pwc-archive/papers-with-abstracts huggingface dataset:
Learning Complete 3D Morphable Face Models from Images and Videos
Authors: Mallikarjun B R, Ayush Tewari, Hans-Peter Seidel, Mohamed Elgharib, Christian Theobalt
Date: 2020-10-04 00:00:00
Proceeding: CVPR 2021 1
Tasks: 3D Face Reconstruction, Face Reconstruction, Monocular Reconstruction, Self-Supervised Learning

Most 3D face reconstruction methods rely on 3D morphable models, which disentangle the space of facial deformations into identity geometry, expressions and skin reflectance. These models are typically learned from a limited number of 3D scans and thus do not generalize well across different identities and expressions. We present the first approach to learn complete 3D models of face identity geometry, albedo and expression just from images and videos. The virtually endless collection of such data, in combination with our self-supervised learning-based approach allows for learning face models that generalize beyond the span of existing approaches. Our network design and loss functions ensure a disentangled parameterization of not only identity and albedo, but also, for the first time, an expression basis. Our method also allows for in-the-wild monocular reconstruction at test time. We show that our learned models better generalize and lead to higher quality image-based reconstructions than existing approaches.
Optimal Neural Program Synthesis from Multimodal Specifications
Authors: Xi Ye, Qiaochu Chen, Isil Dillig, Greg Durrett
Date: 2020-10-04 00:00:00
Proceeding: Findings (EMNLP) 2021 11
Tasks: Program Synthesis, valid

Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings; however, it requires integrating noisy signals from the user, like natural language, with hard constraints on the program's behavior. This paper proposes an optimal neural synthesis approach where the goal is to find a program that satisfies user-provided constraints while also maximizing the program's score with respect to a neural model. Specifically, we focus on multimodal synthesis tasks in which the user intent is expressed using a combination of natural language (NL) and input-output examples. At the core of our method is a top-down recurrent neural model that places distributions over abstract syntax trees conditioned on the NL input. This model not only allows for efficient search over the space of syntactically valid programs, but it allows us to leverage automated program analysis techniques for pruning the search space based on infeasibility of partial programs with respect to the user's constraints. The experimental results on a multimodal synthesis dataset (StructuredRegex) show that our method substantially outperforms prior state-of-the-art techniques in terms of accuracy and efficiency, and finds model-optimal programs more frequently.
Local Additivity Based Data Augmentation for Semi-supervised NER
Authors: Jiaao Chen, Zhenghui Wang, Ran Tian, Zichao Yang, Diyi Yang
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Data Augmentation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Sentence

Named Entity Recognition (NER) is one of the first stages in deep language understanding yet current NER models heavily rely on human-annotated data. In this work, to alleviate the dependence on labeled data, we propose a Local Additivity based Data Augmentation (LADA) method for semi-supervised NER, in which we create virtual samples by interpolating sequences close to each other. Our approach has two variations: Intra-LADA and Inter-LADA, where Intra-LADA performs interpolations among tokens within one sentence, and Inter-LADA samples different sentences to interpolate. Through linear additions between sampled training data, LADA creates an infinite amount of labeled data and improves both entity and context learning. We further extend LADA to the semi-supervised setting by designing a novel consistency loss for unlabeled data. Experiments conducted on two NER benchmarks demonstrate the effectiveness of our methods over several strong baselines. We have publicly released our code at https://github.com/GT-SALT/LADA.
Explainability via Responsibility
Authors: Faraz Khadivpour, Matthew Guzdial
Date: 2020-10-04 00:00:00
Tasks: AI Agent, BIG-bench Machine Learning, Explainable artificial intelligence

Procedural Content Generation via Machine Learning (PCGML) refers to a group of methods for creating game content (e.g. platformer levels, game maps, etc.) using machine learning models. PCGML approaches rely on black box models, which can be difficult to understand and debug by human designers who do not have expert knowledge about machine learning. This can be even more tricky in co-creative systems where human designers must interact with AI agents to generate game content. In this paper we present an approach to explainable artificial intelligence in which certain training instances are offered to human users as an explanation for the AI agent's actions during a co-creation process. We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.
Spatial Damage Characterization in Self-Sensing Materials via Neural Network-Aided Electrical Impedance Tomography: A Computational Study
Authors: Lang Zhao, Tyler Tallman, Guang Lin
Date: 2020-10-04 00:00:00
Tasks: Structural Health Monitoring

Continuous structural health monitoring (SHM) and integrated nondestructive evaluation (NDE) are important for ensuring the safe operation of high-risk engineering structures. Recently, piezoresistive nanocomposite materials have received much attention for SHM and NDE. These materials are self-sensing because their electrical conductivity changes in response to deformation and damage. Combined with electrical impedance tomography (EIT), it is possible to map deleterious effects. However, EIT suffers from important limitations -- it is computationally expensive, provides indistinct information on damage shape, and can miss multiple damages if they are close together. In this article we apply a novel neural network approach to quantify damage metrics such as size, number, and location from EIT data. This network is trained using a simulation routine calibrated to experimental data for a piezoresistive carbon nanofiber-modified epoxy. Our results show that the network can predict the number of damages with 99.2% accuracy, quantify damage size with respect to the averaged radius at an average of 2.46% error, and quantify damage position with respect to the domain length at an average of 0.89% error. These results are an important first step in translating the combination of self-sensing materials and EIT to real-world SHM and NDE.
Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization
Authors: Jiaao Chen, Diyi Yang
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Abstractive Dialogue Summarization, Articles, Decoder, Text Summarization

Text summarization is one of the most challenging and interesting problems in NLP. Although much attention has been paid to summarizing structured text like news reports or encyclopedia articles, summarizing conversations---an essential part of human-human/machine interaction where most important pieces of information are scattered across various utterances of different speakers---remains relatively under-investigated. This work proposes a multi-view sequence-to-sequence model by first extracting conversational structures of unstructured daily chats from different views to represent conversations and then utilizing a multi-view decoder to incorporate different views to generate dialogue summaries. Experiments on a large-scale dialogue summarization corpus demonstrated that our methods significantly outperformed previous state-of-the-art models via both automatic evaluations and human judgment. We also discussed specific challenges that current approaches faced with this task. We have publicly released our code at https://github.com/GT-SALT/Multi-View-Seq2Seq.
Surface Agnostic Metrics for Cortical Volume Segmentation and Regression
Authors: Samuel Budd, Prachi Patkee, Ana Baburamani, Mary Rutherford, Emma C. Robinson, Bernhard Kainz
Date: 2020-10-04 00:00:00
Tasks: regression

The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.
Diagonal Memory Optimisation for Machine Learning on Micro-controllers
Authors: Peter Blacker, Christopher Paul Bridges, Simon Hadfield
Date: 2020-10-04 00:00:00
Tasks: BIG-bench Machine Learning, CPU, GPU

As machine learning spreads into more and more application areas, micro controllers and low power CPUs are increasingly being used to perform inference with machine learning models. The capability to deploy onto these limited hardware targets is enabling machine learning models to be used across a diverse range of new domains. Optimising the inference process on these targets poses different challenges from either desktop CPU or GPU implementations, where the small amounts of RAM available on these targets sets limits on size of models which can be executed. Analysis of the memory use patterns of eleven machine learning models was performed. Memory load and store patterns were observed using a modified version of the Valgrind debugging tool, identifying memory areas holding values necessary for the calculation as inference progressed. These analyses identified opportunities optimise the memory use of these models by overlapping the input and output buffers of individual tensor operations. Three methods are presented which can calculate the safe overlap of input and output buffers for tensor operations. Ranging from a computationally expensive approach with the ability to operate on compiled layer operations, to a versatile analytical solution which requires access to the original source code of the layer. The diagonal memory optimisation technique is described and shown to achieve memory savings of up to 34.5% when applied to eleven common models. Micro-controller targets are identified where it is only possible to deploy some models if diagonal memory optimisation is used.
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation
Authors: Luyu Gao, Xinyi Wang, Graham Neubig
Date: 2020-10-04 00:00:00
Proceeding: Findings of the Association for Computational Linguistics 2020
Tasks: Decoder, Machine Translation, NMT, Translation

To improve the performance of Neural Machine Translation~(NMT) for low-resource languages~(LRL), one effective strategy is to leverage parallel data from a related high-resource language~(HRL). However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate \emph{into} the LRL, by designing a better decoder word embedding. Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding~\citep{SDE}, we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.
Multi-Modal Retrieval using Graph Neural Networks
Authors: Aashish Kumar Misraa, Ajinkya Kale, Pranav Aggarwal, Ali Aminian
Date: 2020-10-04 00:00:00
Tasks: Image Retrieval, Re-Ranking, Retrieval

Most real world applications of image retrieval such as Adobe Stock, which is a marketplace for stock photography and illustrations, need a way for users to find images which are both visually (i.e. aesthetically) and conceptually (i.e. containing the same salient objects) as a query image. Learning visual-semantic representations from images is a well studied problem for image retrieval. Filtering based on image concepts or attributes is traditionally achieved with index-based filtering (e.g. on textual tags) or by re-ranking after an initial visual embedding based retrieval. In this paper, we learn a joint vision and concept embedding in the same high-dimensional space. This joint model gives the user fine-grained control over the semantics of the result set, allowing them to explore the catalog of images more rapidly. We model the visual and concept relationships as a graph structure, which captures the rich information through node neighborhood. This graph structure helps us learn multi-modal node embeddings using Graph Neural Networks. We also introduce a novel inference time control, based on selective neighborhood connectivity allowing the user control over the retrieval algorithm. We evaluate these multi-modal embeddings quantitatively on the downstream relevance task of image retrieval on MS-COCO dataset and qualitatively on MS-COCO and an Adobe Stock dataset.
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting
Authors: Usman Sajid, Wenchi Ma, Guanghui Wang
Date: 2020-10-04 00:00:00
Tasks: Crowd Counting, regression

Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditions. The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance. However, the PRM module requires an additional and compromising crowd-density classification process. To address these issues and challenges, the paper proposes a new multi-resolution fusion based end-to-end crowd counting network. It employs three deep-layers based columns/branches, each catering the respective crowd-density scale. These columns regularly fuse (share) the information with each other. The network is divided into three phases with each phase containing one or more columns. Three input priors are introduced to serve as an efficient and effective alternative to the PRM module, without requiring any additional classification operations. Along with the final crowd count regression head, the network also contains three auxiliary crowd estimation regression heads, which are strategically placed at each phase end to boost the overall performance. Comprehensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms all the state-of-the-art models under the RMSE evaluation metric. The proposed approach also has better generalization capability with the best results during the cross-dataset experiments.
KiU-Net: Overcomplete Convolutional Architectures for Biomedical Image and Volumetric Segmentation
Authors: Jeya Maria Jose Valanarasu, Vishwanath A. Sindagi, Ilker Hacihaliloglu, Vishal M. Patel
Date: 2020-10-04 00:00:00
Tasks: 3D Medical Imaging Segmentation, Brain Tumor Segmentation, Computed Tomography (CT), Image Segmentation, Liver Segmentation, Medical Image Segmentation, Semantic Segmentation, Ultrasound, Volumetric Medical Image Segmentation

Most methods for medical image segmentation use U-Net or its variants as they have been successful in most of the applications. After a detailed analysis of these "traditional" encoder-decoder based approaches, we observed that they perform poorly in detecting smaller structures and are unable to segment boundary regions precisely. This issue can be attributed to the increase in receptive field size as we go deeper into the encoder. The extra focus on learning high level features causes the U-Net based approaches to learn less information about low-level features which are crucial for detecting small structures. To overcome this issue, we propose using an overcomplete convolutional architecture where we project our input image into a higher dimension such that we constrain the receptive field from increasing in the deep layers of the network. We design a new architecture for image segmentation- KiU-Net which has two branches: (1) an overcomplete convolutional network Kite-Net which learns to capture fine details and accurate edges of the input, and (2) U-Net which learns high level features. Furthermore, we also propose KiU-Net 3D which is a 3D convolutional architecture for volumetric segmentation. We perform a detailed study of KiU-Net by performing experiments on five different datasets covering various image modalities like ultrasound (US), magnetic resonance imaging (MRI), computed tomography (CT), microscopic and fundus images. The proposed method achieves a better performance as compared to all the recent methods with an additional benefit of fewer parameters and faster convergence. Additionally, we also demonstrate that the extensions of KiU-Net based on residual blocks and dense blocks result in further performance improvements. The implementation of KiU-Net can be found here: https://github.com/jeya-maria-jose/KiU-Net-pytorch
Data-efficient Online Classification with Siamese Networks and Active Learning
Authors: Kleanthis Malialis, Christos G. Panayiotou, Marios M. Polycarpou
Date: 2020-10-04 00:00:00
Tasks: Active Learning, Classification, General Classification

An ever increasing volume of data is nowadays becoming available in a streaming manner in many application areas, such as, in critical infrastructure systems, finance and banking, security and crime and web analytics. To meet this new demand, predictive models need to be built online where learning occurs on-the-fly. Online learning poses important challenges that affect the deployment of online classification systems to real-life problems. In this paper we investigate learning from limited labelled, nonstationary and imbalanced data in online classification. We propose a learning method that synergistically combines siamese neural networks and active learning. The proposed method uses a multi-sliding window approach to store data, and maintains separate and balanced queues for each class. Our study shows that the proposed method is robust to data nonstationarity and imbalance, and significantly outperforms baselines and state-of-the-art algorithms in terms of both learning speed and performance. Importantly, it is effective even when only 1% of the labels of the arriving instances are available.
Generating Dialogue Responses from a Semantic Latent Space
Authors: Wei-Jen Ko, Avik Ray, Yilin Shen, Hongxia Jin
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Dialogue Generation, valid

Existing open-domain dialogue generation models are usually trained to mimic the gold response in the training set using cross-entropy loss on the vocabulary. However, a good response does not need to resemble the gold response, since there are multiple possible responses to a given prompt. In this work, we hypothesize that the current models are unable to integrate information from multiple semantically similar valid responses of a prompt, resulting in the generation of generic and uninformative responses. To address this issue, we propose an alternative to the end-to-end classification on vocabulary. We learn the pair relationship between the prompts and responses as a regression task on a latent space instead. In our novel dialog generation model, the representations of semantically related sentences are close to each other on the latent space. Human evaluation showed that learning the task on a continuous space can generate responses that are both relevant and informative.
Inquisitive Question Generation for High Level Text Comprehension
Authors: Wei-Jen Ko, Te-Yuan Chen, Yiyan Huang, Greg Durrett, Junyi Jessy Li
Date: 2020-10-04 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Question Generation, Question-Generation, Reading Comprehension, Vocal Bursts Intensity Prediction

Inquisitive probing questions come naturally to humans in a variety of settings, but is a challenging task for automatic systems. One natural type of question to ask tries to fill a gap in knowledge during text comprehension, like reading a news article: we might ask about background information, deeper reasons behind things occurring, or more. Despite recent progress with data-driven approaches, generating such questions is beyond the range of models trained on existing datasets. We introduce INQUISITIVE, a dataset of ~19K questions that are elicited while a person is reading through a document. Compared to existing datasets, INQUISITIVE questions target more towards high-level (semantic and discourse) comprehension of text. We show that readers engage in a series of pragmatic strategies to seek information. Finally, we evaluate question generation models based on GPT-2 and show that our model is able to generate reasonable questions although the task is challenging, and highlight the importance of context to generate INQUISITIVE questions.
Bayesian Feature Selection in Joint Quantile Time Series Analysis
Authors: Ning Ning
Date: 2020-10-04 00:00:00
Tasks: Dimensionality Reduction, feature selection, parameter estimation, quantile regression, Time Series, Time Series Analysis

Quantile feature selection over correlated multivariate time series data has always been a methodological challenge and is an open problem. In this paper, we propose a general Bayesian dimension reduction methodology for feature selection in high-dimensional joint quantile time series analysis, under the name of the quantile feature selection time series (QFSTS) model. The QFSTS model is a general structural time series model, where each component yields an additive contribution to the time series modeling with direct interpretations. Its flexibility is compound in the sense that users can add/deduct components for each time series and each time series can have its own specific valued components of different sizes. Feature selection is conducted in the quantile regression component, where each time series has its own pool of contemporaneous external predictors allowing nowcasting. Bayesian methodology in extending feature selection to the quantile time series research area is developed using multivariate asymmetric Laplace distribution, spike-and-slab prior setup, the Metropolis-Hastings algorithm, and the Bayesian model averaging technique, all implemented consistently in the Bayesian paradigm. The QFSTS model requires small datasets to train and converges fast. Extensive examinations confirmed that the QFSTS model has superior performance in feature selection, parameter estimation, and forecast.