pwc-archive/papers-with-abstracts huggingface dataset:
FluidC+: A novel community detection algorithm based on fluid propagation
Authors: Jinfang Sheng, Kai Wang, Zejun Sun, Jie Hu, Bin Wang and Aman Ullah
Date: 2019-04-26 00:00:00
Proceeding: International Journal of Modern Physics C 2019 4
Tasks: Community Detection

In recent years, community detection has gradually become a hot topic in the complex network data mining field. The research of community detection is helpful not only to understand network topology structure but also to explore network hiding function. In this paper, we improve FluidC which is a novel community detection algorithm based on fluid propagation, by ameliorating the quality of seed set based on positive feedback and determining the node update order. We first summarize the shortcomings of FluidC and analyze the reasons result in these drawbacks. Then, we took some effective measures to overcome them and proposed an efficient community detection algorithm, called FluidC+. Finally, experiments on the generated network and real-world network show that our method not only greatly improves the performance of the original algorithm FluidC but also is better than many state-of-the-art algorithms, especially in the performance on real-world network with ground truth.
Generative locally linear embedding: A module for manifold unfolding and visualization
Authors: Benyamin Ghojogh, Ali Ghodsi, Fakhri Karray, Mark Crowley
Date: 2021-07-06 00:00:00
Proceeding: Software Impacts 2021 7
Tasks: Data Visualization, Dimensionality Reduction, Variational Inference

Data often have nonlinear patterns in machine learning. One can unfold the nonlinear manifold of a dataset for low-dimensional visualization and feature extraction. Locally Linear Embedding (LLE) is a nonlinear spectral method for dimensionality reduction and manifold unfolding. It embeds data using the same linear reconstruction weights as in the input space. In this paper, we propose an open source module which not only implements LLE, but also includes implementations of two generative LLE algorithms whose linear reconstruction phases are stochastic. Using this module, one can generate as many manifold unfoldings as desired for data visualization or feature extraction.
Increasing FPS for single board computers and embedded computers in 2021 (Jetson nano and YOVOv4-tiny). Practice and review
Authors: Ildar R
Date: 2021-04-12 00:00:00
Proceeding: journal 2021 4

This manuscript provides a review of methods for increasing the frame per second of single-board
computers. The main emphasis is on the Jetson family of single-board computers from Nvidia
Company, due to the possibility of using a graphical interface for calculations. But taking into account
the popular low-cost segment of single-board computers as RaspberryPI family, BananaPI, OrangePI,
etc., we also provided an overview of methods for increasing the frame per second without using a
Graphics Processing Unit. We considered frameworks, software development kit, and various libraries
that can be used in the process of increasing the frame per second in single-board computers. Finally,
we tested the presented methods for the YOLOv4-tiny model with a custom dataset on the Jetson nano
and presented the results in the table.
AASAE: Augmentation-Augmented Stochastic Autoencoders
Authors: William Falcon, Ananya Harsh Jha, Teddy Koker, Kyunghyun Cho
Date: 2021-07-26 00:00:00
Tasks: Contrastive Learning, Data Augmentation, image-classification, Image Classification, Self-Supervised Learning

Recent methods for self-supervised learning can be grouped into two paradigms: contrastive and non-contrastive approaches. Their success can largely be attributed to data augmentation pipelines which generate multiple views of a single input that preserve the underlying semantics. In this work, we introduce augmentation-augmented stochastic autoencoders (AASAE), yet another alternative to self-supervised learning, based on autoencoding. We derive AASAE starting from the conventional variational autoencoder (VAE), by replacing the KL divergence regularization, which is agnostic to the input domain, with data augmentations that explicitly encourage the internal representations to encode domain-specific invariances and equivariances. We empirically evaluate the proposed AASAE on image classification, similar to how recent contrastive and non-contrastive learning algorithms have been evaluated. Our experiments confirm the effectiveness of data augmentation as a replacement for KL divergence regularization. The AASAE outperforms the VAE by 30% on CIFAR-10, 40% on STL-10 and 45% on Imagenet. On CIFAR-10 and STL-10, the results for AASAE are largely comparable to the state-of-the-art algorithms for self-supervised learning.
Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
Authors: Yuxin Chen, Ziqi Zhang, Chunfeng Yuan, Bing Li, Ying Deng, Weiming Hu
Date: 2021-07-26 00:00:00
Proceeding: ICCV 2021 10
Tasks: Action Recognition, Skeleton Based Action Recognition

Graph convolutional networks (GCNs) have been widely used and achieved remarkable results in skeleton-based action recognition. In GCNs, graph topology dominates feature aggregation and therefore is the key to extracting representative features. In this work, we propose a novel Channel-wise Topology Refinement Graph Convolution (CTR-GC) to dynamically learn different topologies and effectively aggregate joint features in different channels for skeleton-based action recognition. The proposed CTR-GC models channel-wise topologies through learning a shared topology as a generic prior for all channels and refining it with channel-specific correlations for each channel. Our refinement method introduces few extra parameters and significantly reduces the difficulty of modeling channel-wise topologies. Furthermore, via reformulating graph convolutions into a unified form, we find that CTR-GC relaxes strict constraints of graph convolutions, leading to stronger representation capability. Combining CTR-GC with temporal modeling modules, we develop a powerful graph convolutional network named CTR-GCN which notably outperforms state-of-the-art methods on the NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets.
EiGLasso: Scalable Estimation of Cartesian Product of Sparse Inverse Covariance Matrices
Authors: Jun Ho Yoon, Seyoung Kim
Date: 2020-08-01 00:00:00
Proceeding: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) 2020 8

In this paper, we address the problem of jointly estimating dependencies across samples and dependencies across multiple features, where each set of dependencies is modeled as an inverse covariance matrix. In particular, we study a matrix-variate Gaussian distribution with the Kronecker-sum of sample-wise and feature-wise inverse covariances. While this Kronecker-sum model has been studied as an intuitively more appealing convex alternative to the Kronecker-product of two inverse covariance matrices, the existing methods do not scale to large datasets. We introduce a highly-efficient optimization method for estimating the Kronecker-sum structured inverse covariance matrix from matrix-variate data. In addition, we describe an alternative simpler approach for handling the non-identifiability of parameters than the strategies proposed in previous works. Using simulated and real data, we demonstrate our approach leads to one or two orders-of-magnitude speedup of the previous methods.
Parametric Contrastive Learning
Authors: Jiequan Cui, Zhisheng Zhong, Shu Liu, Bei Yu, Jiaya Jia
Date: 2021-07-26 00:00:00
Proceeding: ICCV 2021 10
Tasks: Contrastive Learning, Image Classification, Long-tail Learning

In this paper, we propose Parametric Contrastive Learning (PaCo) to tackle long-tailed recognition. Based on theoretical analysis, we observe supervised contrastive loss tends to bias on high-frequency classes and thus increases the difficulty of imbalanced learning. We introduce a set of parametric class-wise learnable centers to rebalance from an optimization perspective. Further, we analyze our PaCo loss under a balanced setting. Our analysis demonstrates that PaCo can adaptively enhance the intensity of pushing samples of the same class close as more samples are pulled together with their corresponding centers and benefit hard example learning. Experiments on long-tailed CIFAR, ImageNet, Places, and iNaturalist 2018 manifest the new state-of-the-art for long-tailed recognition. On full ImageNet, models trained with PaCo loss surpass supervised contrastive learning across various ResNet backbones, e.g., our ResNet-200 achieves 81.8% top-1 accuracy. Our code is available at https://github.com/dvlab-research/Parametric-Contrastive-Learning.
Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data
Authors: Yuqian Fu, Yanwei Fu, Yu-Gang Jiang
Date: 2021-07-26 00:00:00
Tasks: Cross-Domain Few-Shot, cross-domain few-shot learning, Few-Shot Learning, Meta-Learning

A recent study finds that existing few-shot learning methods, trained on the source domain, fail to generalize to the novel target domain when a domain gap is observed. This motivates the task of Cross-Domain Few-Shot Learning (CD-FSL). In this paper, we realize that the labeled target data in CD-FSL has not been leveraged in any way to help the learning process. Thus, we advocate utilizing few labeled target data to guide the model learning. Technically, a novel meta-FDMixup network is proposed. We tackle this problem mainly from two aspects. Firstly, to utilize the source and the newly introduced target data of two different class sets, a mixup module is re-proposed and integrated into the meta-learning mechanism. Secondly, a novel disentangle module together with a domain classifier is proposed to extract the disentangled domain-irrelevant and domain-specific features. These two modules together enable our model to narrow the domain gap thus generalizing well to the target datasets. Additionally, a detailed feasibility and pilot study is conducted to reflect the intuitive understanding of CD-FSL under our new setting. Experimental results show the effectiveness of our new setting and the proposed method. Codes and models are available at https://github.com/lovelyqian/Meta-FDMixup.
Rohingya Refugee Crisis and the State of Insecurity in Bangladesh
Authors: Hossain Ahmed Taufiq
Date: 2021-07-26 00:00:00
Tasks: Humanitarian

The ongoing Rohingya refugee crisis is considered as one of the largest human-made humanitarian disasters of the 21st century. So far, Bangladesh is the largest recipient of these refugees. According to the United Nations Office for the Coordination of Humanitarian Affairs (UN OCHA), approximately 650,000 new entrants have been recorded since the new violence erupted on 25 August 2017 in the Rakhine state of Myanmar.1 However, such crisis is nothing new in Bangladesh, nor are the security-related challenges new that such an exodus brings with it. Ever since the military came to power in Myanmar (in 1962), Rohingya exodus to neighboring countries became a recurring incident. The latest mass exodus of Rohingyas from Rakhine state of Myanmar to Bangladesh is the largest of such influxes. Unlike, the previous refugee crisis, the ongoing crisis has wide-ranging security implications on Bangladesh. They are also varied and multifaceted. Thus, responsibilities for ensuring effective protection have become operationally multilateral. The problem of security regarding the Rohingya refugee issue is complicated by the Islamist insurgency, illicit methamphetamine/yaba drug trafficking, and HIV/AIDS/STI prevalence factors. The chapter examines the different dimensions of security challenges that the recent spell of Rohingya exodus brings to Bangladesh and the refugees themselves. In order to understand the challenges, firstly the chapter attempts to conceptualize the prominent security frameworks. Secondly, it examines the context and political economy behind the persecution of Rohingyas in the Rakhine state. Thirdly, it explores the political and military aspects of security. Fourthly, it explores the social and economic dimensions. Finally, it examines the environmental impacts of Rohingya crisis in Bangladesh.
HANet: Hierarchical Alignment Networks for Video-Text Retrieval
Authors: Peng Wu, Xiangteng He, Mingqian Tang, Yiliang Lv, Jing Liu
Date: 2021-07-26 00:00:00
Tasks: Retrieval, Text Matching, Text Retrieval, text similarity, Video-Text Retrieval

Video-text retrieval is an important yet challenging task in vision-language understanding, which aims to learn a joint embedding space where related video and text instances are close to each other. Most current works simply measure the video-text similarity based on video-level and text-level embeddings. However, the neglect of more fine-grained or local information causes the problem of insufficient representation. Some works exploit the local details by disentangling sentences, but overlook the corresponding videos, causing the asymmetry of video-text representation. To address the above limitations, we propose a Hierarchical Alignment Network (HANet) to align different level representations for video-text matching. Specifically, we first decompose video and text into three semantic levels, namely event (video and text), action (motion and verb), and entity (appearance and noun). Based on these, we naturally construct hierarchical representations in the individual-local-global manner, where the individual level focuses on the alignment between frame and word, local level focuses on the alignment between video clip and textual context, and global level focuses on the alignment between the whole video and text. Different level alignments capture fine-to-coarse correlations between video and text, as well as take the advantage of the complementary information among three semantic levels. Besides, our HANet is also richly interpretable by explicitly learning key semantic concepts. Extensive experiments on two public datasets, namely MSR-VTT and VATEX, show the proposed HANet outperforms other state-of-the-art methods, which demonstrates the effectiveness of hierarchical representation and alignment. Our code is publicly available.
Contextual Transformer Networks for Visual Recognition
Authors: Yehao Li, Ting Yao, Yingwei Pan, Tao Mei
Date: 2021-07-26 00:00:00
Tasks: Image Classification, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

Transformer with self-attention has led to the revolutionizing of natural language processing field, and recently inspires the emergence of Transformer-style architecture design with competitive results in numerous computer vision tasks. Nevertheless, most of existing designs directly employ self-attention over a 2D feature map to obtain the attention matrix based on pairs of isolated queries and keys at each spatial location, but leave the rich contexts among neighbor keys under-exploited. In this work, we design a novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition. Such design fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation. Technically, CoT block first contextually encodes input keys via a $3\times3$ convolution, leading to a static contextual representation of inputs. We further concatenate the encoded keys with input queries to learn the dynamic multi-head attention matrix through two consecutive $1\times1$ convolutions. The learnt attention matrix is multiplied by input values to achieve the dynamic contextual representation of inputs. The fusion of the static and dynamic contextual representations are finally taken as outputs. Our CoT block is appealing in the view that it can readily replace each $3\times3$ convolution in ResNet architectures, yielding a Transformer-style backbone named as Contextual Transformer Networks (CoTNet). Through extensive experiments over a wide range of applications (e.g., image recognition, object detection and instance segmentation), we validate the superiority of CoTNet as a stronger backbone. Source code is available at \url{https://github.com/JDAI-CV/CoTNet}.
Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
Authors: Chengcheng Han, Zeqiu Fan, Dongxiang Zhang, Minghui Qiu, Ming Gao, Aoying Zhou
Date: 2021-07-26 00:00:00
Proceeding: Findings (ACL) 2021 8
Tasks: Domain Adaptation, Few-Shot Text Classification, Meta-Learning, text-classification, Text Classification

Meta-learning has emerged as a trending technique to tackle few-shot text classification and achieved state-of-the-art performance. However, existing solutions heavily rely on the exploitation of lexical features and their distributional signatures on training data, while neglecting to strengthen the model's ability to adapt to new tasks. In this paper, we propose a novel meta-learning framework integrated with an adversarial domain adaptation network, aiming to improve the adaptive ability of the model and generate high-quality text embedding for new classes. Extensive experiments are conducted on four benchmark datasets and our method demonstrates clear superiority over the state-of-the-art models in all the datasets. In particular, the accuracy of 1-shot and 5-shot classification on the dataset of 20 Newsgroups is boosted from 52.1% to 59.6%, and from 68.3% to 77.8%, respectively.
Belief Propagation as Diffusion
Authors: Olivier Peltre
Date: 2021-07-26 00:00:00

We introduce novel belief propagation algorithms to estimate the marginals of a high dimensional probability distribution. They involve natural (co)homological constructions relevant for a localised description of statistical systems.
DYPLODOC: Dynamic Plots for Document Classification
Authors: Anastasia Malysheva, Alexey Tikhonov, Ivan P. Yamshchikov
Date: 2021-07-26 00:00:00
Tasks: Classification, Document Classification

Narrative generation and analysis are still on the fringe of modern natural language processing yet are crucial in a variety of applications. This paper proposes a feature extraction method for plot dynamics. We present a dataset that consists of the plot descriptions for thirteen thousand TV shows alongside meta-information on their genres and dynamic plots extracted from them. We validate the proposed tool for plot dynamics extraction and discuss possible applications of this method to the tasks of narrative analysis and generation.
Thought Flow Nets: From Single Predictions to Trains of Model Thought
Authors: Hendrik Schuff, Heike Adel, Ngoc Thang Vu
Date: 2021-07-26 00:00:00
Tasks: Question Answering

When humans solve complex problems, they typically create a sequence of ideas (involving an intuitive decision, reflection, error correction, etc.) in order to reach a conclusive decision. Contrary to this, today's models are mostly trained to map an input to one single and fixed output. In this paper, we investigate how we can give models the opportunity of a second, third and $k$-th thought. Taking inspiration from Hegel's dialectics, we propose the concept of a thought flow which creates a sequence of predictions. We present a self-correction mechanism that is trained to estimate the model's correctness and performs iterative prediction updates based on the correctness prediction's gradient. We introduce our method at the example of question answering and conduct extensive experiments that demonstrate (i) our method's ability to correct its own predictions and (ii) its potential to notably improve model performances. In addition, we conduct a qualitative analysis of thought flow correction patterns and explore how thought flow predictions affect human users within a crowdsourcing study. We find that (iii) thought flows enable improved user performance and are perceived as more natural, correct, and intelligent as single and/or top-3 predictions.
Hindsight Value Function for Variance Reduction in Stochastic Dynamic Environment
Authors: Jiaming Guo, Rui Zhang, Xishan Zhang, Shaohui Peng, Qi Yi, Zidong Du, Xing Hu, Qi Guo, Yunji Chen
Date: 2021-07-26 00:00:00
Tasks: Deep Reinforcement Learning, Policy Gradient Methods

Policy gradient methods are appealing in deep reinforcement learning but suffer from high variance of gradient estimate. To reduce the variance, the state value function is applied commonly. However, the effect of the state value function becomes limited in stochastic dynamic environments, where the unexpected state dynamics and rewards will increase the variance. In this paper, we propose to replace the state value function with a novel hindsight value function, which leverages the information from the future to reduce the variance of the gradient estimate for stochastic dynamic environments. Particularly, to obtain an ideally unbiased gradient estimate, we propose an information-theoretic approach, which optimizes the embeddings of the future to be independent of previous actions. In our experiments, we apply the proposed hindsight value function in stochastic dynamic environments, including discrete-action environments and continuous-action environments. Compared with the standard state value function, the proposed hindsight value function consistently reduces the variance, stabilizes the training, and improves the eventual policy.
A General Theory for Client Sampling in Federated Learning
Authors: Yann Fraboni, Richard Vidal, Laetitia Kameni, Marco Lorenzi
Date: 2021-07-26 00:00:00
Tasks: Federated Learning

While client sampling is a central operation of current state-of-the-art federated learning (FL) approaches, the impact of this procedure on the convergence and speed of FL remains under-investigated. In this work, we provide a general theoretical framework to quantify the impact of a client sampling scheme and of the clients heterogeneity on the federated optimization. First, we provide a unified theoretical ground for previously reported sampling schemes experimental results on the relationship between FL convergence and the variance of the aggregation weights. Second, we prove for the first time that the quality of FL convergence is also impacted by the resulting covariance between aggregation weights. Our theory is general, and is here applied to Multinomial Distribution (MD) and Uniform sampling, two default unbiased client sampling schemes of FL, and demonstrated through a series of experiments in non-iid and unbalanced scenarios. Our results suggest that MD sampling should be used as default sampling scheme, due to the resilience to the changes in data ratio during the learning process, while Uniform sampling is superior only in the special case when clients have the same amount of data.