pwc-archive/papers-with-abstracts huggingface dataset:
SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
Authors: Thibault Formal, Carlos Lassance, Benjamin Piwowarski, Stéphane Clinchant
Date: 2021-09-21 00:00:00
Tasks: Information Retrieval, Retrieval, Zero Shot on BEIR (Inference Free Model), Zero-shot Text Search

In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors methods has proven to work well. Meanwhile, there has been a growing interest in learning \emph{sparse} representations for documents and queries, that could inherit from the desirable properties of bag-of-words models such as the exact matching of terms and the efficiency of inverted indexes. Introduced recently, the SPLADE model provides highly sparse representations and competitive results with respect to state-of-the-art dense and sparse approaches. In this paper, we build on SPLADE and propose several significant improvements in terms of effectiveness and/or efficiency. More specifically, we modify the pooling mechanism, benchmark a model solely based on document expansion, and introduce models trained with distillation. We also report results on the BEIR benchmark. Overall, SPLADE is considerably improved with more than $9$\% gains on NDCG@10 on TREC DL 2019, leading to state-of-the-art results on the BEIR benchmark.
Heterogeneous Ensemble for ESG Ratings Prediction
Authors: Tim Krappel, Alex Bogun, Damian Borth
Date: 2021-09-21 00:00:00
Tasks: Prediction

Over the past years, topics ranging from climate change to human rights have seen increasing importance for investment decisions. Hence, investors (asset managers and asset owners) who wanted to incorporate these issues started to assess companies based on how they handle such topics. For this assessment, investors rely on specialized rating agencies that issue ratings along the environmental, social and governance (ESG) dimensions. Such ratings allow them to make investment decisions in favor of sustainability. However, rating agencies base their analysis on subjective assessment of sustainability reports, not provided by every company. Furthermore, due to human labor involved, rating agencies are currently facing the challenge to scale up the coverage in a timely manner. In order to alleviate these challenges and contribute to the overall goal of supporting sustainability, we propose a heterogeneous ensemble model to predict ESG ratings using fundamental data. This model is based on feedforward neural network, CatBoost and XGBoost ensemble members. Given the public availability of fundamental data, the proposed method would allow cost-efficient and scalable creation of initial ESG ratings (also for companies without sustainability reporting). Using our approach we are able to explain 54% of the variation in ratings R2 using fundamental data and outperform prior work in this area.
Comparison of Neural Network based Soft Computing Techniques for Electromagnetic Modeling of a Microstrip Patch Antenna
Authors: Yuvraj Singh Malhi, Navneet Gupta
Date: 2021-09-21 00:00:00
Tasks: Dielectric Constant

This paper presents the comparison of various neural networks and algorithms based on accuracy, quickness, and consistency for antenna modelling. Using MATLAB Nntool, 22 different combinations of networks and training algorithms are used to predict the dimensions of a rectangular microstrip antenna using dielectric constant, height of substrate, and frequency of oper-ation as input. Comparison and characterization of networks is done based on accuracy, mean square error, and training time. Algorithms, on the other hand, are analyzed by their accuracy, speed, reliability, and smoothness in the training process. Finally, these results are analyzed, and recommendations are made for each neural network and algorithm based on uses, advantages, and disadvantages. For example, it is observed that Reduced Radial Bias network is the most accurate network and Scaled Conjugate Gradient is the most reliable algorithm for electromagnetic modelling. This paper will help a researcher find the optimum network and algorithm directly without doing time-taking experimentation.
LOTR: Face Landmark Localization Using Localization Transformer
Authors: Ukrit Watchareeruetai, Benjaphan Sommana, Sanjana Jain, Pavit Noinongyao, Ankush Ganguly, Aubin Samacoits, Samuel W. F. Earp, Nakarin Sritrakool
Date: 2021-09-21 00:00:00
Tasks: Face Alignment, Face Recognition

This paper presents a novel Transformer-based facial landmark localization network named Localization Transformer (LOTR). The proposed framework is a direct coordinate regression approach leveraging a Transformer network to better utilize the spatial information in the feature map. An LOTR model consists of three main modules: 1) a visual backbone that converts an input image into a feature map, 2) a Transformer module that improves the feature representation from the visual backbone, and 3) a landmark prediction head that directly predicts the landmark coordinates from the Transformer's representation. Given cropped-and-aligned face images, the proposed LOTR can be trained end-to-end without requiring any post-processing steps. This paper also introduces the smooth-Wing loss function, which addresses the gradient discontinuity of the Wing loss, leading to better convergence than standard loss functions such as L1, L2, and Wing loss. Experimental results on the JD landmark dataset provided by the First Grand Challenge of 106-Point Facial Landmark Localization indicate the superiority of LOTR over the existing methods on the leaderboard and two recent heatmap-based approaches. On the WFLW dataset, the proposed LOTR framework demonstrates promising results compared with several state-of-the-art methods. Additionally, we report the improvement in state-of-the-art face recognition performance when using our proposed LOTRs for face alignment.
Search For Deep Graph Neural Networks
Authors: Guosheng Feng, Chunnan Wang, Hongzhi Wang
Date: 2021-09-21 00:00:00
Tasks: Diversity, Q-Learning

Current GNN-oriented NAS methods focus on the search for different layer aggregate components with shallow and simple architectures, which are limited by the 'over-smooth' problem. To further explore the benefits from structural diversity and depth of GNN architectures, we propose a GNN generation pipeline with a novel two-stage search space, which aims at automatically generating high-performance while transferable deep GNN models in a block-wise manner. Meanwhile, to alleviate the 'over-smooth' problem, we incorporate multiple flexible residual connection in our search space and apply identity mapping in the basic GNN layers. For the search algorithm, we use deep-q-learning with epsilon-greedy exploration strategy and reward reshaping. Extensive experiments on real-world datasets show that our generated GNN models outperforms existing manually designed and NAS-based ones.
Learning offline: memory replay in biological and artificial reinforcement learning
Authors: Emma L. Roscow, Raymond Chua, Rui Ponte Costa, Matt W. Jones, Nathan Lepora
Date: 2021-09-21 00:00:00
Tasks: Continual Learning, Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning to act in an environment to maximise rewards is among the brain's key functions. This process has often been conceptualised within the framework of reinforcement learning, which has also gained prominence in machine learning and artificial intelligence (AI) as a way to optimise decision-making. A common aspect of both biological and machine reinforcement learning is the reactivation of previously experienced episodes, referred to as replay. Replay is important for memory consolidation in biological neural networks, and is key to stabilising learning in deep neural networks. Here, we review recent developments concerning the functional roles of replay in the fields of neuroscience and AI. Complementary progress suggests how replay might support learning processes, including generalisation and continual learning, affording opportunities to transfer knowledge across the two fields to advance the understanding of biological and artificial learning and memory.
Online Multi-horizon Transaction Metric Estimation with Multi-modal Learning in Payment Networks
Authors: Chin-Chia Michael Yeh, Zhongfang Zhuang, Junpeng Wang, Yan Zheng, Javid Ebrahimi, Ryan Mercer, Liang Wang, Wei zhang
Date: 2021-09-21 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Predicting metrics associated with entities' transnational behavior within payment processing networks is essential for system monitoring. Multivariate time series, aggregated from the past transaction history, can provide valuable insights for such prediction. The general multivariate time series prediction problem has been well studied and applied across several domains, including manufacturing, medical, and entomology. However, new domain-related challenges associated with the data such as concept drift and multi-modality have surfaced in addition to the real-time requirements of handling the payment transaction data at scale. In this work, we study the problem of multivariate time series prediction for estimating transaction metrics associated with entities in the payment transaction database. We propose a model with five unique components to estimate the transaction metrics from multi-modality data. Four of these components capture interaction, temporal, scale, and shape perspectives, and the fifth component fuses these perspectives together. We also propose a hybrid offline/online training scheme to address concept drift in the data and fulfill the real-time requirements. Combining the estimation model with a graphical user interface, the prototype transaction metric estimation system has demonstrated its potential benefit as a tool for improving a payment processing company's system monitoring capability.
CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval
Authors: Zhijian Hou, Chong-Wah Ngo, Wing Kwong Chan
Date: 2021-09-21 00:00:00
Tasks: Corpus Video Moment Retrieval, Moment Retrieval, orpus Video Moment Retrieval, Representation Learning, Retrieval, rpus Video Moment Retrieval, Video Corpus Moment Retrieval, Video Retrieval

This paper tackles a recently proposed Video Corpus Moment Retrieval task. This task is essential because advanced video retrieval applications should enable users to retrieve a precise moment from a large video corpus. We propose a novel CONtextual QUery-awarE Ranking~(CONQUER) model for effective moment localization and ranking. CONQUER explores query context for multi-modal fusion and representation learning in two different steps. The first step derives fusion weights for the adaptive combination of multi-modal video content. The second step performs bi-directional attention to tightly couple video and query as a single joint representation for moment localization. As query context is fully engaged in video representation learning, from feature fusion to transformation, the resulting feature is user-centered and has a larger capacity in capturing multi-modal signals specific to query. We conduct studies on two datasets, TVR for closed-world TV episodes and DiDeMo for open-world user-generated videos, to investigate the potential advantages of fusing video and query online as a joint representation for moment retrieval.
Unsupervised Abstract Reasoning for Raven's Problem Matrices
Authors: Tao Zhuo, Qiang Huang, Mohan Kankanhalli
Date: 2021-09-21 00:00:00

Raven's Progressive Matrices (RPM) is highly correlated with human intelligence, and it has been widely used to measure the abstract reasoning ability of humans. In this paper, to study the abstract reasoning capability of deep neural networks, we propose the first unsupervised learning method for solving RPM problems. Since the ground truth labels are not allowed, we design a pseudo target based on the prior constraints of the RPM formulation to approximate the ground truth label, which effectively converts the unsupervised learning strategy into a supervised one. However, the correct answer is wrongly labelled by the pseudo target, and thus the noisy contrast will lead to inaccurate model training. To alleviate this issue, we propose to improve the model performance with negative answers. Moreover, we develop a decentralization method to adapt the feature representation to different RPM problems. Extensive experiments on three datasets demonstrate that our method even outperforms some of the supervised approaches. Our code is available at https://github.com/visiontao/ncd.
Generating Local Maps of Science using Deep Bibliographic Coupling
Authors: Gaëlle Candel, David Naccache
Date: 2021-09-21 00:00:00

Bibliographic and co-citation coupling are two analytical methods widely used to measure the degree of similarity between scientific papers. These approaches are intuitive, easy to put into practice, and computationally cheap. Moreover, they have been used to generate a map of science, allowing visualizing research field interactions. Nonetheless, these methods do not work unless two papers share a standard reference, limiting the two papers usability with no direct connection. In this work, we propose to extend bibliographic coupling to the deep neighborhood, by using graph diffusion methods. This method allows defining similarity between any two papers, making it possible to generate a local map of science, highlighting field organization.
Fast nonlinear risk assessment for autonomous vehicles using learned conditional probabilistic models of agent futures
Authors: Ashkan Jasour, Xin Huang, Allen Wang, Brian C. Williams
Date: 2021-09-21 00:00:00
Tasks: Autonomous Vehicles, Position

This paper presents fast non-sampling based methods to assess the risk for trajectories of autonomous vehicles when probabilistic predictions of other agents' futures are generated by deep neural networks (DNNs). The presented methods address a wide range of representations for uncertain predictions including both Gaussian and non-Gaussian mixture models to predict both agent positions and control inputs conditioned on the scene contexts. We show that the problem of risk assessment when Gaussian mixture models (GMMs) of agent positions are learned can be solved rapidly to arbitrary levels of accuracy with existing numerical methods. To address the problem of risk assessment for non-Gaussian mixture models of agent position, we propose finding upper bounds on risk using nonlinear Chebyshev's Inequality and sums-of-squares (SOS) programming; they are both of interest as the former is much faster while the latter can be arbitrarily tight. These approaches only require higher order statistical moments of agent positions to determine upper bounds on risk. To perform risk assessment when models are learned for agent control inputs as opposed to positions, we propagate the moments of uncertain control inputs through the nonlinear motion dynamics to obtain the exact moments of uncertain position over the planning horizon. To this end, we construct deterministic linear dynamical systems that govern the exact time evolution of the moments of uncertain position in the presence of uncertain control inputs. The presented methods are demonstrated on realistic predictions from DNNs trained on the Argoverse and CARLA datasets and are shown to be effective for rapidly assessing the probability of low probability events.
Generalization in Text-based Games via Hierarchical Reinforcement Learning
Authors: Yunqiu Xu, Meng Fang, Ling Chen, Yali Du, Chengqi Zhang
Date: 2021-09-21 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Deep Reinforcement Learning, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-based games

Deep reinforcement learning provides a promising approach for text-based games in studying natural language communication between humans and artificial agents. However, the generalization still remains a big challenge as the agents depend critically on the complexity and variety of training tasks. In this paper, we address this problem by introducing a hierarchical framework built upon the knowledge graph-based RL agent. In the high level, a meta-policy is executed to decompose the whole game into a set of subtasks specified by textual goals, and select one of them based on the KG. Then a sub-policy in the low level is executed to conduct goal-conditioned reinforcement learning. We carry out experiments on games with various difficulty levels and show that the proposed method enjoys favorable generalizability.
Mutual Consistency Learning for Semi-supervised Medical Image Segmentation
Authors: Yicheng Wu, ZongYuan Ge, Donghao Zhang, Minfeng Xu, Lei Zhang, Yong Xia, Jianfei Cai
Date: 2021-09-21 00:00:00
Tasks: Image Segmentation, Medical Image Segmentation, Segmentation, Semantic Segmentation, Semi-supervised Medical Image Segmentation

In this paper, we propose a novel mutual consistency network (MC-Net+) to effectively exploit the unlabeled data for semi-supervised medical image segmentation. The MC-Net+ model is motivated by the observation that deep models trained with limited annotations are prone to output highly uncertain and easily mis-classified predictions in the ambiguous regions (e.g., adhesive edges or thin branches) for medical image segmentation. Leveraging these challenging samples can make the semi-supervised segmentation model training more effective. Therefore, our proposed MC-Net+ model consists of two new designs. First, the model contains one shared encoder and multiple slightly different decoders (i.e., using different up-sampling strategies). The statistical discrepancy of multiple decoders' outputs is computed to denote the model's uncertainty, which indicates the unlabeled hard regions. Second, we apply a novel mutual consistency constraint between one decoder's probability output and other decoders' soft pseudo labels. In this way, we minimize the discrepancy of multiple outputs (i.e., the model uncertainty) during training and force the model to generate invariant results in such challenging regions, aiming at regularizing the model training. We compared the segmentation results of our MC-Net+ model with five state-of-the-art semi-supervised approaches on three public medical datasets. Extension experiments with two standard semi-supervised settings demonstrate the superior performance of our model over other methods, which sets a new state of the art for semi-supervised medical image segmentation. Our code is released publicly at https://github.com/ycwu1997/MC-Net.
Identifying biases in legal data: An algorithmic fairness perspective
Authors: Jackson Sargent, Melanie Weber
Date: 2021-09-21 00:00:00
Tasks: Fairness, regression

The need to address representation biases and sentencing disparities in legal case data has long been recognized. Here, we study the problem of identifying and measuring biases in large-scale legal case data from an algorithmic fairness perspective. Our approach utilizes two regression models: A baseline that represents the decisions of a "typical" judge as given by the data and a "fair" judge that applies one of three fairness concepts. Comparing the decisions of the "typical" judge and the "fair" judge allows for quantifying biases across demographic groups, as we demonstrate in four case studies on criminal data from Cook County (Illinois).
Audio Interval Retrieval using Convolutional Neural Networks
Authors: Ievgeniia Kuzminykh, Dan Shevchuk, Stavros Shiaeles, Bogdan Ghita
Date: 2021-09-21 00:00:00
Tasks: Audio Classification, Retrieval, Speech-to-Text

Modern streaming services are increasingly labeling videos based on their visual or audio content. This typically augments the use of technologies such as AI and ML by allowing to use natural speech for searching by keywords and video descriptions. Prior research has successfully provided a number of solutions for speech to text, in the case of a human speech, but this article aims to investigate possible solutions to retrieve sound events based on a natural language query, and estimate how effective and accurate they are. In this study, we specifically focus on the YamNet, AlexNet, and ResNet-50 pre-trained models to automatically classify audio samples using their respective melspectrograms into a number of predefined classes. The predefined classes can represent sounds associated with actions within a video fragment. Two tests are conducted to evaluate the performance of the models on two separate problems: audio classification and intervals retrieval based on a natural language query. Results show that the benchmarked models are comparable in terms of performance, with YamNet slightly outperforming the other two models. YamNet was able to classify single fixed-size audio samples with 92.7% accuracy and 68.75% precision while its average accuracy on intervals retrieval was 71.62% and precision was 41.95%. The investigated method may be embedded into an automated event marking architecture for streaming services.
Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems
Authors: Subbarao Kambhampati, Sarath Sreedharan, Mudit Verma, Yantian Zha, Lin Guan
Date: 2021-09-21 00:00:00

Despite the surprising power of many modern AI systems that often learn their own representations, there is significant discontent about their inscrutability and the attendant problems in their ability to interact with humans. While alternatives such as neuro-symbolic approaches have been proposed, there is a lack of consensus on what they are about. There are often two independent motivations (i) symbols as a lingua franca for human-AI interaction and (ii) symbols as system-produced abstractions used by the AI system in its internal reasoning. The jury is still out on whether AI systems will need to use symbols in their internal reasoning to achieve general intelligence capabilities. Whatever the answer there is, the need for (human-understandable) symbols in human-AI interaction seems quite compelling. Symbols, like emotions, may well not be sine qua non for intelligence per se, but they will be crucial for AI systems to interact with us humans -- as we can neither turn off our emotions nor get by without our symbols. In particular, in many human-designed domains, humans would be interested in providing explicit (symbolic) knowledge and advice -- and expect machine explanations in kind. This alone requires AI systems to to maintain a symbolic interface for interaction with humans. In this blue sky paper, we argue this point of view, and discuss research directions that need to be pursued to allow for this type of human-AI interaction.