pwc-archive/papers-with-abstracts huggingface dataset:
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation
Authors: Xiong Lyu, Mike Ludkovski
Date: 2020-03-19 00:00:00

We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the benefit of replication growing as response features are learned, inputs concentrate, and the metamodeling overhead rises. Motivated by the problem of learning the level set of the mean simulator response we develop four novel schemes: Multi-Level Batching (MLB), Ratchet Batching (RB), Adaptive Batched Stepwise Uncertainty Reduction (ABSUR), Adaptive Design with Stepwise Allocation (ADSA) and Deterministic Design with Stepwise Allocation (DDSA). Our algorithms simultaneously (MLB, RB and ABSUR) or sequentially (ADSA and DDSA) determine the sequential design inputs and the respective number of replicates. Illustrations using synthetic examples and an application in quantitative finance (Bermudan option pricing via Regression Monte Carlo) show that adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
Uncertainty Estimation in Cancer Survival Prediction
Authors: Hrushikesh Loya, Pranav Poduval, Deepak Anand, Neeraj Kumar, Amit Sethi
Date: 2020-03-19 00:00:00
Tasks: Prediction, Survival Prediction, Variational Inference

Survival models are used in various fields, such as the development of cancer treatment protocols. Although many statistical and machine learning models have been proposed to achieve accurate survival predictions, little attention has been paid to obtain well-calibrated uncertainty estimates associated with each prediction. The currently popular models are opaque and untrustworthy in that they often express high confidence even on those test cases that are not similar to the training samples, and even when their predictions are wrong. We propose a Bayesian framework for survival models that not only gives more accurate survival predictions but also quantifies the survival uncertainty better. Our approach is a novel combination of variational inference for uncertainty estimation, neural multi-task logistic regression for estimating nonlinear and time-varying risk models, and an additional sparsity-inducing prior to work with high dimensional data.
Bipartite Link Prediction based on Topological Features via 2-hop Path
Authors: Jungwoon Shin
Date: 2020-03-19 00:00:00
Tasks: Clustering, Link Prediction, Node Clustering

A variety of real-world systems can be modeled as bipartite networks. One of the most powerful and simple link prediction methods is Linear-Graph Autoencoder(LGAE) which has promising performance on challenging tasks such as link prediction and node clustering. LGAE relies on simple linear model w.r.t. the adjacency matrix of the graph to learn vector space representations of nodes. In this paper, we consider the case of bipartite link predictions where node attributes are unavailable. When using LGAE, we propose to multiply the reconstructed adjacency matrix with a symmetrically normalized training adjacency matrix. As a result, 2-hop paths are formed which we use as the predicted adjacency matrix to evaluate the performance of our model. Experimental results on both synthetic and real-world dataset show our approach consistently outperforms Graph Autoencoder and Linear Graph Autoencoder model in 10 out of 12 bipartite dataset and reaches competitive performances in 2 other bipartite dataset.
Ensemble learning in CNN augmented with fully connected subnetworks
Authors: Daiki Hirata, Norikazu Takahashi
Date: 2020-03-19 00:00:00
Tasks: Ensemble Learning, Image Classification, Object Recognition

Convolutional Neural Networks (CNNs) have shown remarkable performance in general object recognition tasks. In this paper, we propose a new model called EnsNet which is composed of one base CNN and multiple Fully Connected SubNetworks (FCSNs). In this model, the set of feature-maps generated by the last convolutional layer in the base CNN is divided along channels into disjoint subsets, and these subsets are assigned to the FCSNs. Each of the FCSNs is trained independent of others so that it can predict the class label from the subset of the feature-maps assigned to it. The output of the overall model is determined by majority vote of the base CNN and the FCSNs. Experimental results using the MNIST, Fashion-MNIST and CIFAR-10 datasets show that the proposed approach further improves the performance of CNNs. In particular, an EnsNet achieves a state-of-the-art error rate of 0.16% on MNIST.
XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning
Authors: Sung Whan Yoon, Do-Yeon Kim, Jun Seo, Jaekyun Moon
Date: 2020-03-19 00:00:00
Proceeding: ICML 2020 1
Tasks: Few-Shot Learning, Novel Concepts, TAR

Learning novel concepts while preserving prior knowledge is a long-standing challenge in machine learning. The challenge gets greater when a novel task is given with only a few labeled examples, a problem known as incremental few-shot learning. We propose XtarNet, which learns to extract task-adaptive representation (TAR) for facilitating incremental few-shot learning. The method utilizes a backbone network pretrained on a set of base categories while also employing additional modules that are meta-trained across episodes. Given a new task, the novel feature extracted from the meta-trained modules is mixed with the base feature obtained from the pretrained model. The process of combining two different features provides TAR and is also controlled by meta-trained modules. The TAR contains effective information for classifying both novel and base categories. The base and novel classifiers quickly adapt to a given task by utilizing the TAR. Experiments on standard image datasets indicate that XtarNet achieves state-of-the-art incremental few-shot learning performance. The concept of TAR can also be used in conjunction with existing incremental few-shot learning methods; extensive simulation results in fact show that applying TAR enhances the known methods significantly.
CPR-GCN: Conditional Partial-Residual Graph Convolutional Network in Automated Anatomical Labeling of Coronary Arteries
Authors: Han Yang, Xingjian Zhen, Ying Chi, Lei Zhang, Xian-Sheng Hua
Date: 2020-03-19 00:00:00
Proceeding: CVPR 2020 6
Tasks: Anatomy, Graph Neural Network, Position

Automated anatomical labeling plays a vital role in coronary artery disease diagnosing procedure. The main challenge in this problem is the large individual variability inherited in human anatomy. Existing methods usually rely on the position information and the prior knowledge of the topology of the coronary artery tree, which may lead to unsatisfactory performance when the main branches are confusing. Motivated by the wide application of the graph neural network in structured data, in this paper, we propose a conditional partial-residual graph convolutional network (CPR-GCN), which takes both position and CT image into consideration, since CT image contains abundant information such as branch size and spanning direction. Two majority parts, a Partial-Residual GCN and a conditions extractor, are included in CPR-GCN. The conditions extractor is a hybrid model containing the 3D CNN and the LSTM, which can extract 3D spatial image features along the branches. On the technical side, the Partial-Residual GCN takes the position features of the branches, with the 3D spatial image features as conditions, to predict the label for each branches. While on the mathematical side, our approach twists the partial differential equation (PDE) into the graph modeling. A dataset with 511 subjects is collected from the clinic and annotated by two experts with a two-phase annotation process. According to the five-fold cross-validation, our CPR-GCN yields 95.8% meanRecall, 95.4% meanPrecision and 0.955 meanF1, which outperforms state-of-the-art approaches.
Lifelong Learning with Searchable Extension Units
Authors: Wenjin Wang, Yunqing Hu, Yin Zhang
Date: 2020-03-19 00:00:00
Tasks: Lifelong learning, Neural Architecture Search

Lifelong learning remains an open problem. One of its main difficulties is catastrophic forgetting. Many dynamic expansion approaches have been proposed to address this problem, but they all use homogeneous models of predefined structure for all tasks. The common original model and expansion structures ignore the requirement of different model structures on different tasks, which leads to a less compact model for multiple tasks and causes the model size to increase rapidly as the number of tasks increases. Moreover, they can not perform best on all tasks. To solve those problems, in this paper, we propose a new lifelong learning framework named Searchable Extension Units (SEU) by introducing Neural Architecture Search into lifelong learning, which breaks down the need for a predefined original model and searches for specific extension units for different tasks, without compromising the performance of the model on different tasks. Our approach can obtain a much more compact model without catastrophic forgetting. The experimental results on the PMNIST, the split CIFAR10 dataset, the split CIFAR100 dataset, and the Mixture dataset empirically prove that our method can achieve higher accuracy with much smaller model, whose size is about 25-33 percentage of that of the state-of-the-art methods.
Quality Control of Neuron Reconstruction Based on Deep Learning
Authors: Donghuan Lu, Sujun Zhao, Peng Xie, Kai Ma, Li-Juan Liu, Yefeng Zheng
Date: 2020-03-19 00:00:00
Tasks: Binary Classification, Deep Learning

Neuron reconstruction is essential to generate exquisite neuron connectivity map for understanding brain function. Despite the significant amount of effect that has been made on automatic reconstruction methods, manual tracing by well-trained human annotators is still necessary. To ensure the quality of reconstructed neurons and provide guidance for annotators to improve their efficiency, we propose a deep learning based quality control method for neuron reconstruction in this paper. By formulating the quality control problem into a binary classification task regarding each single point, the proposed approach overcomes the technical difficulties resulting from the large image size and complex neuron morphology. Not only it provides the evaluation of reconstruction quality, but also can locate exactly where the wrong tracing begins. This work presents one of the first comprehensive studies for whole-brain scale quality control of neuron reconstructions. Experiments on five-fold cross validation with a large dataset demonstrate that the proposed approach can detect 74.7% errors with only 1.4% false alerts.
Adjust Planning Strategies to Accommodate Reinforcement Learning Agents
Authors: Xuerun Chen
Date: 2020-03-19 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In agent control issues, the idea of combining reinforcement learning and planning has attracted much attention. Two methods focus on micro and macro action respectively. Their advantages would show together if there is a good cooperation between them. An essential for the cooperation is to find an appropriate boundary, assigning different functions to each method. Such boundary could be represented by parameters in a planning algorithm. In this paper, we create an optimization strategy for planning parameters, through analysis to the connection of reaction and planning; we also create a non-gradient method for accelerating the optimization. The whole algorithm can find a satisfactory setting of planning parameters, making full use of reaction capability of specific agents.
QnAMaker: Data to Bot in 2 Minutes
Authors: Parag Agrawal, Tulasi Menon, Aya Kamel, Michel Naim, Chaikesh Chouragade, Gurvinder Singh, Rohan Kulkarni, Anshuman Suri, Sahithi Katakam, Vineet Pratik, Prakul Bansal, Simerpreet Kaur, Neha Rajput, Anand Duggal, Achraf Chalabi, Prashant Choudhari, Reddy Satti, Niranjan Nayak
Date: 2020-03-19 00:00:00
Tasks: Question Answering

Having a bot for seamless conversations is a much-desired feature that products and services today seek for their websites and mobile apps. These bots help reduce traffic received by human support significantly by handling frequent and directly answerable known questions. Many such services have huge reference documents such as FAQ pages, which makes it hard for users to browse through this data. A conversation layer over such raw data can lower traffic to human support by a great margin. We demonstrate QnAMaker, a service that creates a conversational layer over semi-structured data such as FAQ pages, product manuals, and support documents. QnAMaker is the popular choice for Extraction and Question-Answering as a service and is used by over 15,000 bots in production. It is also used by search interfaces and not just bots.
Detecting Lane and Road Markings at A Distance with Perspective Transformer Layers
Authors: Zhuoping Yu, Xiaozhou Ren, Yuyao Huang, Wei Tian, Junqiao Zhao
Date: 2020-03-19 00:00:00
Tasks: Decoder, Semantic Segmentation

Accurate detection of lane and road markings is a task of great importance for intelligent vehicles. In existing approaches, the detection accuracy often degrades with the increasing distance. This is due to the fact that distant lane and road markings occupy a small number of pixels in the image, and scales of lane and road markings are inconsistent at various distances and perspectives. The Inverse Perspective Mapping (IPM) can be used to eliminate the perspective distortion, but the inherent interpolation can lead to artifacts especially around distant lane and road markings and thus has a negative impact on the accuracy of lane marking detection and segmentation. To solve this problem, we adopt the Encoder-Decoder architecture in Fully Convolutional Networks and leverage the idea of Spatial Transformer Networks to introduce a novel semantic segmentation neural network. This approach decomposes the IPM process into multiple consecutive differentiable homographic transform layers, which are called "Perspective Transformer Layers". Furthermore, the interpolated feature map is refined by subsequent convolutional layers thus reducing the artifacts and improving the accuracy. The effectiveness of the proposed method in lane marking detection is validated on two public datasets: TuSimple and ApolloScape
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention
Authors: Tianyi Zhang, Yun Gu, Xiaolin Huang, Enmei Tu, Jie Yang
Date: 2020-03-19 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

With the popularity of stereo cameras in computer assisted surgery techniques, a second viewpoint would provide additional information in surgery. However, how to effectively access and use stereo information for the super-resolution (SR) purpose is often a challenge. In this paper, we propose a disparity-constrained stereo super-resolution network (DCSSRnet) to simultaneously compute a super-resolved image in a stereo image pair. In particular, we incorporate a disparity-based constraint mechanism into the generation of SR images in a deep neural network framework with an additional atrous parallax-attention modules. Experiment results on laparoscopic images demonstrate that the proposed framework outperforms current SR methods on both quantitative and qualitative evaluations. Our DCSSRnet provides a promising solution on enhancing spatial resolution of stereo image pairs, which will be extremely beneficial for the endoscopic surgery.
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions
Authors: Rui Wang, Joel Lehman, Aditya Rawal, Jiale Zhi, Yulun Li, Jeff Clune, Kenneth O. Stanley
Date: 2020-03-19 00:00:00
Proceeding: ICML 2020 1
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Creating open-ended algorithms, which generate their own never-ending stream of novel and appropriately challenging learning opportunities, could help to automate and accelerate progress in machine learning. A recent step in this direction is the Paired Open-Ended Trailblazer (POET), an algorithm that generates and solves its own challenges, and allows solutions to goal-switch between challenges to avoid local optima. However, the original POET was unable to demonstrate its full creative potential because of limitations of the algorithm itself and because of external issues including a limited problem space and lack of a universal progress measure. Importantly, both limitations pose impediments not only for POET, but for the pursuit of open-endedness in general. Here we introduce and empirically validate two new innovations to the original algorithm, as well as two external innovations designed to help elucidate its full potential. Together, these four advances enable the most open-ended algorithmic demonstration to date. The algorithmic innovations are (1) a domain-general measure of how meaningfully novel new challenges are, enabling the system to potentially create and solve interesting challenges endlessly, and (2) an efficient heuristic for determining when agents should goal-switch from one problem to another (helping open-ended search better scale). Outside the algorithm itself, to enable a more definitive demonstration of open-endedness, we introduce (3) a novel, more flexible way to encode environmental challenges, and (4) a generic measure of the extent to which a system continues to exhibit open-ended innovation. Enhanced POET produces a diverse range of sophisticated behaviors that solve a wide range of environmental challenges, many of which cannot be solved through other means.
Clustering with Fast, Automated and Reproducible assessment applied to longitudinal neural tracking
Authors: Hanlin Zhu, Xue Li, Liuyang Sun, Fei He, Zhengtuo Zhao, Lan Luan, Ngoc Mai Tran, Chong Xie
Date: 2020-03-19 00:00:00
Tasks: Clustering, Entity Resolution, Model Selection, Spike Sorting

Across many areas, from neural tracking to database entity resolution, manual assessment of clusters by human experts presents a bottleneck in rapid development of scalable and specialized clustering methods. To solve this problem we develop C-FAR, a novel method for Fast, Automated and Reproducible assessment of multiple hierarchical clustering algorithms simultaneously. Our algorithm takes any number of hierarchical clustering trees as input, then strategically queries pairs for human feedback, and outputs an optimal clustering among those nominated by these trees. While it is applicable to large dataset in any domain that utilizes pairwise comparisons for assessment, our flagship application is the cluster aggregation step in spike-sorting, the task of assigning waveforms (spikes) in recordings to neurons. On simulated data of 96 neurons under adverse conditions, including drifting and 25\% blackout, our algorithm produces near-perfect tracking relative to the ground truth. Our runtime scales linearly in the number of input trees, making it a competitive computational tool. These results indicate that C-FAR is highly suitable as a model selection and assessment tool in clustering tasks.
Super Low Resolution RF Powered Accelerometers for Alerting on Hospitalized Patient Bed Exits
Authors: Michael Chesser, Asangi Jayatilaka, Renuka Visvanathan, Christophe Fumeaux, Alanson Sample, Damith C. Ranasinghe
Date: 2020-03-19 00:00:00
Tasks: Activity Recognition, TAG

Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometer-based RFID sensor embodiment---ID-Sensor---for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections
Authors: Yi-An Lai, Xuan Zhu, Yi Zhang, Mona Diab
Date: 2020-03-19 00:00:00
Proceeding: LREC 2020 5
Tasks: Descriptive, Diversity, text-classification, Text Classification

Summarizing data samples by quantitative measures has a long history, with descriptive statistics being a case in point. However, as natural language processing methods flourish, there are still insufficient characteristic metrics to describe a collection of texts in terms of the words, sentences, or paragraphs they comprise. In this work, we propose metrics of diversity, density, and homogeneity that quantitatively measure the dispersion, sparsity, and uniformity of a text collection. We conduct a series of simulations to verify that each metric holds desired properties and resonates with human intuitions. Experiments on real-world datasets demonstrate that the proposed characteristic metrics are highly correlated with text classification performance of a renowned model, BERT, which could inspire future applications.
Pose Augmentation: Class-agnostic Object Pose Transformation for Object Recognition
Authors: Yunhao Ge, Jiaping Zhao, Laurent Itti
Date: 2020-03-19 00:00:00
Proceeding: ECCV 2020 8
Tasks: Object, Object Recognition

Object pose increases intraclass object variance which makes object recognition from 2D images harder. To render a classifier robust to pose variations, most deep neural networks try to eliminate the influence of pose by using large datasets with many poses for each class. Here, we propose a different approach: a class-agnostic object pose transformation network (OPT-Net) can transform an image along 3D yaw and pitch axes to synthesize additional poses continuously. Synthesized images lead to better training of an object classifier. We design a novel eliminate-add structure to explicitly disentangle pose from object identity: first eliminate pose information of the input image and then add target pose information (regularized as continuous variables) to synthesize any target pose. We trained OPT-Net on images of toy vehicles shot on a turntable from the iLab-20M dataset. After training on unbalanced discrete poses (5 classes with 6 poses per object instance, plus 5 classes with only 2 poses), we show that OPT-Net can synthesize balanced continuous new poses along yaw and pitch axes with high quality. Training a ResNet-18 classifier with original plus synthesized poses improves mAP accuracy by 9% overtraining on original poses only. Further, the pre-trained OPT-Net can generalize to new object classes, which we demonstrate on both iLab-20M and RGB-D. We also show that the learned features can generalize to ImageNet.