pwc-archive/papers-with-abstracts huggingface dataset:
Closed-Form Factorization of Latent Semantics in GANs
Authors: Yujun Shen, Bolei Zhou
Date: 2020-07-13 00:00:00
Proceeding: CVPR 2021 1
Tasks: Attribute, Form, Image Generation, Image Manipulation

A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In order to identify such latent dimensions for image editing, previous methods typically annotate a collection of synthesized samples and train linear classifiers in the latent space. However, they require a clear definition of the target attribute as well as the corresponding manual annotations, limiting their applications in practice. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. In particular, we take a closer look into the generation mechanism of GANs and further propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights. With a lightning-fast implementation, our approach is capable of not only finding semantically meaningful dimensions comparably to the state-of-the-art supervised methods, but also resulting in far more versatile concepts across multiple GAN models trained on a wide range of datasets.
Gravitational-wave selection effects using neural-network classifiers
Authors: Davide Gerosa, Geraint Pratten, Alberto Vecchio
Date: 2020-07-13 00:00:00
Tasks: image-classification, Image Classification

We present a novel machine-learning approach to estimate selection effects in gravitational-wave observations. Using techniques similar to those commonly employed in image classification and pattern recognition, we train a series of neural-network classifiers to predict the LIGO/Virgo detectability of gravitational-wave signals from compact-binary mergers. We include the effect of spin precession, higher-order modes, and multiple detectors and show that their omission, as it is common in large population studies, tends to overestimate the inferred merger rate in selected regions of the parameter space. Although here we train our classifiers using a simple signal-to-noise ratio threshold, our approach is ready to be used in conjunction with full pipeline injections, thus paving the way toward including actual distributions of astrophysical and noise triggers into gravitational-wave population analyses.
Towards causal benchmarking of bias in face analysis algorithms
Authors: Guha Balakrishnan, Yuanjun Xiong, Wei Xia, Pietro Perona
Date: 2020-07-13 00:00:00
Proceeding: ECCV 2020 8
Tasks: Attribute, Benchmarking, Fairness, Gender Classification

Measuring algorithmic bias is crucial both to assess algorithmic fairness, and to guide the improvement of algorithms. Current methods to measure algorithmic bias in computer vision, which are based on observational datasets, are inadequate for this task because they conflate algorithmic bias with dataset bias. To address this problem we develop an experimental method for measuring algorithmic bias of face analysis algorithms, which manipulates directly the attributes of interest, e.g., gender and skin tone, in order to reveal causal links between attribute variation and performance change. Our proposed method is based on generating synthetic ``transects'' of matched sample images that are designed to differ along specific attributes while leaving other attributes constant. A crucial aspect of our approach is relying on the perception of human observers, both to guide manipulations, and to measure algorithmic bias. Besides allowing the measurement of algorithmic bias, synthetic transects have other advantages with respect to observational datasets: they sample attributes more evenly allowing for more straightforward bias analysis on minority and intersectional groups, they enable prediction of bias in new scenarios, they greatly reduce ethical and legal challenges, and they are economical and fast to obtain, helping make bias testing affordable and widely available. We validate our method by comparing it to a study that employs the traditional observational method for analyzing bias in gender classification algorithms. The two methods reach different conclusions. While the observational method reports gender and skin color biases, the experimental method reveals biases due to gender, hair length, age, and facial hair.
Security and Machine Learning in the Real World
Authors: Ivan Evtimov, Weidong Cui, Ece Kamar, Emre Kiciman, Tadayoshi Kohno, Jerry Li
Date: 2020-07-13 00:00:00
Tasks: BIG-bench Machine Learning

Machine learning (ML) models deployed in many safety- and business-critical systems are vulnerable to exploitation through adversarial examples. A large body of academic research has thoroughly explored the causes of these blind spots, developed sophisticated algorithms for finding them, and proposed a few promising defenses. A vast majority of these works, however, study standalone neural network models. In this work, we build on our experience evaluating the security of a machine learning software product deployed on a large scale to broaden the conversation to include a systems security view of these vulnerabilities. We describe novel challenges to implementing systems security best practices in software with ML components. In addition, we propose a list of short-term mitigation suggestions that practitioners deploying machine learning modules can use to secure their systems. Finally, we outline directions for new research into machine learning attacks and defenses that can serve to advance the state of ML systems security.
CenterNet3D: An Anchor Free Object Detector for Point Cloud
Authors: Guojun Wang, Jian Wu, Bin Tian, Siyu Teng, Long Chen, Dongpu Cao
Date: 2020-07-13 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, Keypoint Estimation, Object, object-detection, Object Detection

Accurate and fast 3D object detection from point clouds is a key task in autonomous driving. Existing one-stage 3D object detection methods can achieve real-time performance, however, they are dominated by anchor-based detectors which are inefficient and require additional post-processing. In this paper, we eliminate anchors and model an object as a single point--the center point of its bounding box. Based on the center point, we propose an anchor-free CenterNet3D network that performs 3D object detection without anchors. Our CenterNet3D uses keypoint estimation to find center points and directly regresses 3D bounding boxes. However, because inherent sparsity of point clouds, 3D object center points are likely to be in empty space which makes it difficult to estimate accurate boundaries. To solve this issue, we propose an extra corner attention module to enforce the CNN backbone to pay more attention to object boundaries. Besides, considering that one-stage detectors suffer from the discordance between the predicted bounding boxes and corresponding classification confidences, we develop an efficient keypoint-sensitive warping operation to align the confidences to the predicted bounding boxes. Our proposed CenterNet3D is non-maximum suppression free which makes it more efficient and simpler. We evaluate CenterNet3D on the widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art anchor-based one-stage methods and has comparable performance to two-stage methods as well. It has an inference speed of 20 FPS and achieves the best speed and accuracy trade-off. Our source code will be released at https://github.com/wangguojun2018/CenterNet3d.
Lossless Compression of Structured Convolutional Models via Lifting
Authors: Gustav Sourek, Filip Zelezny, Ondrej Kuzelka
Date: 2020-07-13 00:00:00
Proceeding: ICLR 2021 1
Tasks: Knowledge Base Completion

Lifting is an efficient technique to scale up graphical models generalized to relational domains by exploiting the underlying symmetries. Concurrently, neural models are continuously expanding from grid-like tensor data into structured representations, such as various attributed graphs and relational databases. To address the irregular structure of the data, the models typically extrapolate on the idea of convolution, effectively introducing parameter sharing in their, dynamically unfolded, computation graphs. The computation graphs themselves then reflect the symmetries of the underlying data, similarly to the lifted graphical models. Inspired by lifting, we introduce a simple and efficient technique to detect the symmetries and compress the neural models without loss of any information. We demonstrate through experiments that such compression can lead to significant speedups of structured convolutional models, such as various Graph Neural Networks, across various tasks, such as molecule classification and knowledge-base completion.
A unified machine learning approach to time series forecasting applied to demand at emergency departments
Authors: Michaela A. C. Vollmer, Ben Glampson, Thomas A. Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt
Date: 2020-07-13 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis, Time Series Forecasting

There were 25.6 million attendances at Emergency Departments (EDs) in England in 2019 corresponding to an increase of 12 million attendances over the past ten years. The steadily rising demand at EDs creates a constant challenge to provide adequate quality of care while maintaining standards and productivity. Managing hospital demand effectively requires an adequate knowledge of the future rate of admission. Using 8 years of electronic admissions data from two major acute care hospitals in London, we develop a novel ensemble methodology that combines the outcomes of the best performing time series and machine learning approaches in order to make highly accurate forecasts of demand, 1, 3 and 7 days in the future. Both hospitals face an average daily demand of 208 and 106 attendances respectively and experience considerable volatility around this mean. However, our approach is able to predict attendances at these emergency departments one day in advance up to a mean absolute error of +/- 14 and +/- 10 patients corresponding to a mean absolute percentage error of 6.8% and 8.6% respectively. Our analysis compares machine learning algorithms to more traditional linear models. We find that linear models often outperform machine learning methods and that the quality of our predictions for any of the forecasting horizons of 1, 3 or 7 days are comparable as measured in MAE. In addition to comparing and combining state-of-the-art forecasting methods to predict hospital demand, we consider two different hyperparameter tuning methods, enabling a faster deployment of our models without compromising performance. We believe our framework can readily be used to forecast a wide range of policy relevant indicators.
Simple and Efficient Hard Label Black-box Adversarial Attacks in Low Query Budget Regimes
Authors: Satya Narayan Shukla, Anit Kumar Sahu, Devin Willmott, J. Zico Kolter
Date: 2020-07-13 00:00:00
Tasks: Bayesian Optimization

We focus on the problem of black-box adversarial attacks, where the aim is to generate adversarial examples for deep learning models solely based on information limited to output label~(hard label) to a queried data input. We propose a simple and efficient Bayesian Optimization~(BO) based approach for developing black-box adversarial attacks. Issues with BO's performance in high dimensions are avoided by searching for adversarial examples in a structured low-dimensional subspace. We demonstrate the efficacy of our proposed attack method by evaluating both $\ell_\infty$ and $\ell_2$ norm constrained untargeted and targeted hard label black-box attacks on three standard datasets - MNIST, CIFAR-10 and ImageNet. Our proposed approach consistently achieves 2x to 10x higher attack success rate while requiring 10x to 20x fewer queries compared to the current state-of-the-art black-box adversarial attacks.
MeTRAbs: Metric-Scale Truncation-Robust Heatmaps for Absolute 3D Human Pose Estimation
Authors: István Sárándi, Timm Linder, Kai O. Arras, Bastian Leibe
Date: 2020-07-12 00:00:00
Tasks: 3D Absolute Human Pose Estimation, 3D Human Pose Estimation, Pose Estimation

Heatmap representations have formed the basis of human pose estimation systems for many years, and their extension to 3D has been a fruitful line of recent research. This includes 2.5D volumetric heatmaps, whose X and Y axes correspond to image space and Z to metric depth around the subject. To obtain metric-scale predictions, 2.5D methods need a separate post-processing step to resolve scale ambiguity. Further, they cannot localize body joints outside the image boundaries, leading to incomplete estimates for truncated images. To address these limitations, we propose metric-scale truncation-robust (MeTRo) volumetric heatmaps, whose dimensions are all defined in metric 3D space, instead of being aligned with image space. This reinterpretation of heatmap dimensions allows us to directly estimate complete, metric-scale poses without test-time knowledge of distance or relying on anthropometric heuristics, such as bone lengths. To further demonstrate the utility our representation, we present a differentiable combination of our 3D metric-scale heatmaps with 2D image-space ones to estimate absolute 3D pose (our MeTRAbs architecture). We find that supervision via absolute pose loss is crucial for accurate non-root-relative localization. Using a ResNet-50 backbone without further learned layers, we obtain state-of-the-art results on Human3.6M, MPI-INF-3DHP and MuPoTS-3D. Our code will be made publicly available to facilitate further research.
Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations
Authors: Weihao Gao, Xiangjun Fan, Chong Wang, Jiankai Sun, Kai Jia, Wenzhi Xiao, Ruofan Ding, Xingyan Bin, Hui Yang, Xiaobing Liu
Date: 2020-07-12 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Reranking, Retrieval

One of the core problems in large-scale recommendations is to retrieve top relevant candidates accurately and efficiently, preferably in sub-linear time. Previous approaches are mostly based on a two-step procedure: first learn an inner-product model, and then use some approximate nearest neighbor (ANN) search algorithm to find top candidates. In this paper, we present Deep Retrieval (DR), to learn a retrievable structure directly with user-item interaction data (e.g. clicks) without resorting to the Euclidean space assumption in ANN algorithms. DR's structure encodes all candidate items into a discrete latent space. Those latent codes for the candidates are model parameters and learnt together with other neural network parameters to maximize the same objective function. With the model learnt, a beam search over the structure is performed to retrieve the top candidates for reranking. Empirically, we first demonstrate that DR, with sub-linear computational complexity, can achieve almost the same accuracy as the brute-force baseline on two public datasets. Moreover, we show that, in a live production recommendation system, a deployed DR approach significantly outperforms a well-tuned ANN baseline in terms of engagement metrics. To the best of our knowledge, DR is among the first non-ANN algorithms successfully deployed at the scale of hundreds of millions of items for industrial recommendation systems.
FocusLiteNN: High Efficiency Focus Quality Assessment for Digital Pathology
Authors: Zhongling Wang, Mahdi S. Hosseini, Adyn Miles, Konstantinos N. Plataniotis, Zhou Wang
Date: 2020-07-11 00:00:00
Tasks: Vocal Bursts Intensity Prediction

Out-of-focus microscopy lens in digital pathology is a critical bottleneck in high-throughput Whole Slide Image (WSI) scanning platforms, for which pixel-level automated Focus Quality Assessment (FQA) methods are highly desirable to help significantly accelerate the clinical workflows. Existing FQA methods include both knowledge-driven and data-driven approaches. While data-driven approaches such as Convolutional Neural Network (CNN) based methods have shown great promises, they are difficult to use in practice due to their high computational complexity and lack of transferability. Here, we propose a highly efficient CNN-based model that maintains fast computations similar to the knowledge-driven methods without excessive hardware requirements such as GPUs. We create a training dataset using FocusPath which encompasses diverse tissue slides across nine different stain colors, where the stain diversity greatly helps the model to learn diverse color spectrum and tissue structures. In our attempt to reduce the CNN complexity, we find with surprise that even trimming down the CNN to the minimal level, it still achieves a highly competitive performance. We introduce a novel comprehensive evaluation dataset, the largest of its kind, annotated and compiled from TCGA repository for model assessment and comparison, for which the proposed method exhibits superior precision-speed trade-off when compared with existing knowledge-driven and data-driven FQA approaches.
Nodule2vec: a 3D Deep Learning System for Pulmonary Nodule Retrieval Using Semantic Representation
Authors: Ilia Kravets, Tal Heletz, Hayit Greenspan
Date: 2020-07-11 00:00:00
Tasks: Content-Based Image Retrieval, Decision Making, Image Retrieval, Lung Nodule Detection, Retrieval, Transfer Learning

Content-based retrieval supports a radiologist decision making process by presenting the doctor the most similar cases from the database containing both historical diagnosis and further disease development history. We present a deep learning system that transforms a 3D image of a pulmonary nodule from a CT scan into a low-dimensional embedding vector. We demonstrate that such a vector representation preserves semantic information about the nodule and offers a viable approach for content-based image retrieval (CBIR). We discuss the theoretical limitations of the available datasets and overcome them by applying transfer learning of the state-of-the-art lung nodule detection model. We evaluate the system using the LIDC-IDRI dataset of thoracic CT scans. We devise a similarity score and show that it can be utilized to measure similarity 1) between annotations of the same nodule by different radiologists and 2) between the query nodule and the top four CBIR results. A comparison between doctors and algorithm scores suggests that the benefit provided by the system to the radiologist end-user is comparable to obtaining a second radiologist's opinion.
Investigation of Sentiment Controllable Chatbot
Authors: Hung-Yi Lee, Cheng-Hao Ho, Chien-Fu Lin, Chiung-Chih Chang, Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu, Kuan-Yu Chen
Date: 2020-07-11 00:00:00
Tasks: Chatbot, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the output sentences. In this paper, we investigate four models to scale or adjust the sentiment of the chatbot response: a persona-based model, reinforcement learning, a plug and play model, and CycleGAN, all based on the seq2seq model. We also develop machine-evaluated metrics to estimate whether the responses are reasonable given the input. These metrics, together with human evaluation, are used to analyze the performance of the four models in terms of different aspects; reinforcement learning and CycleGAN are shown to be very attractive.
Transfer learning extensions for the probabilistic classification vector machine
Authors: Christoph Raab, Frank-Michael Schleif
Date: 2020-07-11 00:00:00
Tasks: Classification, General Classification, Transfer Learning

Transfer learning is focused on the reuse of supervised learning models in a new context. Prominent applications can be found in robotics, image processing or web mining. In these fields, the learning scenarios are naturally changing but often remain related to each other motivating the reuse of existing supervised models. Current transfer learning models are neither sparse nor interpretable. Sparsity is very desirable if the methods have to be used in technically limited environments and interpretability is getting more critical due to privacy regulations. In this work, we propose two transfer learning extensions integrated into the sparse and interpretable probabilistic classification vector machine. They are compared to standard benchmarks in the field and show their relevance either by sparsity or performance improvements.
Polestar: An Intelligent, Efficient and National-Wide Public Transportation Routing Engine
Authors: Hao Liu, Ying Li, Yanjie Fu, Huaibo Mei, Jingbo Zhou, Xu Ma, Hui Xiong
Date: 2020-07-11 00:00:00

Public transportation plays a critical role in people's daily life. It has been proven that public transportation is more environmentally sustainable, efficient, and economical than any other forms of travel. However, due to the increasing expansion of transportation networks and more complex travel situations, people are having difficulties in efficiently finding the most preferred route from one place to another through public transportation systems. To this end, in this paper, we present Polestar, a data-driven engine for intelligent and efficient public transportation routing. Specifically, we first propose a novel Public Transportation Graph (PTG) to model public transportation system in terms of various travel costs, such as time or distance. Then, we introduce a general route search algorithm coupled with an efficient station binding method for efficient route candidate generation. After that, we propose a two-pass route candidate ranking module to capture user preferences under dynamic travel situations. Finally, experiments on two real-world data sets demonstrate the advantages of Polestar in terms of both efficiency and effectiveness. Indeed, in early 2019, Polestar has been deployed on Baidu Maps, one of the world's largest map services. To date, Polestar is servicing over 330 cities, answers over a hundred millions of queries each day, and achieves substantial improvement of user click ratio.
HOBFLOPS CNNs: Hardware Optimized Bitslice-Parallel Floating-Point Operations for Convolutional Neural Networks
Authors: James Garland, David Gregg
Date: 2020-07-11 00:00:00

Convolutional neural networks (CNNs) are typically trained using 16- or 32-bit floating-point (FP) and researchers show that low-precision floating-point (FP) can be highly effective for inference. Low-precision FP can be implemented in field programmable gate array (FPGA) and application-specific integrated circuit (ASIC) accelerators, but existing processors do not generally support custom precision FP. We propose hardware optimized bitslice-parallel floating-point operators (HOBFLOPS), a method of generating efficient custom-precision emulated bitslice-parallel software FP arithmetic. We generate custom-precision FP routines optimized using a hardware synthesis design flow to create circuits. We provide standard cell libraries matching the bitwise operations on the target microprocessor architecture, and a code-generator to translate the hardware circuits to bitslice software equivalents. We exploit bitslice parallelism to create a very wide (32-512 element) vectorized convolutional neural network (CNN) convolution. Hardware optimized bitslice-parallel floating-point operators (HOBFLOPS) multiply-accumulate (MAC) performance in CNN convolution on Arm and Intel processors are compared to Berkeley's SoftFP16 equivalent MAC. HOBFLOPS16 outperforms SoftFP16 by 8x on Intel AVX512. HOBFLOPS offers arbitrary-precision FP with custom range and precision e.g., HOBFLOPS9 performs at 6x the performance of HOBFLOPS16 on Arm Neon. HOBFLOPS allows researchers to prototype different levels of custom FP precision in the arithmetic of software CNN accelerators. Furthermore, HOBFLOPS fast custom-precision FP CNNs may be valuable in cases where memory bandwidth is limited.