pwc-archive/papers-with-abstracts huggingface dataset:
MetaConcept: Learn to Abstract via Concept Graph for Weakly-Supervised Few-Shot Learning
Authors: Baoquan Zhang, Ka-Cheong Leung, Yunming Ye, Xutao Li
Date: 2020-07-05 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Meta-learning has been proved to be an effective framework to address few-shot learning problems. The key challenge is how to minimize the generalization error of base learner across tasks. In this paper, we explore the concept hierarchy knowledge by leveraging concept graph, and take the concept graph as explicit meta-knowledge for the base learner, instead of learning implicit meta-knowledge, so as to boost the classification performance of meta-learning on weakly-supervised few-shot learning problems. To this end, we propose a novel meta-learning framework, called MetaConcept, which learns to abstract concepts via the concept graph. Specifically, we firstly propose a novel regularization with multi-level conceptual abstraction to constrain a meta-learner to learn to abstract concepts via the concept graph (i.e. identifying the concepts from low to high levels). Then, we propose a meta concept inference network as the meta-learner for the base learner, aiming to quickly adapt to a novel task by the joint inference of the abstract concepts and a few annotated samples. We have conducted extensive experiments on two weakly-supervised few-shot learning benchmarks, namely, WS-ImageNet-Pure and WS-ImageNet-Mix. Our experimental results show that 1) the proposed MetaConcept outperforms state-of-the-art methods with an improvement of 2% to 6% in classification accuracy; 2) the proposed MetaConcept can be able to yield a good performance though merely training with weakly-labeled data sets.
Finding Symmetry Breaking Order Parameters with Euclidean Neural Networks
Authors: Tess E. Smidt, Mario Geiger, Benjamin Kurt Miller
Date: 2020-07-04 00:00:00

Curie's principle states that "when effects show certain asymmetry, this asymmetry must be found in the causes that gave rise to them". We demonstrate that symmetry equivariant neural networks uphold Curie's principle and can be used to articulate many symmetry-relevant scientific questions into simple optimization problems. We prove these properties mathematically and demonstrate them numerically by training a Euclidean symmetry equivariant neural network to learn symmetry-breaking input to deform a square into a rectangle and to generate octahedra tilting patterns in perovskites.
Counterfactual Data Augmentation using Locally Factored Dynamics
Authors: Silviu Pitis, Elliot Creager, Animesh Garg
Date: 2020-07-06 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: counterfactual, Data Augmentation, General Reinforcement Learning, Multi-Goal Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), valid

Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often sparse, and the dynamics at any given time step can often be decomposed into locally independent causal mechanisms. Such local causal structures can be leveraged to improve the sample efficiency of sequence prediction and off-policy reinforcement learning. We formalize this by introducing local causal models (LCMs), which are induced from a global causal model by conditioning on a subset of the state space. We propose an approach to inferring these structures given an object-oriented state representation, as well as a novel algorithm for Counterfactual Data Augmentation (CoDA). CoDA uses local structures and an experience replay to generate counterfactual experiences that are causally valid in the global model. We find that CoDA significantly improves the performance of RL agents in locally factored tasks, including the batch-constrained and goal-conditioned settings.
Accelerating Nonconvex Learning via Replica Exchange Langevin Diffusion
Authors: Yi Chen, Jinglin Chen, Jing Dong, Jian Peng, Zhaoran Wang
Date: 2020-07-04 00:00:00
Proceeding: ICLR 2019 5

Langevin diffusion is a powerful method for nonconvex optimization, which enables the escape from local minima by injecting noise into the gradient. In particular, the temperature parameter controlling the noise level gives rise to a tradeoff between ``global exploration'' and ``local exploitation'', which correspond to high and low temperatures. To attain the advantages of both regimes, we propose to use replica exchange, which swaps between two Langevin diffusions with different temperatures. We theoretically analyze the acceleration effect of replica exchange from two perspectives: (i) the convergence in \chi^2-divergence, and (ii) the large deviation principle. Such an acceleration effect allows us to faster approach the global minima. Furthermore, by discretizing the replica exchange Langevin diffusion, we obtain a discrete-time algorithm. For such an algorithm, we quantify its discretization error in theory and demonstrate its acceleration effect in practice.
Coronary Heart Disease Diagnosis Based on Improved Ensemble Learning
Authors: Kuntoro Adi Nugroho, Noor Akhmad Setiawan, Teguh Bharata Adji
Date: 2020-07-06 00:00:00
Tasks: Diversity, Ensemble Learning

Accurate diagnosis is required before performing proper treatments for coronary heart disease. Machine learning based approaches have been proposed by many researchers to improve the accuracy of coronary heart disease diagnosis. Ensemble learning and cascade generalization are among the methods which can be used to improve the generalization ability of learning algorithm. The objective of this study is to develop heart disease diagnosis method based on ensemble learning and cascade generalization. Cascade generalization method with loose coupling strategy is proposed in this study. C4. 5 and RIPPER algorithm were used as meta-level algorithm and Naive Bayes was used as baselevel algorithm. Bagging and Random Subspace were evaluated for constructing the ensemble. The hybrid cascade ensemble methods are compared with the learning algorithms in non-ensemble mode and non-cascade mode. The methods are also compared with Rotation Forest. Based on the evaluation result, the hybrid cascade ensemble method demonstrated the best result for the given heart disease diagnosis case. Accuracy and diversity evaluation was performed to analyze the impact of the cascade strategy. Based on the result, the accuracy of the classifiers in the ensemble is increased but the diversity is decreased.
Exploration of Optimized Semantic Segmentation Architectures for edge-Deployment on Drones
Authors: Vivek Parmar, Narayani Bhatia, Shubham Negi, Manan Suri
Date: 2020-07-06 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this paper, we present an analysis on the impact of network parameters for semantic segmentation architectures in context of UAV data processing. We present the analysis on the DroneDeploy Segmentation benchmark. Based on the comparative analysis we identify the optimal network architecture to be FPN-EfficientNetB3 with pretrained encoder backbones based on Imagenet Dataset. The network achieves IoU score of 0.65 and F1-score of 0.71 over the validation dataset. We also compare the various architectures in terms of their memory footprint and inference latency with further exploration of the impact of TensorRT based optimizations. We achieve memory savings of ~4.1x and latency improvement of 10% compared to Model: FPN and Backbone: InceptionResnetV2.
Intelligent Reflecting Surface Aided Wireless Communications: A Tutorial
Authors: Qingqing Wu, Shuowen Zhang, Beixiong Zheng, Changsheng You, Rui Zhang
Date: 2020-07-06 00:00:00

Intelligent reflecting surface (IRS) is an enabling technology to engineer the radio signal prorogation in wireless networks. By smartly tuning the signal reflection via a large number of low-cost passive reflecting elements, IRS is capable of dynamically altering wireless channels to enhance the communication performance. It is thus expected that the new IRS-aided hybrid wireless network comprising both active and passive components will be highly promising to achieve a sustainable capacity growth cost-effectively in the future. Despite its great potential, IRS faces new challenges to be efficiently integrated into wireless networks, such as reflection optimization, channel estimation, and deployment from communication design perspectives. In this paper, we provide a tutorial overview of IRS-aided wireless communication to address the above issues, and elaborate its reflection and channel models, hardware architecture and practical constraints, as well as various appealing applications in wireless networks. Moreover, we highlight important directions worthy of further investigation in future work.
Participation is not a Design Fix for Machine Learning
Authors: Mona Sloane, Emanuel Moss, Olaitan Awomolo, Laura Forlano
Date: 2020-07-05 00:00:00
Tasks: BIG-bench Machine Learning

This paper critically examines existing modes of participation in design practice and machine learning. Cautioning against 'participation-washing', it suggests that the ML community must become attuned to possibly exploitative and extractive forms of community involvement and shift away from the prerogatives of context-independent scalability.
Accelerated MRI with Un-trained Neural Networks
Authors: Mohammad Zalbagi Darestani, Reinhard Heckel
Date: 2020-07-06 00:00:00
Tasks: compressed sensing, Decoder, Denoising, Image Denoising, Image Inpainting, Image Reconstruction, MRI Reconstruction

Convolutional Neural Networks (CNNs) are highly effective for image reconstruction problems. Typically, CNNs are trained on large amounts of training images. Recently, however, un-trained CNNs such as the Deep Image Prior and Deep Decoder have achieved excellent performance for image reconstruction problems such as denoising and inpainting, \emph{without using any training data}. Motivated by this development, we address the reconstruction problem arising in accelerated MRI with un-trained neural networks. We propose a highly optimized un-trained recovery approach based on a variation of the Deep Decoder and show that it significantly outperforms other un-trained methods, in particular sparsity-based classical compressed sensing methods and naive applications of un-trained neural networks. We also compare performance (both in terms of reconstruction accuracy and computational cost) in an ideal setup for trained methods, specifically on the fastMRI dataset, where the training and test data come from the same distribution. We find that our un-trained algorithm achieves similar performance to a baseline trained neural network, but a state-of-the-art trained network outperforms the un-trained one. Finally, we perform a comparison on a non-ideal setup where the train and test distributions are slightly different, and find that our un-trained method achieves similar performance to a state-of-the-art accelerated MRI reconstruction method.
Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
Authors: Meng Zhou, Ziyu Liu, Pengwei Sui, Yixuan Li, Yuk Ying Chung
Date: 2020-07-06 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft II

We present a multi-agent actor-critic method that aims to implicitly address the credit assignment problem under fully cooperative settings. Our key motivation is that credit assignment among agents may not require an explicit formulation as long as (1) the policy gradients derived from a centralized critic carry sufficient information for the decentralized agents to maximize their joint action value through optimal cooperation and (2) a sustained level of exploration is enforced throughout training. Under the centralized training with decentralized execution (CTDE) paradigm, we achieve the former by formulating the centralized critic as a hypernetwork such that a latent state representation is integrated into the policy gradients through its multiplicative association with the stochastic policies; to achieve the latter, we derive a simple technique called adaptive entropy regularization where magnitudes of the entropy gradients are dynamically rescaled based on the current policy stochasticity to encourage consistent levels of exploration. Our algorithm, referred to as LICA, is evaluated on several benchmarks including the multi-agent particle environments and a set of challenging StarCraft II micromanagement tasks, and we show that LICA significantly outperforms previous methods.
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Authors: Hamad Zogan, Imran Razzak, Xianzhi Wang, Shoaib Jameel, Guandong Xu
Date: 2020-07-03 00:00:00
Tasks: Depression Detection, Prediction, Word Embeddings

Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning methods provide no interpretability for depression prediction, hence their predictions are obscure to human. In this work, we propose interpretive Multi-Modal Depression Detection with Hierarchical Attention Network MDHAN, for detection depressed users on social media and explain the model prediction. We have considered user posts along with Twitter-based multi-modal features, specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words' importance, and capture semantic sequence features from the user timelines (posts). Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.
News Sentiment Analysis
Authors: Antony Samuels, John Mcgonical
Date: 2020-07-05 00:00:00
Tasks: Articles, Sentiment Analysis

Modern technological era has reshaped traditional lifestyle in several domains. The medium of publishing news and events has become faster with the advancement of Information Technology. IT has also been flooded with immense amounts of data, which is being published every minute of every day, by millions of users, in the shape of comments, blogs, news sharing through blogs, social media micro-blogging websites and many more. Manual traversal of such huge data is a challenging job, thus, sophisticated methods are acquired to perform this task automatically and efficiently. News reports events that comprise of emotions - good, bad, neutral. Sentiment analysis is utilized to investigate human emotions present in textual information. This paper presents a lexicon-based approach for sentiment analysis of news articles. The experiments have been performed on BBC news data set, which expresses the applicability and validation of the adopted approach.
Sentiment Analysis on Customer Responses
Authors: Antony Samuels, John Mcgonical
Date: 2020-07-05 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Sentiment analysis is one of the fastest spreading research areas in computer science, making it challenging to keep track of all the activities in the area. We present a customer feedback reviews on product, where we utilize opinion mining, text mining and sentiments, which has affected the surrounded world by changing their opinion on a specific product. Data used in this study are online product reviews collected from Amazon.com. We performed a comparative sentiment analysis of retrieved reviews. This research paper provides you with sentimental analysis of various smart phone opinions on smart phones dividing them Positive, Negative and Neutral Behaviour.
Sentiment Analysis on Social Media Content
Authors: Antony Samuels, John Mcgonical
Date: 2020-07-04 00:00:00
Tasks: BIG-bench Machine Learning, Sentiment Analysis

Nowadays, people from all around the world use social media sites to share information. Twitter for example is a platform in which users send, read posts known as tweets and interact with different communities. Users share their daily lives, post their opinions on everything such as brands and places. Companies can benefit from this massive platform by collecting data related to opinions on them. The aim of this paper is to present a model that can perform sentiment analysis of real data collected from Twitter. Data in Twitter is highly unstructured which makes it difficult to analyze. However, our proposed model is different from prior work in this field because it combined the use of supervised and unsupervised machine learning algorithms. The process of performing sentiment analysis as follows: Tweet extracted directly from Twitter API, then cleaning and discovery of data performed. After that the data were fed into several models for the purpose of training. Each tweet extracted classified based on its sentiment whether it is a positive, negative or neutral. Data were collected on two subjects McDonalds and KFC to show which restaurant has more popularity. Different machine learning algorithms were used. The result from these models were tested using various testing metrics like cross validation and f-score. Moreover, our model demonstrates strong performance on mining texts extracted directly from Twitter.
FracBits: Mixed Precision Quantization via Fractional Bit-Widths
Authors: Linjie Yang, Qing Jin
Date: 2020-07-04 00:00:00
Tasks: Quantization

Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource constraints can be met during the quantization-aware training which results in an optimized mixed precision model. Further, our method can be naturally combined with channel pruning for better computation cost allocation. Our final models achieve comparable or better performance than previous quantization methods with mixed precision on MobilenetV1/V2, ResNet18 under different resource constraints on ImageNet dataset.
Deep Learning Models for Visual Inspection on Automotive Assembling Line
Authors: Muriel Mazzetto, Marcelo Teixeira, Érick Oliveira Rodrigues, Dalcimar Casanova
Date: 2020-07-02 00:00:00
Tasks: Anomaly Detection, Deep Learning, object-detection, Object Detection, Semantic Segmentation

Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection, etc, which guarantee product and process quality. These tasks can be related to more than one type of vehicle that is produced within the same manufacturing line. Visual inspection was essentially human-led but has recently been supplemented by the artificial perception provided by computer vision systems (CVSs). Despite their relevance, the accuracy of CVSs varies accordingly to environmental settings such as lighting, enclosure and quality of image acquisition. These issues entail costly solutions and override part of the benefits introduced by computer vision systems, mainly when it interferes with the operating cycle time of the factory. In this sense, this paper proposes the use of deep learning-based methodologies to assist in visual inspection tasks while leaving very little footprints in the manufacturing environment and exploring it as an end-to-end tool to ease CVSs setup. The proposed approach is illustrated by four proofs of concept in a real automotive assembly line based on models for object detection, semantic segmentation, and anomaly detection.
Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity
Authors: Gonçalo M. Correia, Vlad Niculae, Wilker Aziz, André F. T. Martins
Date: 2020-07-03 00:00:00
Proceeding: NeurIPS 2020 12

Training neural network models with discrete (categorical or structured) latent variables can be computationally challenging, due to the need for marginalization over large or combinatorial sets. To circumvent this issue, one typically resorts to sampling-based approximations of the true marginal, requiring noisy gradient estimators (e.g., score function estimator) or continuous relaxations with lower-variance reparameterized gradients (e.g., Gumbel-Softmax). In this paper, we propose a new training strategy which replaces these estimators by an exact yet efficient marginalization. To achieve this, we parameterize discrete distributions over latent assignments using differentiable sparse mappings: sparsemax and its structured counterparts. In effect, the support of these distributions is greatly reduced, which enables efficient marginalization. We report successful results in three tasks covering a range of latent variable modeling applications: a semisupervised deep generative model, a latent communication game, and a generative model with a bit-vector latent representation. In all cases, we obtain good performance while still achieving the practicality of sampling-based approximations.
An Automated and Robust Image Watermarking Scheme Based on Deep Neural Networks
Authors: Xin Zhong, Pei-Chi Huang, Spyridon Mastorakis, Frank Y. Shih
Date: 2020-07-05 00:00:00
Tasks: Deep Learning

Digital image watermarking is the process of embedding and extracting a watermark covertly on a cover-image. To dynamically adapt image watermarking algorithms, deep learning-based image watermarking schemes have attracted increased attention during recent years. However, existing deep learning-based watermarking methods neither fully apply the fitting ability to learn and automate the embedding and extracting algorithms, nor achieve the properties of robustness and blindness simultaneously. In this paper, a robust and blind image watermarking scheme based on deep learning neural networks is proposed. To minimize the requirement of domain knowledge, the fitting ability of deep neural networks is exploited to learn and generalize an automated image watermarking algorithm. A deep learning architecture is specially designed for image watermarking tasks, which will be trained in an unsupervised manner to avoid human intervention and annotation. To facilitate flexible applications, the robustness of the proposed scheme is achieved without requiring any prior knowledge or adversarial examples of possible attacks. A challenging case of watermark extraction from phone camera-captured images demonstrates the robustness and practicality of the proposal. The experiments, evaluation, and application cases confirm the superiority of the proposed scheme.