pwc-archive/papers-with-abstracts huggingface dataset:
Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials
Authors: Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi
Date: 2022-10-09 00:00:00
Tasks: CPU

Data-driven interatomic potentials have emerged as a powerful class of surrogate models for {\it ab initio} potential energy surfaces that are able to reliably predict macroscopic properties with experimental accuracy. In generating accurate and transferable potentials the most time-consuming and arguably most important task is generating the training set, which still requires significant expert user input. To accelerate this process, this work presents \text{\it hyperactive learning} (HAL), a framework for formulating an accelerated sampling algorithm specifically for the task of training database generation. The key idea is to start from a physically motivated sampler (e.g., molecular dynamics) and add a biasing term that drives the system towards high uncertainty and thus to unseen training configurations. Building on this framework, general protocols for building training databases for alloys and polymers leveraging the HAL framework will be presented. For alloys, ACE potentials for AlSi10 are created by fitting to a minimal HAL-generated database containing 88 configurations (32 atoms each) with fast evaluation times of <100 microsecond/atom/cpu-core. These potentials are demonstrated to predict the melting temperature with excellent accuracy. For polymers, a HAL database is built using ACE, able to determine the density of a long polyethylene glycol (PEG) polymer formed of 200 monomer units with experimental accuracy by only fitting to small isolated PEG polymers with sizes ranging from 2 to 32.
Computing non-equilibrium trajectories by a deep learning approach
Authors: Eric Simonnet
Date: 2022-10-08 00:00:00
Tasks: Deep Learning

Predicting the occurence of rare and extreme events in complex systems is a well-known problem in non-equilibrium physics. These events can have huge impacts on human societies. New approaches have emerged in the last ten years, which better estimate tail distributions. They often use large deviation concepts without the need to perform heavy direct ensemble simulations. In particular, a well-known approach is to derive a minimum action principle and to find its minimizers. The analysis of rare reactive events in non-equilibrium systems without detailed balance is notoriously difficult either theoretically and computationally. They are described in the limit of small noise by the Freidlin-Wentzell action. We propose here a new method which minimizes the geometrical action instead using neural networks: it is called deep gMAM. It relies on a natural and simple machine-learning formulation of the classical gMAM approach. We give a detailed description of the method as well as many examples. These include bimodal switches in complex stochastic (partial) differential equations, quasi-potential estimates, and extreme events in Burgers turbulence.
A two-stage approach for table extraction in invoices
Authors: Thomas Saout, Frédéric Lardeux, Frédéric Saubion
Date: 2022-10-10 00:00:00
Tasks: Table Extraction, Vocal Bursts Valence Prediction

The automated analysis of administrative documents is an important field in document recognition that is studied for decades. Invoices are key documents among these huge amounts of documents available in companies and public services. Invoices contain most of the time data that are presented in tables that should be clearly identified to extract suitable information. In this paper, we propose an approach that combines an image processing based estimation of the shape of the tables with a graph-based representation of the document, which is used to identify complex tables precisely. We propose an experimental evaluation using a real case application.
Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering for Recommendation
Authors: Kang Liu, Feng Xue, Xiangnan He, Dan Guo, Richang Hong
Date: 2022-10-10 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, Sensitivity

Graph Convolution Networks (GCNs), with their efficient ability to capture high-order connectivity in graphs, have been widely applied in recommender systems. Stacking multiple neighbor aggregation is the major operation in GCNs. It implicitly captures popularity features because the number of neighbor nodes reflects the popularity of a node. However, existing GCN-based methods ignore a universal problem: users' sensitivity to item popularity is differentiated, but the neighbor aggregations in GCNs actually fix this sensitivity through Graph Laplacian Normalization, leading to suboptimal personalization. In this work, we propose to model multi-grained popularity features and jointly learn them together with high-order connectivity, to match the differentiation of user preferences exhibited in popularity features. Specifically, we develop a Joint Multi-grained Popularity-aware Graph Convolution Collaborative Filtering model, short for JMP-GCF, which uses a popularity-aware embedding generation to construct multi-grained popularity features, and uses the idea of joint learning to capture the signals within and between different granularities of popularity features that are relevant for modeling user preferences. Additionally, we propose a multistage stacked training strategy to speed up model convergence. We conduct extensive experiments on three public datasets to show the state-of-the-art performance of JMP-GCF.
Improving Continual Relation Extraction through Prototypical Contrastive Learning
Authors: Chengwei Hu, Deqing Yang, Haoliang Jin, Zhen Chen, Yanghua Xiao
Date: 2022-10-10 00:00:00
Proceeding: COLING 2022 10
Tasks: Continual Relation Extraction, Contrastive Learning, Relation, Relation Extraction

Continual relation extraction (CRE) aims to extract relations towards the continuous and iterative arrival of new data, of which the major challenge is the catastrophic forgetting of old tasks. In order to alleviate this critical problem for enhanced CRE performance, we propose a novel Continual Relation Extraction framework with Contrastive Learning, namely CRECL, which is built with a classification network and a prototypical contrastive network to achieve the incremental-class learning of CRE. Specifically, in the contrastive network a given instance is contrasted with the prototype of each candidate relations stored in the memory module. Such contrastive learning scheme ensures the data distributions of all tasks more distinguishable, so as to alleviate the catastrophic forgetting further. Our experiment results not only demonstrate our CRECL's advantage over the state-of-the-art baselines on two public datasets, but also verify the effectiveness of CRECL's contrastive learning on improving CRE performance.
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint
Authors: Aixin Sun
Date: 2022-10-09 00:00:00
Tasks: Diversity, Information Retrieval, Recommendation Systems, Retrieval

Recommendation has become a prominent area of research in the field of Information Retrieval (IR). Evaluation is also a traditional research topic in this community. Motivated by a few counter-intuitive observations reported in recent studies, this perspectives paper takes a fresh look at recommender systems from an evaluation standpoint. Rather than examining metrics like recall, hit rate, or NDCG, or perspectives like novelty and diversity, the key focus here is on how these metrics are calculated when evaluating a recommender algorithm. Specifically, the commonly used train/test data splits and their consequences are re-examined. We begin by examining common data splitting methods, such as random split or leave-one-out, and discuss why the popularity baseline is poorly defined under such splits. We then move on to explore the two implications of neglecting a global timeline during evaluation: data leakage and oversimplification of user preference modeling. Afterwards, we present new perspectives on recommender systems, including techniques for evaluating algorithm performance that more accurately reflect real-world scenarios, and possible approaches to consider decision contexts in user preference modeling.
AgileGAN: stylizing portraits by inversion-consistent transfer learning
Authors: Guoxian Song, Linjie Luo, Jing Liu, Wan-Chun Ma, Chun-Pong Lai, Chuanxia Zheng, Tat-Jen Cham
Date: 2021-07-19 00:00:00
Proceeding: ACM Transactions on Graphics 2021 7
Tasks: Attribute, motion retargeting, Transfer Learning

Portraiture as an art form has evolved from realistic depiction into a plethora of creative styles. While substantial progress has been made in automated stylization, generating high quality stylistic portraits is still a challenge, and even the recent popular Toonify suffers from several artifacts when used on real input images. Such StyleGAN-based methods have focused on finding the best latent inversion mapping for reconstructing input images; however, our key insight is that this does not lead to good generalization to different portrait styles. Hence we propose AgileGAN, a framework that can generate high quality stylistic portraits via inversion-consistent transfer learning. We introduce a novel hierarchical variational autoencoder to ensure the inverse mapped distribution conforms to the original latent Gaussian distribution, while augmenting the original space to a multi-resolution latent space so as to better encode different levels of detail. To better capture attribute-dependent stylization of facial features, we also present an attribute-aware generator and adopt an early stopping strategy to avoid overfitting small training datasets. Our approach provides greater agility in creating high quality and high resolution (1024×1024) portrait stylization models, requiring only a limited number of style exemplars (~100) and short training time (~1 hour). We collected several style datasets for evaluation including 3D cartoons, comics, oil paintings and celebrities. We show that we can achieve superior portrait stylization quality to previous state-of-the-art methods, with comparisons done qualitatively, quantitatively and through a perceptual user study. We also demonstrate two applications of our method, image editing and motion retargeting.
GAFNet: A Global Fourier Self Attention Based Novel Network for multi-modal downstream tasks
Authors: Onkar Susladkar, Gayatri Deshmukh, Dhruv Makwana, Sparsh Mittal, R Sai Chandra Teja, Rekha Singhal
Date: 2023-01-01 00:00:00
Proceeding: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2023 1
Tasks: Image Generation, Image-text Retrieval, Retrieval, Text Retrieval, Visual Grounding

In “vision and language” problems, multimodal inputs are simultaneously processed for combined visual and textual understanding for image-text embedding. In this paper, we discuss the necessity of considering the difference between the feature space and the distribution when performing multimodal learning. We deal with this problem through deep learning and a generative model approach. We introduce a novel network, GAFNet (Global Attention Fourier Net), which learns through large-scale pre-training over
three image-text datasets (COCO, SBU, and CC-3M), for achieving high performance on downstream vision and language tasks. We propose a GAF (Global Attention Fourier) module, which integrates multiple modalities into one latent space. GAF module is independent of the type of modality, and it allows combining shared representations at each stage. Various ways of thinking about the relationships between different modalities directly affect the model’s design. In contrast to previous research, our work considers visual grounding as a pretrainable and transferable quality instead of something that must be trained from scratch. We show that GAFNet is a versatile network that can be used for a wide range of downstream tasks. Experimental results demonstrate that our technique achieves state-of-theart performance on multimodal classification on the CrisisMD dataset and image generation on the COCO dataset. For image-text retrieval, our technique achieves competitive performance.
AI-Lancet: Locating Error-inducing Neurons to Optimize Neural Networks
Authors: Yue Zhao, Hong Zhu, Kai Chen, Shengzhi Zhang
Date: 2021-12-13 00:00:00
Proceeding: ACM SIGSAC Conference on Computer and Communications Security 2021 12

Deep neural network (DNN) has been widely utilized in many areas due to its increasingly high accuracy. However, DNN models could also produce wrong outputs due to internal errors, which may lead to severe security issues. Unlike fixing bugs in traditional computer software, tracing the errors in DNN models and fixing them are much more difficult due to the uninterpretability of DNN. In this paper, we present a novel and systematic approach to trace and fix the errors in deep learning models. In particular, we locate the error-inducing neurons that play a leading role in the erroneous output. With the knowledge of error-inducing neurons, we propose two methods to fix the errors: the neuron-flip and the neuron-fine-tuning. We evaluate our approach using five different training datasets and seven different model architectures. The experimental results demonstrate its efficacy in different application scenarios, including backdoor removal and general defects fixing.
Automatic Classification of Large-Scale Respiratory Sound Dataset Based on Convolutional Neural Network
Authors: Koki Minami, Huimin Lu, Hyoungseop Kim, Shingo Mabu, Yasushi Hirano, Shoji Kido
Date: 2020-01-30 00:00:00
Proceeding: International Conference on Control, Automation and Systems (ICCAS) 2020 1
Tasks: Audio Classification, Classification, Specificity

Auscultation of respiratory sounds is very important for discovering the respiratory disease. However, there is no quantitative evaluation method for the diagnosis of respiratory sounds until now. It is necessary to develop a system to support the diagnosis of respiratory sounds. In addition, there are few studies using dataset suitable for generating realistic classification models that can be used in clinical sites in algorithm development for automatic analysis of respiratory sounds. We describe the development of an algorithm for the automatic classification of the large-scale respiratory sound dataset used in ICBHI 2017 Challenge as containing crackles, containing wheeze, containing both, and normal. Our approach consists of two major components. Firstly, transformation of one-dimensional signals into two- dimensional time-frequency representation images using short-time Fourier transform and continuous wavelet transform. Secondly, classification of transferred images using convolutional neural networks. In this paper, we apply our proposed method to 920 respiratory sound data, and achieve score of 28[%], harmonic score of 81[%], sensitivity of 54[%] and specificity of 42[%].
FormulaNet: A Benchmark Dataset for Mathematical Formula Detection
Authors: Felix M. Schmitt-Koopmann, Elaine M. Huang, Hans-Peter Hutter, Thilo Stadelmann, Alireza Darvishy
Date: 2022-08-29 00:00:00
Proceeding: IEEE Access 2022 8
Tasks: Math

One unsolved sub-task of document analysis is mathematical formula detection (MFD). Research by ourselves and others has shown that existing MFD datasets with inline and display formula labels are small and have insufficient labeling quality. There is therefore an urgent need for datasets with better quality labeling for future research in the MFD field, as they have a high impact on the performance of the models trained on them. We present an advanced labeling pipeline and a new dataset called FormulaNet in this paper. At over 45k pages, we believe that FormulaNet is the largest MFD dataset with inline formula labels. Our experiments demonstrate substantially improved labeling quality for inline and display formulae detection over existing datasets. Additionally, we provide a math formula detection baseline for FormulaNet with an mAP of 0.754. Our dataset is intended to help address the MFD task and may enable the development of new applications, such as making mathematical formulae accessible in PDFs for visually impaired screen reader users.
Research on Smoking Behavior Detection System Based on Deep Learning
Authors: 万里波
Date: 2022-06-01 00:00:00
Proceeding: 2022 2022 6
Tasks: Deep Learning, Face Detection

As we all know,smoking endangers the health of smokers,and the harm of second-hand smoke to the health of people around us can not be ignored;in addition, improper smoking can sometimes cause many safety accidents,such as fire or explosion, and bring huge property losses to the society.Therefore,strengthening the supervision of smoking prohibition in public places has become a growing concern of all sectors of society.Intelligent smoking behavior detection system has become a very urgent demand,and the core of this kind of system is how to design an algorithm to detect smoking behavior quickly and accurately.According to the existing research status of smoking behavior detection,this dissertation proposes a smoking behavior detection method combined with face detection.The method is tested on the self-made smoking behavior data set,and its accuracy is 94.78%,and its detection speed is 26.76FPS, which basically meets the requirements of accuracy and real-time of smoking behavior detection.The main research contents of this dissertation are as follows:
(1)In view of the misjudgment of smoking behavior that may occur in the direct detection of cigarettes,a smoking behavior detection method combining face detection and cigarette detection is proposed.The face detection model and target detection model are used to detect faces and cigarettes respectively,and whether there is smoking behavior is judged according to the position relationship between face detection frame and cigarette detection frame.
(2)Pyramid feature fusion is carried out on the existing face detection model FaceBoxes,which enhances the ability of the model to detect multi-scale faces,and adjusts the structure of rapid digestion convolution layer to retain more face feature information.
(3)Several powerful deep learning target detection algorithms are studied,and their performance on the self-made smoking behavior data set is tested.According to the experimental results,YOLOv4 with excellent performance is selected for subsequent improvement to adapt to the detection of targets such as cigarettes.
(4)In order to improve the detection ability of YOLOv4 for the smaller cigarette target in the image,this dissertation improves the neck of YOLOv4,improves the accuracy of the model,and uses PP-LCNet to replace the original backbone network CSPDarknet53,which speeds up the reasoning speed.
(5)According to the existing web application development technology and the smoking behavior detection method in this dissertation,a simple online smoking behavior detection system is designed,which can realize the interaction between users and the system,and provide the possibility for better applying the smoking behavior detection algorithm to the actual society.
DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning
Authors: Seungjae Lee, Jigang Kim, Inkyu Jang, H. Jin Kim
Date: 2022-10-11 00:00:00
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Hierarchical Reinforcement Learning (HRL) has made notable progress in complex control tasks by leveraging temporal abstraction. However, previous HRL algorithms often suffer from serious data inefficiency as environments get large. The extended components, $i.e.$, goal space and length of episodes, impose a burden on either one or both high-level and low-level policies since both levels share the total horizon of the episode. In this paper, we present a method of Decoupling Horizons Using a Graph in Hierarchical Reinforcement Learning (DHRL) which can alleviate this problem by decoupling the horizons of high-level and low-level policies and bridging the gap between the length of both horizons using a graph. DHRL provides a freely stretchable high-level action interval, which facilitates longer temporal abstraction and faster training in complex tasks. Our method outperforms state-of-the-art HRL algorithms in typical HRL environments. Moreover, DHRL achieves long and complex locomotion and manipulation tasks.
Legal Element-oriented Modeling with Multi-view Contrastive Learning for Legal Case Retrieval
Authors: Zhaowei Wang
Date: 2022-10-11 00:00:00
Tasks: Contrastive Learning, Language Modelling, Retrieval

Legal case retrieval, which aims to retrieve relevant cases given a query case, plays an essential role in the legal system. While recent research efforts improve the performance of traditional ad-hoc retrieval models, legal case retrieval is still challenging since queries are legal cases, which contain hundreds of tokens. Legal cases are much longer and more complicated than keywords queries. Apart from that, the definition of legal relevance is beyond the general definition. In addition to general topical relevance, the relevant cases also involve similar situations and legal elements, which can support the judgment of the current case. In this paper, we propose an interaction-focused network for legal case retrieval with a multi-view contrastive learning objective. The contrastive learning views, including case-view and element-view, aim to overcome the above challenges. The case-view contrastive learning minimizes the hidden space distance between relevant legal case representations produced by a pre-trained language model (PLM) encoder. The element-view builds positive and negative instances by changing legal elements of cases to help the network better compute legal relevance. To achieve this, we employ a legal element knowledge-aware indicator to detect legal elements of cases. We conduct extensive experiments on the benchmark of relevant case retrieval. Evaluation results indicate our proposed method obtains significant improvement over the existing methods.
Frequency-Aware Self-Supervised Monocular Depth Estimation
Authors: Xingyu Chen, Thomas H. Li, Ruonan Zhang, Ge Li
Date: 2022-10-11 00:00:00
Tasks: Depth Estimation, Depth Prediction, Monocular Depth Estimation, Unsupervised Monocular Depth Estimation

We present two versatile methods to generally enhance self-supervised monocular depth estimation (MDE) models. The high generalizability of our methods is achieved by solving the fundamental and ubiquitous problems in photometric loss function. In particular, from the perspective of spatial frequency, we first propose Ambiguity-Masking to suppress the incorrect supervision under photometric loss at specific object boundaries, the cause of which could be traced to pixel-level ambiguity. Second, we present a novel frequency-adaptive Gaussian low-pass filter, designed to robustify the photometric loss in high-frequency regions. We are the first to propose blurring images to improve depth estimators with an interpretable analysis. Both modules are lightweight, adding no parameters and no need to manually change the network structures. Experiments show that our methods provide performance boosts to a large number of existing models, including those who claimed state-of-the-art, while introducing no extra inference computation at all.