pwc-archive/papers-with-abstracts huggingface dataset:
Interpreting Epsilon of Differential Privacy in Terms of Advantage in Guessing or Approximating Sensitive Attributes
Authors: Peeter Laud, Alisa Pankova
Date: 2019-11-28 00:00:00

There are numerous methods of achieving $\epsilon$-differential privacy (DP). The question is what is the appropriate value of $\epsilon$, since there is no common agreement on a "sufficiently small" $\epsilon$, and its goodness depends on the query as well as the data. In this paper, we show how to compute $\epsilon$ that corresponds to $\delta$, defined as the adversary's advantage in probability of guessing some specific property of the output. The attacker's goal can be stated as Boolean expression over guessing particular attributes, possibly within some precision. The attributes combined in this way should be independent. We assume that both the input and the output distributions have corresponding probability density functions, or probability mass functions.
Effective Sub-clonal Cancer Representation to Predict Tumor Evolution
Authors: Adnan Akbar, Geoffroy Dubourg-Felonneau, Andrey Solovyev, John W Cassidy, Nirmesh Patel, Harry W Clifford
Date: 2019-11-28 00:00:00
Tasks: BIG-bench Machine Learning

The majority of cancer treatments end in failure due to Intra-Tumor Heterogeneity (ITH). ITH in cancer is represented by clonal evolution where different sub-clones compete with each other for resources under conditions of Darwinian natural selection. Predicting the growth of these sub-clones within a tumour is among the key challenges of modern cancer research. Predicting tumor behavior enables the creation of risk profiles for patients and the optimisation of their treatment by therapeutically targeting sub-clones more likely to grow. Current research efforts in this space are focused on mathematical modelling of population genetics to quantify the selective advantage of sub-clones, thus enabling predictions of which sub-clones are more likely to grow. These tumor evolution models are based on assumptions which are not valid for real-world tumor micro-environment. Furthermore, these models are often fit on a single instance of a tumor, and hence prediction models cannot be validated. This paper presents an alternative approach for predicting cancer evolution using a data-driven machine learning method. Our proposed method is based on the intuition that if we can capture the true characteristics of sub-clones within a tumor and represent it in the form of features, a sophisticated machine learning algorithm can be trained to predict its behavior. The work presented here provides a novel approach to predicting cancer evolution, utilizing a data-driver approach. We strongly believe that the accumulation of data from microbiologists, oncologists and machine learning researchers could be used to encapsulate the true essence of tumor sub-clones, and can play a vital role in selecting the best cancer treatments for patients.
Dividing and Conquering Cross-Modal Recipe Retrieval: from Nearest Neighbours Baselines to SoTA
Authors: Mikhail Fain, Niall Twomey, Andrey Ponikar, Ryan Fox, Danushka Bollegala
Date: 2019-11-28 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval, Triplet

We propose a novel non-parametric method for cross-modal recipe retrieval which is applied on top of precomputed image and text embeddings. By combining our method with standard approaches for building image and text encoders, trained independently with a self-supervised classification objective, we create a baseline model which outperforms most existing methods on a challenging image-to-recipe task. We also use our method for comparing image and text encoders trained using different modern approaches, thus addressing the issues hindering the development of novel methods for cross-modal recipe retrieval. We demonstrate how to use the insights from model comparison and extend our baseline model with standard triplet loss that improves state-of-the-art on the Recipe1M dataset by a large margin, while using only precomputed features and with much less complexity than existing methods. Further, our approach readily generalizes beyond recipe retrieval to other challenging domains, achieving state-of-the-art performance on Politics and GoodNews cross-modal retrieval tasks.
Using VAEs and Normalizing Flows for One-shot Text-To-Speech Synthesis of Expressive Speech
Authors: Vatsal Aggarwal, Marius Cotescu, Nishant Prateek, Jaime Lorenzo-Trueba, Roberto Barra-Chicote
Date: 2019-11-28 00:00:00
Tasks: Disentanglement, Expressive Speech Synthesis, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

We propose a Text-to-Speech method to create an unseen expressive style using one utterance of expressive speech of around one second. Specifically, we enhance the disentanglement capabilities of a state-of-the-art sequence-to-sequence based system with a Variational AutoEncoder (VAE) and a Householder Flow. The proposed system provides a 22% KL-divergence reduction while jointly improving perceptual metrics over state-of-the-art. At synthesis time we use one example of expressive style as a reference input to the encoder for generating any text in the desired style. Perceptual MUSHRA evaluations show that we can create a voice with a 9% relative naturalness improvement over standard Neural Text-to-Speech, while also improving the perceived emotional intensity (59 compared to the 55 of neutral speech).
Inducing Relational Knowledge from BERT
Authors: Zied Bouraoui, Jose Camacho-Collados, Steven Schockaert
Date: 2019-11-28 00:00:00
Tasks: Language Modeling, Language Modelling, Relation, Word Embeddings

One of the most remarkable properties of word embeddings is the fact that they capture certain types of semantic and syntactic relationships. Recently, pre-trained language models such as BERT have achieved groundbreaking results across a wide range of Natural Language Processing tasks. However, it is unclear to what extent such models capture relational knowledge beyond what is already captured by standard word embeddings. To explore this question, we propose a methodology for distilling relational knowledge from a pre-trained language model. Starting from a few seed instances of a given relation, we first use a large text corpus to find sentences that are likely to express this relation. We then use a subset of these extracted sentences as templates. Finally, we fine-tune a language model to predict whether a given word pair is likely to be an instance of some relation, when given an instantiated template for that relation as input.
ASR is all you need: cross-modal distillation for lip reading
Authors: Triantafyllos Afouras, Joon Son Chung, Andrew Zisserman
Date: 2019-11-28 00:00:00
Tasks: All, Automatic Speech Recognition, Automatic Speech Recognition (ASR), Lipreading, Lip Reading, speech-recognition, Speech Recognition, Visual Speech Recognition

The goal of this work is to train strong models for visual speech recognition without requiring human annotated ground truth data. We achieve this by distilling from an Automatic Speech Recognition (ASR) model that has been trained on a large-scale audio-only corpus. We use a cross-modal distillation method that combines Connectionist Temporal Classification (CTC) with a frame-wise cross-entropy loss. Our contributions are fourfold: (i) we show that ground truth transcriptions are not necessary to train a lip reading system; (ii) we show how arbitrary amounts of unlabelled video data can be leveraged to improve performance; (iii) we demonstrate that distillation significantly speeds up training; and, (iv) we obtain state-of-the-art results on the challenging LRS2 and LRS3 datasets for training only on publicly available data.
Data-Driven Compression of Convolutional Neural Networks
Authors: Ramit Pahwa, Manoj Ghuhan Arivazhagan, Ankur Garg, Siddarth Krishnamoorthy, Rohit Saxena, Sunav Choudhary
Date: 2019-11-28 00:00:00
Tasks: Knowledge Distillation, Model Compression, Reinforcement Learning

Deploying trained convolutional neural networks (CNNs) to mobile devices is a challenging task because of the simultaneous requirements of the deployed model to be fast, lightweight and accurate. Designing and training a CNN architecture that does well on all three metrics is highly non-trivial and can be very time-consuming if done by hand. One way to solve this problem is to compress the trained CNN models before deploying to mobile devices. This work asks and answers three questions on compressing CNN models automatically: a) How to control the trade-off between speed, memory and accuracy during model compression? b) In practice, a deployed model may not see all classes and/or may not need to produce all class labels. Can this fact be used to improve the trade-off? c) How to scale the compression algorithm to execute within a reasonable amount of time for many deployments? The paper demonstrates that a model compression algorithm utilizing reinforcement learning with architecture search and knowledge distillation can answer these questions in the affirmative. Experimental results are provided for current state-of-the-art CNN model families for image feature extraction like VGG and ResNet with CIFAR datasets.
Every Frame Counts: Joint Learning of Video Segmentation and Optical Flow
Authors: Mingyu Ding, Zhe Wang, Bolei Zhou, Jianping Shi, Zhiwu Lu, Ping Luo
Date: 2019-11-28 00:00:00
Tasks: Optical Flow Estimation, Segmentation, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

A major challenge for video semantic segmentation is the lack of labeled data. In most benchmark datasets, only one frame of a video clip is annotated, which makes most supervised methods fail to utilize information from the rest of the frames. To exploit the spatio-temporal information in videos, many previous works use pre-computed optical flows, which encode the temporal consistency to improve the video segmentation. However, the video segmentation and optical flow estimation are still considered as two separate tasks. In this paper, we propose a novel framework for joint video semantic segmentation and optical flow estimation. Semantic segmentation brings semantic information to handle occlusion for more robust optical flow estimation, while the non-occluded optical flow provides accurate pixel-level temporal correspondences to guarantee the temporal consistency of the segmentation. Moreover, our framework is able to utilize both labeled and unlabeled frames in the video through joint training, while no additional calculation is required in inference. Extensive experiments show that the proposed model makes the video semantic segmentation and optical flow estimation benefit from each other and outperforms existing methods under the same settings in both tasks.
DiversityGAN: Diversity-Aware Vehicle Motion Prediction via Latent Semantic Sampling
Authors: Xin Huang, Stephen G. McGill, Jonathan A. DeCastro, Luke Fletcher, John J. Leonard, Brian C. Williams, Guy Rosman
Date: 2019-11-28 00:00:00
Tasks: Autonomous Driving, Diversity, Generative Adversarial Network, motion prediction, Trajectory Prediction

Vehicle trajectory prediction is crucial for autonomous driving and advanced driver assistant systems. While existing approaches may sample from a predicted distribution of vehicle trajectories, they lack the ability to explore it -- a key ability for evaluating safety from a planning and verification perspective. In this work, we devise a novel approach for generating realistic and diverse vehicle trajectories. We extend the generative adversarial network (GAN) framework with a low-dimensional approximate semantic space, and shape that space to capture semantics such as merging and turning. We sample from this space in a way that mimics the predicted distribution, but allows us to control coverage of semantically distinct outcomes. We validate our approach on a publicly available dataset and show results that achieve state-of-the-art prediction performance, while providing improved coverage of the space of predicted trajectory semantics.
Distributed estimation of principal support vector machines for sufficient dimension reduction
Authors: Jun Jin, Chao Ying, Zhou Yu
Date: 2019-11-28 00:00:00
Tasks: Binary Classification, Dimensionality Reduction

The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional linear combinations without loss of information. However, the computational burden of the principal support vector machines method constrains its use for massive data. To address this issue, we in this paper propose two distributed estimation algorithms for fast implementation when the sample size is large. Both the two distributed sufficient dimension reduction estimators enjoy the same statistical efficiency as merging all the data together, which provides rigorous statistical guarantees for their application to large scale datasets. The two distributed algorithms are further adapt to principal weighted support vector machines (Shin et al., 2016) for sufficient dimension reduction in binary classification. The statistical accuracy and computational complexity of our proposed methods are examined through comprehensive simulation studies and a real data application with more than 600000 samples.
A Fine-Grained Sentiment Dataset for Norwegian
Authors: Lilja Øvrelid, Petter Mæhlum, Jeremy Barnes, Erik Velldal
Date: 2019-11-28 00:00:00
Proceeding: LREC 2020 5
Tasks: Sentiment Analysis

We introduce NoReC_fine, a dataset for fine-grained sentiment analysis in Norwegian, annotated with respect to polar expressions, targets and holders of opinion. The underlying texts are taken from a corpus of professionally authored reviews from multiple news-sources and across a wide variety of domains, including literature, games, music, products, movies and more. We here present a detailed description of this annotation effort. We provide an overview of the developed annotation guidelines, illustrated with examples, and present an analysis of inter-annotator agreement. We also report the first experimental results on the dataset, intended as a preliminary benchmark for further experiments.
Training Multi-Object Detector by Estimating Bounding Box Distribution for Input Image
Authors: Jaeyoung Yoo, Hojun Lee, Inseop Chung, Geonseok Seo, Nojun Kwak
Date: 2019-11-28 00:00:00
Proceeding: ICCV 2021 10
Tasks: Density Estimation, Object, object-detection, Object Detection, Real-Time Object Detection

In multi-object detection using neural networks, the fundamental problem is, "How should the network learn a variable number of bounding boxes in different input images?". Previous methods train a multi-object detection network through a procedure that directly assigns the ground truth bounding boxes to the specific locations of the network's output. However, this procedure makes the training of a multi-object detection network too heuristic and complicated. In this paper, we reformulate the multi-object detection task as a problem of density estimation of bounding boxes. Instead of assigning each ground truth to specific locations of network's output, we train a network by estimating the probability density of bounding boxes in an input image using a mixture model. For this purpose, we propose a novel network for object detection called Mixture Density Object Detector (MDOD), and the corresponding objective function for the density-estimation-based training. We applied MDOD to MS COCO dataset. Our proposed method not only deals with multi-object detection problems in a new approach, but also improves detection performances through MDOD. The code is available: https://github.com/yoojy31/MDOD.
Continuous Adaptation for Interactive Object Segmentation by Learning from Corrections
Authors: Theodora Kontogianni, Michael Gygli, Jasper Uijlings, Vittorio Ferrari
Date: 2019-11-28 00:00:00
Proceeding: ECCV 2020 8
Tasks: Interactive Segmentation, Object, Semantic Segmentation

In interactive object segmentation a user collaborates with a computer vision model to segment an object. Recent works employ convolutional neural networks for this task: Given an image and a set of corrections made by the user as input, they output a segmentation mask. These approaches achieve strong performance by training on large datasets but they keep the model parameters unchanged at test time. Instead, we recognize that user corrections can serve as sparse training examples and we propose a method that capitalizes on that idea to update the model parameters on-the-fly to the data at hand. Our approach enables the adaptation to a particular object and its background, to distributions shifts in a test set, to specific object classes, and even to large domain changes, where the imaging modality changes between training and testing. We perform extensive experiments on 8 diverse datasets and show: Compared to a model with frozen parameters, our method reduces the required corrections (i) by 9%-30% when distribution shifts are small between training and testing; (ii) by 12%-44% when specializing to a specific class; (iii) and by 60% and 77% when we completely change domain between training and testing.
Cameras Viewing Cameras Geometry
Authors: Danail Brezov, Michael Werman
Date: 2019-11-28 00:00:00
Tasks: Autonomous Vehicles

A basic problem in computer vision is to understand the structure of a real-world scene given several images of it. Here we study several theoretical aspects of the intra multi-view geometry of calibrated cameras when all that they can reliably recognize is each other. With the proliferation of wearable cameras, autonomous vehicles and drones, the geometry of these multiple cameras is a timely and relevant problem to study.
Comparative Study of Differentially Private Synthetic Data Algorithms from the NIST PSCR Differential Privacy Synthetic Data Challenge
Authors: Claire McKay Bowen, Joshua Snoke
Date: 2019-11-28 00:00:00
Tasks: Synthetic Data Generation

Differentially private synthetic data generation offers a recent solution to release analytically useful data while preserving the privacy of individuals in the data. In order to utilize these algorithms for public policy decisions, policymakers need an accurate understanding of these algorithms' comparative performance. Correspondingly, data practitioners require standard metrics for evaluating the analytic qualities of the synthetic data. In this paper, we present an in-depth evaluation of several differentially private synthetic data algorithms using actual differentially private synthetic data sets created by contestants in the 2018-2019 National Institute of Standards and Technology Public Safety Communications Research (NIST PSCR) Division's ``Differential Privacy Synthetic Data Challenge.'' We offer analyses of these algorithms based on both the accuracy of the data they created and their usability by potential data providers. We frame the methods used in the NIST PSCR data challenge within the broader differentially private synthetic data literature. We implement additional utility metrics, including two of our own, on the differentially private synthetic data and compare mechanism utility on three categories. Our comparative assessment of the differentially private data synthesis methods and the quality metrics shows the relative usefulness, the general strengths and weaknesses, and offers preferred choices of algorithms and metrics. Finally we describe the implications of our evaluation for policymakers seeking to implement differentially private synthetic data algorithms on future data products.
Neural networks with redundant representation: detecting the undetectable
Authors: Elena Agliari, Francesco Alemanno, Adriano Barra, Martino Centonze, Alberto Fachechi
Date: 2019-11-28 00:00:00

We consider a three-layer Sejnowski machine and show that features learnt via contrastive divergence have a dual representation as patterns in a dense associative memory of order P=4. The latter is known to be able to Hebbian-store an amount of patterns scaling as N^{P-1}, where N denotes the number of constituting binary neurons interacting P-wisely. We also prove that, by keeping the dense associative network far from the saturation regime (namely, allowing for a number of patterns scaling only linearly with N, while P>2) such a system is able to perform pattern recognition far below the standard signal-to-noise threshold. In particular, a network with P=4 is able to retrieve information whose intensity is O(1) even in the presence of a noise O(\sqrt{N}) in the large N limit. This striking skill stems from a redundancy representation of patterns -- which is afforded given the (relatively) low-load information storage -- and it contributes to explain the impressive abilities in pattern recognition exhibited by new-generation neural networks. The whole theory is developed rigorously, at the replica symmetric level of approximation, and corroborated by signal-to-noise analysis and Monte Carlo simulations.
A novel classification-selection approach for the self updating of template-based face recognition systems
Authors: Giulia Orrù, Gian Luca Marcialis, Fabio Roli
Date: 2019-11-28 00:00:00
Tasks: Face Recognition, General Classification

The boosting on the need of security notably increased the amount of possible facial recognition applications, especially due to the success of the Internet of Things (IoT) paradigm. However, although handcrafted and deep learning-inspired facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, facial expressions, lighting changes, and pose. These variations cannot be captured in a single acquisition and require multiple acquisitions of long duration, which are expensive and need a high level of collaboration from the users. Among others, self-update algorithms have been proposed in order to mitigate these problems. Self-updating aims to add novel templates to the users' gallery among the inputs submitted during system operations. Consequently, computational complexity and storage space tend to be among the critical requirements of these algorithms. The present paper deals with the above problems by a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates stored in the system database. The rationale behind the proposed approach is in the working hypothesis that a dominating mode characterises the features' distribution given the client. Therefore, the key point is to select the best templates around that mode. We propose two methods, which are tested on systems based on handcrafted features and deep-learning-inspired autoencoders at the state-of-the-art. Three benchmark data sets are used. Experimental results confirm that, by effective and compact feature sets which can support our working hypothesis, the proposed classification-selection approaches overcome the problem of manual updating and, in case, stringent computational requirements.