pwc-archive/papers-with-abstracts huggingface dataset:
PointGroup: Dual-Set Point Grouping for 3D Instance Segmentation
Authors: Li Jiang, Hengshuang Zhao, Shaoshuai Shi, Shu Liu, Chi-Wing Fu, Jiaya Jia
Date: 2020-04-03 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Instance Segmentation, Clustering, Instance Segmentation, Panoptic Segmentation, Scene Understanding, Segmentation, Semantic Segmentation

Instance segmentation is an important task for scene understanding. Compared to the fully-developed 2D, 3D instance segmentation for point clouds have much room to improve. In this paper, we present PointGroup, a new end-to-end bottom-up architecture, specifically focused on better grouping the points by exploring the void space between objects. We design a two-branch network to extract point features and predict semantic labels and offsets, for shifting each point towards its respective instance centroid. A clustering component is followed to utilize both the original and offset-shifted point coordinate sets, taking advantage of their complementary strength. Further, we formulate the ScoreNet to evaluate the candidate instances, followed by the Non-Maximum Suppression (NMS) to remove duplicates. We conduct extensive experiments on two challenging datasets, ScanNet v2 and S3DIS, on which our method achieves the highest performance, 63.6% and 64.0%, compared to 54.9% and 54.4% achieved by former best solutions in terms of mAP with IoU threshold 0.5.
Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware
Authors: Christoph Ostrau, Jonas Homburg, Christian Klarhorst, Michael Thies, Ulrich Rückert
Date: 2020-04-03 00:00:00
Tasks: Benchmarking, CPU, GPU, Neural Architecture Search

With more and more event-based neuromorphic hardware systems being developed at universities and in industry, there is a growing need for assessing their performance with domain specific measures. In this work, we use the methodology of converting pre-trained non-spiking to spiking neural networks to evaluate the performance loss and measure the energy-per-inference for three neuromorphic hardware systems (BrainScaleS, Spikey, SpiNNaker) and common simulation frameworks for CPU (NEST) and CPU/GPU (GeNN). For analog hardware we further apply a re-training technique known as hardware-in-the-loop training to cope with device mismatch. This analysis is performed for five different networks, including three networks that have been found by an automated optimization with a neural architecture search framework. We demonstrate that the conversion loss is usually below one percent for digital implementations, and moderately higher for analog systems with the benefit of much lower energy-per-inference costs.
Aligned Cross Entropy for Non-Autoregressive Machine Translation
Authors: Marjan Ghazvininejad, Vladimir Karpukhin, Luke Zettlemoyer, Omer Levy
Date: 2020-04-03 00:00:00
Proceeding: ICML 2020 1
Tasks: Machine Translation, Translation

Non-autoregressive machine translation models significantly speed up decoding by allowing for parallel prediction of the entire target sequence. However, modeling word order is more challenging due to the lack of autoregressive factors in the model. This difficultly is compounded during training with cross entropy loss, which can highly penalize small shifts in word order. In this paper, we propose aligned cross entropy (AXE) as an alternative loss function for training of non-autoregressive models. AXE uses a differentiable dynamic program to assign loss based on the best possible monotonic alignment between target tokens and model predictions. AXE-based training of conditional masked language models (CMLMs) substantially improves performance on major WMT benchmarks, while setting a new state of the art for non-autoregressive models.
Orthogonal Inductive Matrix Completion
Authors: Antoine Ledent, Rodrigo Alves, Marius Kloft
Date: 2020-04-03 00:00:00
Tasks: Matrix Completion

We propose orthogonal inductive matrix completion (OMIC), an interpretable approach to matrix completion based on a sum of multiple orthonormal side information terms, together with nuclear-norm regularization. The approach allows us to inject prior knowledge about the singular vectors of the ground truth matrix. We optimize the approach by a provably converging algorithm, which optimizes all components of the model simultaneously. We study the generalization capabilities of our method in both the distribution-free setting and in the case where the sampling distribution admits uniform marginals, yielding learning guarantees that improve with the quality of the injected knowledge in both cases. As particular cases of our framework, we present models which can incorporate user and item biases or community information in a joint and additive fashion. We analyse the performance of OMIC on several synthetic and real datasets. On synthetic datasets with a sliding scale of user bias relevance, we show that OMIC better adapts to different regimes than other methods. On real-life datasets containing user/items recommendations and relevant side information, we find that OMIC surpasses the state-of-the-art, with the added benefit of greater interpretability.
Deep Learning based detection of Acute Aortic Syndrome in contrast CT images
Authors: Manikanta Srikar Yellapragada, Yiting Xie, Benedikt Graf, David Richmond, Arun Krishnan, Arkadiusz Sitek
Date: 2020-04-03 00:00:00
Tasks: Computed Tomography (CT), Multiple Instance Learning

Acute aortic syndrome (AAS) is a group of life threatening conditions of the aorta. We have developed an end-to-end automatic approach to detect AAS in computed tomography (CT) images. Our approach consists of two steps. At first, we extract N cross sections along the segmented aorta centerline for each CT scan. These cross sections are stacked together to form a new volume which is then classified using two different classifiers, a 3D convolutional neural network (3D CNN) and a multiple instance learning (MIL). We trained, validated, and compared two models on 2291 contrast CT volumes. We tested on a set aside cohort of 230 normal and 50 positive CT volumes. Our models detected AAS with an Area under Receiver Operating Characteristic curve (AUC) of 0.965 and 0.985 using 3DCNN and MIL, respectively.
Analyzing autoencoder-based acoustic word embeddings
Authors: Yevgen Matusevych, Herman Kamper, Sharon Goldwater
Date: 2020-04-03 00:00:00
Tasks: Decoder, Word Embeddings

Recent studies have introduced methods for learning acoustic word embeddings (AWEs)---fixed-size vector representations of words which encode their acoustic features. Despite the widespread use of AWEs in speech processing research, they have only been evaluated quantitatively in their ability to discriminate between whole word tokens. To better understand the applications of AWEs in various downstream tasks and in cognitive modeling, we need to analyze the representation spaces of AWEs. Here we analyze basic properties of AWE spaces learned by a sequence-to-sequence encoder-decoder model in six typologically diverse languages. We first show that these AWEs preserve some information about words' absolute duration and speaker. At the same time, the representation space of these AWEs is organized such that the distance between words' embeddings increases with those words' phonetic dissimilarity. Finally, the AWEs exhibit a word onset bias, similar to patterns reported in various studies on human speech processing and lexical access. We argue this is a promising result and encourage further evaluation of AWEs as a potentially useful tool in cognitive science, which could provide a link between speech processing and lexical memory.
M2: Mixed Models with Preferences, Popularities and Transitions for Next-Basket Recommendation
Authors: Bo Peng, Zhiyun Ren, Srinivasan Parthasarathy, Xia Ning
Date: 2020-04-03 00:00:00
Tasks: Decoder, Next-basket recommendation

Next-basket recommendation considers the problem of recommending a set of items into the next basket that users will purchase as a whole. In this paper, we develop a novel mixed model with preferences, popularities and transitions (M2) for the next-basket recommendation. This method models three important factors in next-basket generation process: 1) users' general preferences, 2) items' global popularities and 3) transition patterns among items. Unlike existing recurrent neural network-based approaches, M2 does not use the complicated networks to model the transitions among items, or generate embeddings for users. Instead, it has a simple encoder-decoder based approach (ed-Trans) to better model the transition patterns among items. We compared M2 with different combinations of the factors with 5 state-of-the-art next-basket recommendation methods on 4 public benchmark datasets in recommending the first, second and third next basket. Our experimental results demonstrate that M2 significantly outperforms the state-of-the-art methods on all the datasets in all the tasks, with an improvement of up to 22.1%. In addition, our ablation study demonstrates that the ed-Trans is more effective than recurrent neural networks in terms of the recommendation performance. We also have a thorough discussion on various experimental protocols and evaluation metrics for next-basket recommendation evaluation.
Quantifying Data Augmentation for LiDAR based 3D Object Detection
Authors: Martin Hahner, Dengxin Dai, Alexander Liniger, Luc van Gool
Date: 2020-04-03 00:00:00
Tasks: 3D Object Detection, Data Augmentation, Object, object-detection, Object Detection, Translation

In this work, we shed light on different data augmentation techniques commonly used in Light Detection and Ranging (LiDAR) based 3D Object Detection. For the bulk of our experiments, we utilize the well known PointPillars pipeline and the well established KITTI dataset. We investigate a variety of global and local augmentation techniques, where global augmentation techniques are applied to the entire point cloud of a scene and local augmentation techniques are only applied to points belonging to individual objects in the scene. Our findings show that both types of data augmentation can lead to performance increases, but it also turns out, that some augmentation techniques, such as individual object translation, for example, can be counterproductive and can hurt the overall performance. We show that these findings transfer and generalize well to other state of the art 3D Object Detection methods and the challenging STF dataset. On the KITTI dataset we can gain up to 1.5% and on the STF dataset up to 1.7% in 3D mAP on the moderate car class.
Weighted Random Search for Hyperparameter Optimization
Authors: Adrian-Catalin Florea, Razvan Andonie
Date: 2020-04-03 00:00:00
Tasks: Hyperparameter Optimization

We introduce an improved version of Random Search (RS), used here for hyperparameter optimization of machine learning algorithms. Unlike the standard RS, which generates for each trial new values for all hyperparameters, we generate new values for each hyperparameter with a probability of change. The intuition behind our approach is that a value that already triggered a good result is a good candidate for the next step, and should be tested in new combinations of hyperparameter values. Within the same computational budget, our method yields better results than the standard RS. Our theoretical results prove this statement. We test our method on a variation of one of the most commonly used objective function for this class of problems (the Grievank function) and for the hyperparameter optimization of a deep learning CNN architecture. Our results can be generalized to any optimization problem defined on a discrete domain.
Estimation and Uniform Inference in Sparse High-Dimensional Additive Models
Authors: Philipp Bach, Sven Klaassen, Jannis Kueck, Martin Spindler
Date: 2020-04-03 00:00:00
Tasks: Additive models, valid, Vocal Bursts Intensity Prediction

We develop a novel method to construct uniformly valid confidence bands for a nonparametric component $f_1$ in the sparse additive model $Y=f_1(X_1)+\ldots + f_p(X_p) + \varepsilon$ in a high-dimensional setting. Our method integrates sieve estimation into a high-dimensional Z-estimation framework, facilitating the construction of uniformly valid confidence bands for the target component $f_1$. To form these confidence bands, we employ a multiplier bootstrap procedure. Additionally, we provide rates for the uniform lasso estimation in high dimensions, which may be of independent interest. Through simulation studies, we demonstrate that our proposed method delivers reliable results in terms of estimation and coverage, even in small samples.
Using Large-Scale Anomaly Detection on Code to Improve Kotlin Compiler
Authors: Timofey Bryksin, Victor Petukhov, Ilya Alexin, Stanislav Prikhodko, Alexey Shpilman, Vladimir Kovalenko, Nikita Povarov
Date: 2020-04-03 00:00:00
Tasks: Anomaly Detection

In this work, we apply anomaly detection to source code and bytecode to facilitate the development of a programming language and its compiler. We define anomaly as a code fragment that is different from typical code written in a particular programming language. Identifying such code fragments is beneficial to both language developers and end users, since anomalies may indicate potential issues with the compiler or with runtime performance. Moreover, anomalies could correspond to problems in language design. For this study, we choose Kotlin as the target programming language. We outline and discuss approaches to obtaining vector representations of source code and bytecode and to the detection of anomalies across vectorized code snippets. The paper presents a method that aims to detect two types of anomalies: syntax tree anomalies and so-called compiler-induced anomalies that arise only in the compiled bytecode. We describe several experiments that employ different combinations of vectorization and anomaly detection techniques and discuss types of detected anomalies and their usefulness for language developers. We demonstrate that the extracted anomalies and the underlying extraction technique provide additional value for language development.
Deep Transfer Learning for Texture Classification in Colorectal Cancer Histology
Authors: Srinath Jayachandran, Ashlin Ghosh
Date: 2020-04-03 00:00:00
Tasks: Classification, Diagnostic, General Classification, Texture Classification, Transfer Learning

Microscopic examination of tissues or histopathology is one of the diagnostic procedures for detecting colorectal cancer. The pathologist involved in such an examination usually identifies tissue type based on texture analysis, especially focusing on tumour-stroma ratio. In this work, we automate the task of tissue classification within colorectal cancer histology samples using deep transfer learning. We use discriminative fine-tuning with one-cycle-policy and apply structure-preserving colour normalization to boost our results. We also provide visual explanations of the deep neural network's decision on texture classification. With achieving state-of-the-art test accuracy of 96.2% we also embark on using deployment friendly architecture called SqueezeNet for memory-limited hardware.
Deep Learning for Image Search and Retrieval in Large Remote Sensing Archives
Authors: Gencer Sumbul, Jian Kang, Begüm Demir
Date: 2020-04-03 00:00:00
Tasks: Deep Hashing, Image Retrieval, Retrieval

This chapter presents recent advances in content based image search and retrieval (CBIR) systems in remote sensing (RS) for fast and accurate information discovery from massive data archives. Initially, we analyze the limitations of the traditional CBIR systems that rely on the hand-crafted RS image descriptors. Then, we focus our attention on the advances in RS CBIR systems for which deep learning (DL) models are at the forefront. In particular, we present the theoretical properties of the most recent DL based CBIR systems for the characterization of the complex semantic content of RS images. After discussing their strengths and limitations, we present the deep hashing based CBIR systems that have high time-efficient search capability within huge data archives. Finally, the most promising research directions in RS CBIR are discussed.
Interpreting Medical Image Classifiers by Optimization Based Counterfactual Impact Analysis
Authors: David Major, Dimitrios Lenis, Maria Wimmer, Gert Sluiter, Astrid Berg, Katja Bühler
Date: 2020-04-03 00:00:00
Tasks: counterfactual, Diagnostic, General Classification

Clinical applicability of automated decision support systems depends on a robust, well-understood classification interpretation. Artificial neural networks while achieving class-leading scores fall short in this regard. Therefore, numerous approaches have been proposed that map a salient region of an image to a diagnostic classification. Utilizing heuristic methodology, like blurring and noise, they tend to produce diffuse, sometimes misleading results, hindering their general adoption. In this work we overcome these issues by presenting a model agnostic saliency mapping framework tailored to medical imaging. We replace heuristic techniques with a strong neighborhood conditioned inpainting approach, which avoids anatomically implausible artefacts. We formulate saliency attribution as a map-quality optimization task, enforcing constrained and focused attributions. Experiments on public mammography data show quantitatively and qualitatively more precise localization and clearer conveying results than existing state-of-the-art methods.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Authors: Paulo R. de O. da Costa, Jason Rhuggenaath, Yingqian Zhang, Alp Akcay
Date: 2020-04-03 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Traveling Salesman Problem

Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance. However, few studies have focused on improvement heuristics, where a given solution is improved until reaching a near-optimal one. In this work, we propose to learn a local search heuristic based on 2-opt operators via deep reinforcement learning. We propose a policy gradient algorithm to learn a stochastic policy that selects 2-opt operations given a current solution. Moreover, we introduce a policy neural network that leverages a pointing attention mechanism, which unlike previous works, can be easily extended to more general k-opt moves. Our results show that the learned policies can improve even over random initial solutions and approach near-optimal solutions at a faster rate than previous state-of-the-art deep learning methods.
Cell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
Authors: Filip Lux, Petr Matula
Date: 2020-04-03 00:00:00
Tasks: Cell Detection, Cell Segmentation, Cell Tracking, Data Augmentation, Deep Learning, Segmentation

We propose a cell segmentation method for analyzing images of densely clustered cells. The method combines the strengths of marker-controlled watershed transformation and a convolutional neural network (CNN). We demonstrate the method universality and high performance on three Cell Tracking Challenge (CTC) datasets of clustered cells captured by different acquisition techniques. For all tested datasets, our method reached the top performance in both cell detection and segmentation. Based on a series of experiments, we observed: (1) Predicting both watershed marker function and segmentation function significantly improves the accuracy of the segmentation. (2) Both functions can be learned independently. (3) Training data augmentation by scaling and rigid geometric transformations is superior to augmentation that involves elastic transformations. Our method is simple to use, and it generalizes well for various data with state-of-the-art performance.
On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network
Authors: Kieran Woodward, Eiman Kanjo, David J. Brown, T. M. McGinnity
Date: 2020-04-03 00:00:00
Tasks: Edge-computing, Transfer Learning

Stress is a growing concern in modern society adversely impacting the wider population more than ever before. The accurate inference of stress may result in the possibility for personalised interventions. However, individual differences between people limits the generalisability of machine learning models to infer emotions as people's physiology when experiencing the same emotions widely varies. In addition, it is time consuming and extremely challenging to collect large datasets of individuals' emotions as it relies on users labelling sensor data in real-time for extended periods. We propose the development of a personalised, cross-domain 1D CNN by utilising transfer learning from an initial base model trained using data from 20 participants completing a controlled stressor experiment. By utilising physiological sensors (HR, HRV EDA) embedded within edge computing interfaces that additionally contain a labelling technique, it is possible to collect a small real-world personal dataset that can be used for on-device transfer learning to improve model personalisation and cross-domain performance.
Predicting rice blast disease: machine learning versus process based models
Authors: David F. Nettleton, Dimitrios Katsantonis, Argyris Kalaitzidis, Natasa Sarafijanovic-Djukic, Pau Puigdollers, Roberto Confalonieri
Date: 2020-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Management

Rice is the second most important cereal crop worldwide, and the first in terms of number of people who depend on it as a major staple food. Rice blast disease is the most important biotic constraint of rice cultivation causing each year millions of dollars of losses. Despite the efforts for breeding new resistant varieties, agricultural practices and chemical control are still the most important methods for disease management. Thus, rice blast forecasting is a primary tool to support rice growers in controlling the disease. In this study, we compared four models for predicting rice blast disease, two operational process-based models (Yoshino and WARM) and two approaches based on machine learning algorithms (M5Rules and RNN), the former inducing a rule-based model and the latter building a neural network. In situ telemetry is important to obtain quality in-field data for predictive models and this was a key aspect of the RICE-GUARD project on which this study is based. According to the authors, this is the first time process-based and machine learning modelling approaches for supporting plant disease management are compared.