pwc-archive/papers-with-abstracts huggingface dataset:
FedSmart: An Auto Updating Federated Learning Optimization Mechanism
Authors: Anxun He, Jianzong Wang, Zhangcheng Huang, Jing Xiao
Date: 2020-09-16 00:00:00
Tasks: Federated Learning, Privacy Preserving

Federated learning has made an important contribution to data privacy-preserving. Many previous works are based on the assumption that the data are independently identically distributed (IID). As a result, the model performance on non-identically independently distributed (non-IID) data is beyond expectation, which is the concrete situation. Some existing methods of ensuring the model robustness on non-IID data, like the data-sharing strategy or pretraining, may lead to privacy leaking. In addition, there exist some participants who try to poison the model with low-quality data. In this paper, a performance-based parameter return method for optimization is introduced, we term it FederatedSmart (FedSmart). It optimizes different model for each client through sharing global gradients, and it extracts the data from each client as a local validation set, and the accuracy that model achieves in round t determines the weights of the next round. The experiment results show that FedSmart enables the participants to allocate a greater weight to the ones with similar data distribution.
Extremely Low Bit Transformer Quantization for On-Device Neural Machine Translation
Authors: Insoo Chung, Byeongwook Kim, Yoonjung Choi, Se Jung Kwon, Yongkweon Jeon, Baeseong Park, Sangha Kim, Dongsoo Lee
Date: 2020-09-16 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Machine Translation, NMT, Quantization, Translation

The deployment of widely used Transformer architecture is challenging because of heavy computation load and memory overhead during inference, especially when the target device is limited in computational resources such as mobile or edge devices. Quantization is an effective technique to address such challenges. Our analysis shows that for a given number of quantization bits, each block of Transformer contributes to translation quality and inference computations in different manners. Moreover, even inside an embedding block, each word presents vastly different contributions. Correspondingly, we propose a mixed precision quantization strategy to represent Transformer weights by an extremely low number of bits (e.g., under 3 bits). For example, for each word in an embedding block, we assign different quantization bits based on statistical property. Our quantized Transformer model achieves 11.8$\times$ smaller model size than the baseline model, with less than -0.5 BLEU. We achieve 8.3$\times$ reduction in run-time memory footprints and 3.5$\times$ speed up (Galaxy N10+) such that our proposed compression strategy enables efficient implementation for on-device NMT.
Information Bottleneck Constrained Latent Bidirectional Embedding for Zero-Shot Learning
Authors: Yang Liu, Lei Zhou, Xiao Bai, Lin Gu, Tatsuya Harada, Jun Zhou
Date: 2020-09-16 00:00:00
Tasks: Attribute, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize novel classes by transferring semantic knowledge from seen classes to unseen classes. Though many ZSL methods rely on a direct mapping between the visual and the semantic space, the calibration deviation and hubness problem limit the generalization capability to unseen classes. Recently emerged generative ZSL methods generate unseen image features to transform ZSL into a supervised classification problem. However, most generative models still suffer from the seen-unseen bias problem as only seen data is used for training. To address these issues, we propose a novel bidirectional embedding based generative model with a tight visual-semantic coupling constraint. We learn a unified latent space that calibrates the embedded parametric distributions of both visual and semantic spaces. Since the embedding from high-dimensional visual features comprise much non-semantic information, the alignment of visual and semantic in latent space would inevitably been deviated. Therefore, we introduce information bottleneck (IB) constraint to ZSL for the first time to preserve essential attribute information during the mapping. Specifically, we utilize the uncertainty estimation and the wake-sleep procedure to alleviate the feature noises and improve model abstraction capability. In addition, our method can be easily extended to transductive ZSL setting by generating labels for unseen images. We then introduce a robust loss to solve this label noise problem. Extensive experimental results show that our method outperforms the state-of-the-art methods in different ZSL settings on most benchmark datasets. The code will be available at https://github.com/osierboy/IBZSL.
Question Directed Graph Attention Network for Numerical Reasoning over Text
Authors: Kunlong Chen, Weidi Xu, Xingyi Cheng, Zou Xiaochuan, Yuyu Zhang, Le Song, Taifeng Wang, Yuan Qi, Wei Chu
Date: 2020-09-16 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Graph Attention, Machine Reading Comprehension, Natural Language Understanding, Question Answering, Reading Comprehension

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. The code link is at: https://github.com/emnlp2020qdgat/QDGAT
Exploring Font-independent Features for Scene Text Recognition
Authors: Yizhi Wang, Zhouhui Lian
Date: 2020-09-16 00:00:00
Tasks: Image-to-Image Translation, Scene Text Recognition, Translation

Scene text recognition (STR) has been extensively studied in last few years. Many recently-proposed methods are specially designed to accommodate the arbitrary shape, layout and orientation of scene texts, but ignoring that various font (or writing) styles also pose severe challenges to STR. These methods, where font features and content features of characters are tangled, perform poorly in text recognition on scene images with texts in novel font styles. To address this problem, we explore font-independent features of scene texts via attentional generation of glyphs in a large number of font styles. Specifically, we introduce trainable font embeddings to shape the font styles of generated glyphs, with the image feature of scene text only representing its essential patterns. The generation process is directed by the spatial attention mechanism, which effectively copes with irregular texts and generates higher-quality glyphs than existing image-to-image translation methods. Experiments conducted on several STR benchmarks demonstrate the superiority of our method compared to the state of the art.
Theory of Mind with Guilt Aversion Facilitates Cooperative Reinforcement Learning
Authors: Dung Nguyen, Svetha Venkatesh, Phuoc Nguyen, Truyen Tran
Date: 2020-09-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Guilt aversion induces experience of a utility loss in people if they believe they have disappointed others, and this promotes cooperative behaviour in human. In psychological game theory, guilt aversion necessitates modelling of agents that have theory about what other agents think, also known as Theory of Mind (ToM). We aim to build a new kind of affective reinforcement learning agents, called Theory of Mind Agents with Guilt Aversion (ToMAGA), which are equipped with an ability to think about the wellbeing of others instead of just self-interest. To validate the agent design, we use a general-sum game known as Stag Hunt as a test bed. As standard reinforcement learning agents could learn suboptimal policies in social dilemmas like Stag Hunt, we propose to use belief-based guilt aversion as a reward shaping mechanism. We show that our belief-based guilt averse agents can efficiently learn cooperative behaviours in Stag Hunt Games.
On the Landscape of One-hidden-layer Sparse Networks and Beyond
Authors: Dachao Lin, Ruoyu Sun, Zhihua Zhang
Date: 2020-09-16 00:00:00
Tasks: Network Pruning

Sparse neural networks have received increasing interest due to their small size compared to dense networks. Nevertheless, most existing works on neural network theory have focused on dense neural networks, and the understanding of sparse networks is very limited. In this paper, we study the loss landscape of one-hidden-layer sparse networks. First, we consider sparse networks with a dense final layer. We show that linear networks can have no spurious valleys under special sparse structures, and non-linear networks could also admit no spurious valleys under a wide final layer. Second, we discover that spurious valleys and spurious minima can exist for wide sparse networks with a sparse final layer. This is different from wide dense networks which do not have spurious valleys under mild assumptions.
Weakly-Supervised Online Hashing
Authors: Yu-Wei Zhan, Xin Luo, Yu Sun, Yongxin Wang, Zhen-Duo Chen, Xin-Shun Xu
Date: 2020-09-16 00:00:00
Tasks: Image Retrieval, Retrieval

With the rapid development of social websites, recent years have witnessed an explosive growth of social images with user-provided tags which continuously arrive in a streaming fashion. Due to the fast query speed and low storage cost, hashing-based methods for image search have attracted increasing attention. However, existing hashing methods for social image retrieval are based on batch mode which violates the nature of social images, i.e., social images are usually generated periodically or collected in a stream fashion. Although there exist many online image hashing methods, they either adopt unsupervised learning which ignore the relevant tags, or are designed in the supervised manner which needs high-quality labels. In this paper, to overcome the above limitations, we propose a new method named Weakly-supervised Online Hashing (WOH). In order to learn high-quality hash codes, WOH exploits the weak supervision by considering the semantics of tags and removing the noise. Besides, We develop a discrete online optimization algorithm for WOH, which is efficient and scalable. Extensive experiments conducted on two real-world datasets demonstrate the superiority of WOH compared with several state-of-the-art hashing baselines.
A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition
Authors: Pawan Kumar Singh, Supratim Das, Ram Sarkar, Mita Nasipuri
Date: 2020-09-16 00:00:00
Tasks: Optical Character Recognition (OCR), Tree Decomposition

A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts. But in a country like India, where multi-script scenario is prevalent, identifying scripts beforehand becomes obligatory. In this paper, we present the significance of Gabor wavelets filters in extracting directional energy and entropy distributions for 11 official handwritten scripts namely, Bangla, Devanagari, Gujarati, Gurumukhi, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu and Roman. The experimentation is conducted at block level based on a quad-tree decomposition approach and evaluated using six different well-known classifiers. Finally, the best identification accuracy of 96.86% has been achieved by Multi Layer Perceptron (MLP) classifier for 3-fold cross validation at level-2 decomposition. The results serve to establish the efficacy of the present approach to the classification of handwritten Indic scripts
Handwritten Script Identification from Text Lines
Authors: Pawan Kumar Singh, Iman Chatterjee, Ram Sarkar, Mita Nasipuri
Date: 2020-09-16 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

In a multilingual country like India where 12 different official scripts are in use, automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents, searching for documents on the web/digital archives containing a particular script and for the selection of script specific Optical Character Recognition (OCR) system in a multilingual environment. In this paper, we propose a robust method towards identifying scripts from the handwritten documents at text line-level. The recognition is based upon features extracted using Chain Code Histogram (CCH) and Discrete Fourier Transform (DFT). The proposed method is experimented on 800 handwritten text lines written in seven Indic scripts namely, Gujarati, Kannada, Malayalam, Oriya, Tamil, Telugu, Urdu along with Roman script and yielded an average identification rate of 95.14% using Support Vector Machine (SVM) classifier.
Surgical Video Motion Magnification with Suppression of Instrument Artefacts
Authors: Mirek Janatka, Hani J. Marcus, Neil L. Dorward, Danail Stoyanov
Date: 2020-09-16 00:00:00
Tasks: Motion Magnification, SSIM

Video motion magnification could directly highlight subsurface blood vessels in endoscopic video in order to prevent inadvertent damage and bleeding. Applying motion filters to the full surgical image is however sensitive to residual motion from the surgical instruments and can impede practical application due to aberration motion artefacts. By storing the temporal filter response from local spatial frequency information for a single cardiovascular cycle prior to tool introduction to the scene, a filter can be used to determine if motion magnification should be active for a spatial region of the surgical image. In this paper, we propose a strategy to reduce aberration due to non-physiological motion for surgical video motion magnification. We present promising results on endoscopic transnasal transsphenoidal pituitary surgery with a quantitative comparison to recent methods using Structural Similarity (SSIM), as well as qualitative analysis by comparing spatio-temporal cross sections of the videos and individual frames.
An Extensive Experimental Evaluation of Automated Machine Learning Methods for Recommending Classification Algorithms (Extended Version)
Authors: Márcio P. Basgalupp, Rodrigo C. Barros, Alex G. C. de Sá, Gisele L. Pappa, Rafael G. Mantovani, André C. P. L. F. de Carvalho, Alex A. Freitas
Date: 2020-09-16 00:00:00
Tasks: AutoML, BIG-bench Machine Learning, Classification, Evolutionary Algorithms, General Classification, Meta-Learning

This paper presents an experimental comparison among four Automated Machine Learning (AutoML) methods for recommending the best classification algorithm for a given input dataset. Three of these methods are based on Evolutionary Algorithms (EAs), and the other is Auto-WEKA, a well-known AutoML method based on the Combined Algorithm Selection and Hyper-parameter optimisation (CASH) approach. The EA-based methods build classification algorithms from a single machine learning paradigm: either decision-tree induction, rule induction, or Bayesian network classification. Auto-WEKA combines algorithm selection and hyper-parameter optimisation to recommend classification algorithms from multiple paradigms. We performed controlled experiments where these four AutoML methods were given the same runtime limit for different values of this limit. In general, the difference in predictive accuracy of the three best AutoML methods was not statistically significant. However, the EA evolving decision-tree induction algorithms has the advantage of producing algorithms that generate interpretable classification models and that are more scalable to large datasets, by comparison with many algorithms from other learning paradigms that can be recommended by Auto-WEKA. We also observed that Auto-WEKA has shown meta-overfitting, a form of overfitting at the meta-learning level, rather than at the base-learning level.
Job2Vec: Job Title Benchmarking with Collective Multi-View Representation Learning
Authors: Denghui Zhang, Junming Liu, HengShu Zhu, Yanchi Liu, Lichen Wang, Pengyang Wang, Hui Xiong
Date: 2020-09-16 00:00:00
Tasks: Benchmarking, Link Prediction, Position, Representation Learning

Job Title Benchmarking (JTB) aims at matching job titles with similar expertise levels across various companies. JTB could provide precise guidance and considerable convenience for both talent recruitment and job seekers for position and salary calibration/prediction. Traditional JTB approaches mainly rely on manual market surveys, which is expensive and labor-intensive. Recently, the rapid development of Online Professional Graph has accumulated a large number of talent career records, which provides a promising trend for data-driven solutions. However, it is still a challenging task since (1) the job title and job transition (job-hopping) data is messy which contains a lot of subjective and non-standard naming conventions for the same position (e.g., Programmer, Software Development Engineer, SDE, Implementation Engineer), (2) there is a large amount of missing title/transition information, and (3) one talent only seeks limited numbers of jobs which brings the incompleteness and randomness modeling job transition patterns. To overcome these challenges, we aggregate all the records to construct a large-scale Job Title Benchmarking Graph (Job-Graph), where nodes denote job titles affiliated with specific companies and links denote the correlations between jobs. We reformulate the JTB as the task of link prediction over the Job-Graph that matched job titles should have links. Along this line, we propose a collective multi-view representation learning method (Job2Vec) by examining the Job-Graph jointly in (1) graph topology view, (2)semantic view, (3) job transition balance view, and (4) job transition duration view. We fuse the multi-view representations in the encode-decode paradigm to obtain a unified optimal representation for the task of link prediction. Finally, we conduct extensive experiments to validate the effectiveness of our proposed method.
Multi-Label Activity Recognition using Activity-specific Features and Activity Correlations
Authors: Yanyi Zhang, Xinyu Li, Ivan Marsic
Date: 2020-09-16 00:00:00
Proceeding: CVPR 2021 1
Tasks: Activity Recognition, Video Classification

Multi-label activity recognition is designed for recognizing multiple activities that are performed simultaneously or sequentially in each video. Most recent activity recognition networks focus on single-activities, that assume only one activity in each video. These networks extract shared features for all the activities, which are not designed for multi-label activities. We introduce an approach to multi-label activity recognition that extracts independent feature descriptors for each activity and learns activity correlations. This structure can be trained end-to-end and plugged into any existing network structures for video classification. Our method outperformed state-of-the-art approaches on four multi-label activity recognition datasets. To better understand the activity-specific features that the system generated, we visualized these activity-specific features in the Charades dataset.
Quasi-Autoregressive Residual (QuAR) Flows
Authors: Achintya Gopal
Date: 2020-09-16 00:00:00

Normalizing Flows are a powerful technique for learning and modeling probability distributions given samples from those distributions. The current state of the art results are built upon residual flows as these can model a larger hypothesis space than coupling layers. However, residual flows are extremely computationally expensive both to train and to use, which limits their applicability in practice. In this paper, we introduce a simplification to residual flows using a Quasi-Autoregressive (QuAR) approach. Compared to the standard residual flow approach, this simplification retains many of the benefits of residual flows while dramatically reducing the compute time and memory requirements, thus making flow-based modeling approaches far more tractable and broadening their potential applicability.
Too Much Information Kills Information: A Clustering Perspective
Authors: Yicheng Xu, Vincent Chau, Chenchen Wu, Yong Zhang, Vassilis Zissimopoulos, Yifei Zou
Date: 2020-09-16 00:00:00
Tasks: Clustering, Learning Theory

Clustering is one of the most fundamental tools in the artificial intelligence area, particularly in the pattern recognition and learning theory. In this paper, we propose a simple, but novel approach for variance-based k-clustering tasks, included in which is the widely known k-means clustering. The proposed approach picks a sampling subset from the given dataset and makes decisions based on the data information in the subset only. With certain assumptions, the resulting clustering is provably good to estimate the optimum of the variance-based objective with high probability. Extensive experiments on synthetic datasets and real-world datasets show that to obtain competitive results compared with k-means method (Llyod 1982) and k-means++ method (Arthur and Vassilvitskii 2007), we only need 7% information of the dataset. If we have up to 15% information of the dataset, then our algorithm outperforms both the k-means method and k-means++ method in at least 80% of the clustering tasks, in terms of the quality of clustering. Also, an extended algorithm based on the same idea guarantees a balanced k-clustering result.
Meta-AAD: Active Anomaly Detection with Deep Reinforcement Learning
Authors: Daochen Zha, Kwei-Herng Lai, Mingyang Wan, Xia Hu
Date: 2020-09-16 00:00:00
Tasks: Anomaly Detection, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Re-Ranking

High false-positive rate is a long-standing challenge for anomaly detection algorithms, especially in high-stake applications. To identify the true anomalies, in practice, analysts or domain experts will be employed to investigate the top instances one by one in a ranked list of anomalies identified by an anomaly detection system. This verification procedure generates informative labels that can be leveraged to re-rank the anomalies so as to help the analyst to discover more true anomalies given a time budget. Some re-ranking strategies have been proposed to approximate the above sequential decision process. Specifically, existing strategies have been focused on making the top instances more likely to be anomalous based on the feedback. Then they greedily select the top-1 instance for query. However, these greedy strategies could be sub-optimal since some low-ranked instances could be more helpful in the long-term. In this work, we propose Active Anomaly Detection with Meta-Policy (Meta-AAD), a novel framework that learns a meta-policy for query selection. Specifically, Meta-AAD leverages deep reinforcement learning to train the meta-policy to select the most proper instance to explicitly optimize the number of discovered anomalies throughout the querying process. Meta-AAD is easy to deploy since a trained meta-policy can be directly applied to any new datasets without further tuning. Extensive experiments on 24 benchmark datasets demonstrate that Meta-AAD significantly outperforms the state-of-the-art re-ranking strategies and the unsupervised baseline. The empirical analysis shows that the trained meta-policy is transferable and inherently achieves a balance between long-term and short-term rewards.