empirical evaluations on several density estimation tasks demonstrates the effectiveness of the proposed method.
Tasks	Density Estimation, Latent Variable Models
Published	2017-11-20
URL	http://arxiv.org/abs/1711.08352v2
PDF	http://arxiv.org/pdf/1711.08352v2.pdf
PWC	https://paperswithcode.com/paper/asymmetric-variational-autoencoders
Repo	https://github.com/apsyx/mvae
Framework	pytorch

When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach


Title	When Image Denoising Meets High-Level Vision Tasks: A Deep Learning Approach
Authors	Ding Liu, Bihan Wen, Xianming Liu, Zhangyang Wang, Thomas S. Huang
Abstract	Conventionally, image denoising and high-level vision tasks are handled separately in computer vision. In this paper, we cope with the two jointly and explore the mutual influence between them. First we propose a convolutional neural network for image denoising which achieves the state-of-the-art performance. Second we propose a deep neural network solution that cascades two modules for image denoising and various high-level tasks, respectively, and use the joint loss for updating only the denoising network via back-propagation. We demonstrate that on one hand, the proposed denoiser has the generality to overcome the performance degradation of different high-level vision tasks. On the other hand, with the guidance of high-level vision information, the denoising network can generate more visually appealing results. To the best of our knowledge, this is the first work investigating the benefit of exploiting image semantics simultaneously for image denoising and high-level vision tasks via deep learning. The code is available online https://github.com/Ding-Liu/DeepDenoising.
Tasks	Denoising, Image Denoising
Published	2017-06-14
URL	http://arxiv.org/abs/1706.04284v3
PDF	http://arxiv.org/pdf/1706.04284v3.pdf
PWC	https://paperswithcode.com/paper/when-image-denoising-meets-high-level-vision
Repo	https://github.com/rgsl888/U-Finger-A-Fingerprint-Denosing-Network
Framework	none

Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?


Title	Does Higher Order LSTM Have Better Accuracy for Segmenting and Labeling Sequence Data?
Authors	Yi Zhang, Xu Sun, Shuming Ma, Yang Yang, Xuancheng Ren
Abstract	Existing neural models usually predict the tag of the current token independent of the neighboring tags. The popular LSTM-CRF model considers the tag dependencies between every two consecutive tags. However, it is hard for existing neural models to take longer distance dependencies of tags into consideration. The scalability is mainly limited by the complex model structures and the cost of dynamic programming during training. In our work, we first design a new model called “high order LSTM” to predict multiple tags for the current token which contains not only the current tag but also the previous several tags. We call the number of tags in one prediction as “order”. Then we propose a new method called Multi-Order BiLSTM (MO-BiLSTM) which combines low order and high order LSTMs together. MO-BiLSTM keeps the scalability to high order models with a pruning technique. We evaluate MO-BiLSTM on all-phrase chunking and NER datasets. Experiment results show that MO-BiLSTM achieves the state-of-the-art result in chunking and highly competitive results in two NER datasets.
Tasks	Chunking
Published	2017-11-22
URL	http://arxiv.org/abs/1711.08231v3
PDF	http://arxiv.org/pdf/1711.08231v3.pdf
PWC	https://paperswithcode.com/paper/does-higher-order-lstm-have-better-accuracy
Repo	https://github.com/lancopku/Multi-Order-LSTM
Framework	tf

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting


Title	Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Authors	Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha
Abstract	Machine learning algorithms, when applied to sensitive data, pose a distinct threat to privacy. A growing body of prior work demonstrates that models produced by these algorithms may leak specific private information in the training data to an attacker, either through the models’ structure or their observable behavior. However, the underlying cause of this privacy risk is not well understood beyond a handful of anecdotal accounts that suggest overfitting and influence might play a role. This paper examines the effect that overfitting and influence have on the ability of an attacker to learn information about the training data from machine learning models, either through training set membership inference or attribute inference attacks. Using both formal and empirical analyses, we illustrate a clear relationship between these factors and the privacy risk that arises in several popular machine learning algorithms. We find that overfitting is sufficient to allow an attacker to perform membership inference and, when the target attribute meets certain conditions about its influence, attribute inference attacks. Interestingly, our formal analysis also shows that overfitting is not necessary for these attacks and begins to shed light on what other factors may be in play. Finally, we explore the connection between membership inference and attribute inference, showing that there are deep connections between the two that lead to effective new attacks.
Tasks
Published	2017-09-05
URL	http://arxiv.org/abs/1709.01604v5
PDF	http://arxiv.org/pdf/1709.01604v5.pdf
PWC	https://paperswithcode.com/paper/privacy-risk-in-machine-learning-analyzing
Repo	https://github.com/samuel-yeom/ml-privacy-csf18
Framework	none


Title	A Deep Neural Architecture for Sentence-level Sentiment Classification in Twitter Social Networking
Authors	Huy Nguyen, Minh-Le Nguyen
Abstract	This paper introduces a novel deep learning framework including a lexicon-based approach for sentence-level prediction of sentiment label distribution. We propose to first apply semantic rules and then use a Deep Convolutional Neural Network (DeepCNN) for character-level embeddings in order to increase information for word-level embedding. After that, a Bidirectional Long Short-Term Memory Network (Bi-LSTM) produces a sentence-wide feature representation from the word-level embedding. We evaluate our approach on three Twitter sentiment classification datasets. Experimental results show that our model can improve the classification accuracy of sentence-level sentiment analysis in Twitter social networking.
Tasks	Sentiment Analysis
Published	2017-06-25
URL	http://arxiv.org/abs/1706.08032v1
PDF	http://arxiv.org/pdf/1706.08032v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-neural-architecture-for-sentence-level
Repo	https://github.com/huynt-plus/papers
Framework	none

Learning to Discover Cross-Domain Relations with Generative Adversarial Networks


Title	Learning to Discover Cross-Domain Relations with Generative Adversarial Networks
Authors	Taeksoo Kim, Moonsu Cha, Hyunsoo Kim, Jung Kwon Lee, Jiwon Kim
Abstract	While humans easily recognize relations between data from different domains without any supervision, learning to automatically discover them is in general very challenging and needs many ground-truth pairs that illustrate the relations. To avoid costly pairing, we address the task of discovering cross-domain relations given unpaired data. We propose a method based on generative adversarial networks that learns to discover relations between different domains (DiscoGAN). Using the discovered relations, our proposed network successfully transfers style from one domain to another while preserving key attributes such as orientation and face identity. Source code for official implementation is publicly available https://github.com/SKTBrain/DiscoGAN
Tasks
Published	2017-03-15
URL	http://arxiv.org/abs/1703.05192v2
PDF	http://arxiv.org/pdf/1703.05192v2.pdf
PWC	https://paperswithcode.com/paper/learning-to-discover-cross-domain-relations
Repo	https://github.com/eriklindernoren/PyTorch-GAN
Framework	pytorch

Learning Visual Importance for Graphic Designs and Data Visualizations


Title	Learning Visual Importance for Graphic Designs and Data Visualizations
Authors	Zoya Bylinskii, Nam Wook Kim, Peter O’Donovan, Sami Alsheikh, Spandan Madan, Hanspeter Pfister, Fredo Durand, Bryan Russell, Aaron Hertzmann
Abstract	Knowing where people look and click on visual designs can provide clues about how the designs are perceived, and where the most important or relevant content lies. The most important content of a visual design can be used for effective summarization or to facilitate retrieval from a database. We present automated models that predict the relative importance of different elements in data visualizations and graphic designs. Our models are neural networks trained on human clicks and importance annotations on hundreds of designs. We collected a new dataset of crowdsourced importance, and analyzed the predictions of our models with respect to ground truth importance and human eye movements. We demonstrate how such predictions of importance can be used for automatic design retargeting and thumbnailing. User studies with hundreds of MTurk participants validate that, with limited post-processing, our importance-driven applications are on par with, or outperform, current state-of-the-art methods, including natural image saliency. We also provide a demonstration of how our importance predictions can be built into interactive design tools to offer immediate feedback during the design process.
Tasks
Published	2017-08-08
URL	http://arxiv.org/abs/1708.02660v1
PDF	http://arxiv.org/pdf/1708.02660v1.pdf
PWC	https://paperswithcode.com/paper/learning-visual-importance-for-graphic
Repo	https://github.com/egorabaturov/visimportance-in-pytorch
Framework	pytorch

A Large Self-Annotated Corpus for Sarcasm


Title	A Large Self-Annotated Corpus for Sarcasm
Authors	Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli
Abstract	We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for sarcasm research and for training and evaluating systems for sarcasm detection. The corpus has 1.3 million sarcastic statements – 10 times more than any previous dataset – and many times more instances of non-sarcastic statements, allowing for learning in both balanced and unbalanced label regimes. Each statement is furthermore self-annotated – sarcasm is labeled by the author, not an independent annotator – and provided with user, topic, and conversation context. We evaluate the corpus for accuracy, construct benchmarks for sarcasm detection, and evaluate baseline methods.
Tasks	Sarcasm Detection
Published	2017-04-19
URL	http://arxiv.org/abs/1704.05579v4
PDF	http://arxiv.org/pdf/1704.05579v4.pdf
PWC	https://paperswithcode.com/paper/a-large-self-annotated-corpus-for-sarcasm
Repo	https://github.com/NLPrinceton/SARC
Framework	none

GANs for Biological Image Synthesis


Title	GANs for Biological Image Synthesis
Authors	Anton Osokin, Anatole Chessel, Rafael E. Carazo Salas, Federico Vaggi
Abstract	In this paper, we propose a novel application of Generative Adversarial Networks (GAN) to the synthesis of cells imaged by fluorescence microscopy. Compared to natural images, cells tend to have a simpler and more geometric global structure that facilitates image generation. However, the correlation between the spatial pattern of different fluorescent proteins reflects important biological functions, and synthesized images have to capture these relationships to be relevant for biological applications. We adapt GANs to the task at hand and propose new models with casual dependencies between image channels that can generate multi-channel images, which would be impossible to obtain experimentally. We evaluate our approach using two independent techniques and compare it against sensible baselines. Finally, we demonstrate that by interpolating across the latent space we can mimic the known changes in protein localization that occur through time during the cell cycle, allowing us to predict temporal evolution from static images.
Tasks	Image Generation
Published	2017-08-15
URL	http://arxiv.org/abs/1708.04692v2
PDF	http://arxiv.org/pdf/1708.04692v2.pdf
PWC	https://paperswithcode.com/paper/gans-for-biological-image-synthesis
Repo	https://github.com/aosokin/biogans
Framework	pytorch

Crossmodal Attentive Skill Learner


Title	Crossmodal Attentive Skill Learner
Authors	Shayegan Omidshafiei, Dong-Ki Kim, Jason Pazis, Jonathan P. How
Abstract	This paper presents the Crossmodal Attentive Skill Learner (CASL), integrated with the recently-introduced Asynchronous Advantage Option-Critic (A2OC) architecture [Harb et al., 2017] to enable hierarchical reinforcement learning across multiple sensory inputs. We provide concrete examples where the approach not only improves performance in a single task, but accelerates transfer to new tasks. We demonstrate the attention mechanism anticipates and identifies useful latent features, while filtering irrelevant sensor modalities during execution. We modify the Arcade Learning Environment [Bellemare et al., 2013] to support audio queries, and conduct evaluations of crossmodal learning in the Atari 2600 game Amidar. Finally, building on the recent work of Babaeizadeh et al. [2017], we open-source a fast hybrid CPU-GPU implementation of CASL.
Tasks	Atari Games, Hierarchical Reinforcement Learning
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10314v3
PDF	http://arxiv.org/pdf/1711.10314v3.pdf
PWC	https://paperswithcode.com/paper/crossmodal-attentive-skill-learner
Repo	https://github.com/shayegano/CASL
Framework	tf

GraphGAN: Graph Representation Learning with Generative Adversarial Nets


Title	GraphGAN: Graph Representation Learning with Generative Adversarial Nets
Authors	Hongwei Wang, Jia Wang, Jialin Wang, Miao Zhao, Weinan Zhang, Fuzheng Zhang, Xing Xie, Minyi Guo
Abstract	The goal of graph representation learning is to embed each vertex in a graph into a low-dimensional vector space. Existing graph representation learning methods can be classified into two categories: generative models that learn the underlying connectivity distribution in the graph, and discriminative models that predict the probability of edge existence between a pair of vertices. In this paper, we propose GraphGAN, an innovative graph representation learning framework unifying above two classes of methods, in which the generative model and discriminative model play a game-theoretical minimax game. Specifically, for a given vertex, the generative model tries to fit its underlying true connectivity distribution over all other vertices and produces “fake” samples to fool the discriminative model, while the discriminative model tries to detect whether the sampled vertex is from ground truth or generated by the generative model. With the competition between these two models, both of them can alternately and iteratively boost their performance. Moreover, when considering the implementation of generative model, we propose a novel graph softmax to overcome the limitations of traditional softmax function, which can be proven satisfying desirable properties of normalization, graph structure awareness, and computational efficiency. Through extensive experiments on real-world datasets, we demonstrate that GraphGAN achieves substantial gains in a variety of applications, including link prediction, node classification, and recommendation, over state-of-the-art baselines.
Tasks	Graph Representation Learning, Link Prediction, Node Classification, Representation Learning
Published	2017-11-22
URL	http://arxiv.org/abs/1711.08267v1
PDF	http://arxiv.org/pdf/1711.08267v1.pdf
PWC	https://paperswithcode.com/paper/graphgan-graph-representation-learning-with
Repo	https://github.com/hwwang55/GraphGAN
Framework	tf

Iterative Random Forests to detect predictive and stable high-order interactions


Title	Iterative Random Forests to detect predictive and stable high-order interactions
Authors	Sumanta Basu, Karl Kumbier, James B. Brown, Bin Yu
Abstract	Genomics has revolutionized biology, enabling the interrogation of whole transcriptomes, genome-wide binding sites for proteins, and many other molecular processes. However, individual genomic assays measure elements that interact in vivo as components of larger molecular machines. Understanding how these high-order interactions drive gene expression presents a substantial statistical challenge. Building on Random Forests (RF), Random Intersection Trees (RITs), and through extensive, biologically inspired simulations, we developed the iterative Random Forest algorithm (iRF). iRF trains a feature-weighted ensemble of decision trees to detect stable, high-order interactions with same order of computational cost as RF. We demonstrate the utility of iRF for high-order interaction discovery in two prediction problems: enhancer activity in the early Drosophila embryo and alternative splicing of primary transcripts in human derived cell lines. In Drosophila, among the 20 pairwise transcription factor interactions iRF identifies as stable (returned in more than half of bootstrap replicates), 80% have been previously reported as physical interactions. Moreover, novel third-order interactions, e.g. between Zelda (Zld), Giant (Gt), and Twist (Twi), suggest high-order relationships that are candidates for follow-up experiments. In human-derived cells, iRF re-discovered a central role of H3K36me3 in chromatin-mediated splicing regulation, and identified novel 5th and 6th order interactions, indicative of multi-valent nucleosomes with specific roles in splicing regulation. By decoupling the order of interactions from the computational cost of identification, iRF opens new avenues of inquiry into the molecular mechanisms underlying genome biology.
Tasks
Published	2017-06-26
URL	http://arxiv.org/abs/1706.08457v4
PDF	http://arxiv.org/pdf/1706.08457v4.pdf
PWC	https://paperswithcode.com/paper/iterative-random-forests-to-detect-predictive
Repo	https://github.com/Yu-Group/iterative-Random-Forest
Framework	none

Pileup Mitigation with Machine Learning (PUMML)


Title	Pileup Mitigation with Machine Learning (PUMML)
Authors	Patrick T. Komiske, Eric M. Metodiev, Benjamin Nachman, Matthew D. Schwartz
Abstract	Pileup involves the contamination of the energy distribution arising from the primary collision of interest (leading vertex) by radiation from soft collisions (pileup). We develop a new technique for removing this contamination using machine learning and convolutional neural networks. The network takes as input the energy distribution of charged leading vertex particles, charged pileup particles, and all neutral particles and outputs the energy distribution of particles coming from leading vertex alone. The PUMML algorithm performs remarkably well at eliminating pileup distortion on a wide range of simple and complex jet observables. We test the robustness of the algorithm in a number of ways and discuss how the network can be trained directly on data.
Tasks
Published	2017-07-26
URL	http://arxiv.org/abs/1707.08600v3
PDF	http://arxiv.org/pdf/1707.08600v3.pdf
PWC	https://paperswithcode.com/paper/pileup-mitigation-with-machine-learning-pumml
Repo	https://github.com/pkomiske/PUMML
Framework	none

Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner


Title	Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
Authors	Tseng-Hung Chen, Yuan-Hong Liao, Ching-Yao Chuang, Wan-Ting Hsu, Jianlong Fu, Min Sun
Abstract	Impressive image captioning results are achieved in domains with plenty of training image and sentence pairs (e.g., MSCOCO). However, transferring to a target domain with significant domain shifts but no paired training data (referred to as cross-domain image captioning) remains largely unexplored. We propose a novel adversarial training procedure to leverage unpaired data in the target domain. Two critic networks are introduced to guide the captioner, namely domain critic and multi-modal critic. The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain. The multi-modal critic assesses whether an image and its generated sentence are a valid pair. During training, the critics and captioner act as adversaries – captioner aims to generate indistinguishable sentences, whereas critics aim at distinguishing them. The assessment improves the captioner through policy gradient updates. During inference, we further propose a novel critic-based planning method to select high-quality sentences without additional supervision (e.g., tags). To evaluate, we use MSCOCO as the source domain and four other datasets (CUB-200-2011, Oxford-102, TGIF, and Flickr30k) as the target domains. Our method consistently performs well on all datasets. In particular, on CUB-200-2011, we achieve 21.8% CIDEr-D improvement after adaptation. Utilizing critics during inference further gives another 4.5% boost.
Tasks	Image Captioning
Published	2017-05-02
URL	http://arxiv.org/abs/1705.00930v2
PDF	http://arxiv.org/pdf/1705.00930v2.pdf
PWC	https://paperswithcode.com/paper/show-adapt-and-tell-adversarial-training-of
Repo	https://github.com/tsenghungchen/show-adapt-and-tell
Framework	tf