pwc-archive/papers-with-abstracts huggingface dataset:
On the Geometry of Adversarial Examples
Authors: Marc Khoury, Dylan Hadfield-Menell
Date: 2018-11-01 00:00:00
Proceeding: ICLR 2019 5

Adversarial examples are a pervasive phenomenon of machine learning models
where seemingly imperceptible perturbations to the input lead to
misclassifications for otherwise statistically accurate models. We propose a
geometric framework, drawing on tools from the manifold reconstruction
literature, to analyze the high-dimensional geometry of adversarial examples.
In particular, we highlight the importance of codimension: for low-dimensional
data manifolds embedded in high-dimensional space there are many directions off
the manifold in which to construct adversarial examples. Adversarial examples
are a natural consequence of learning a decision boundary that classifies the
low-dimensional data manifold well, but classifies points near the manifold
incorrectly. Using our geometric framework we prove (1) a tradeoff between
robustness under different norms, (2) that adversarial training in balls around
the data is sample inefficient, and (3) sufficient sampling conditions under
which nearest neighbor classifiers and ball-based adversarial training are
robust.
Minimizing Close-k Aggregate Loss Improves Classification
Authors: Bryan He, James Zou
Date: 2018-11-01 00:00:00
Tasks: Classification, General Classification

In classification, the de facto method for aggregating individual losses is
the average loss. When the actual metric of interest is 0-1 loss, it is common
to minimize the average surrogate loss for some well-behaved (e.g. convex)
surrogate. Recently, several other aggregate losses such as the maximal loss
and average top-$k$ loss were proposed as alternative objectives to address
shortcomings of the average loss. However, we identify common classification
settings, e.g. the data is imbalanced, has too many easy or ambiguous examples,
etc., when average, maximal and average top-$k$ all suffer from suboptimal
decision boundaries, even on an infinitely large training set. To address this
problem, we propose a new classification objective called the close-$k$
aggregate loss, where we adaptively minimize the loss for points close to the
decision boundary. We provide theoretical guarantees for the 0-1 accuracy when
we optimize close-$k$ aggregate loss. We also conduct systematic experiments
across the PMLB and OpenML benchmark datasets. Close-$k$ achieves significant
gains in 0-1 test accuracy, improvements of $\geq 2$% and $p<0.05$, in over 25%
of the datasets compared to average, maximal and average top-$k$. In contrast,
the previous aggregate losses outperformed close-$k$ in less than 2% of the
datasets.
Auditing Data Provenance in Text-Generation Models
Authors: Congzheng Song, Vitaly Shmatikov
Date: 2018-11-01 00:00:00
Tasks: Memorization, Text Generation

To help enforce data-protection regulations such as GDPR and detect unauthorized uses of personal data, we develop a new \emph{model auditing} technique that helps users check if their data was used to train a machine learning model. We focus on auditing deep-learning models that generate natural-language text, including word prediction and dialog generation. These models are at the core of popular online services and are often trained on personal data such as users' messages, searches, chats, and comments. We design and evaluate a black-box auditing method that can detect, with very few queries to a model, if a particular user's texts were used to train it (among thousands of other users). We empirically show that our method can successfully audit well-generalized models that are not overfitted to the training data. We also analyze how text-generation models memorize word sequences and explain why this memorization makes them amenable to auditing.
Learning Beam Search Policies via Imitation Learning
Authors: Renato Negrinho, Matthew R. Gormley, Geoffrey J. Gordon
Date: 2018-11-01 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Imitation Learning, Structured Prediction

Beam search is widely used for approximate decoding in structured prediction problems. Models often use a beam at test time but ignore its existence at train time, and therefore do not explicitly learn how to use the beam. We develop an unifying meta-algorithm for learning beam search policies using imitation learning. In our setting, the beam is part of the model, and not just an artifact of approximate decoding. Our meta-algorithm captures existing learning algorithms and suggests new ones. It also lets us show novel no-regret guarantees for learning beam search policies.
Towards Coherent and Cohesive Long-form Text Generation
Authors: Woon Sang Cho, Pengchuan Zhang, Yizhe Zhang, Xiujun Li, Michel Galley, Chris Brockett, Mengdi Wang, Jianfeng Gao
Date: 2018-11-01 00:00:00
Proceeding: WS 2019 6
Tasks: Form, Language Modeling, Language Modelling, Sentence, Text Generation

Generating coherent and cohesive long-form texts is a challenging task. Previous works relied on large amounts of human-generated texts to train neural language models. However, few attempted to explicitly improve neural language models from the perspectives of coherence and cohesion. In this work, we propose a new neural language model that is equipped with two neural discriminators which provide feedback signals at the levels of sentence (cohesion) and paragraph (coherence). Our model is trained using a simple yet efficient variant of policy gradient, called negative-critical sequence training, which is proposed to eliminate the need of training a separate critic for estimating baseline. Results demonstrate the effectiveness of our approach, showing improvements over the strong baseline -- recurrent attention-based bidirectional MLE-trained neural language model.
Navigation by Imitation in a Pedestrian-Rich Environment
Authors: Jing Bi, Tianyou Xiao, Qiuyue Sun, Chenliang Xu
Date: 2018-11-01 00:00:00
Tasks: Imitation Learning, Navigate

Deep neural networks trained on demonstrations of human actions give robot
the ability to perform self-driving on the road. However, navigation in a
pedestrian-rich environment, such as a campus setup, is still challenging---one
needs to take frequent interventions to the robot and take control over the
robot from early steps leading to a mistake. An arduous burden is, hence,
placed on the learning framework design and data acquisition. In this paper, we
propose a new learning-from-intervention Dataset Aggregation (DAgger) algorithm
to overcome the limitations brought by applying imitation learning to
navigation in the pedestrian-rich environment. Our new learning algorithm
implements an error backtrack function that is able to effectively learn from
expert interventions. Combining our new learning algorithm with deep
convolutional neural networks and a hierarchically-nested policy-selection
mechanism, we show that our robot is able to map pixels direct to control
commands and navigate successfully in real world without explicitly modeling
the pedestrian behaviors or the world model.
Improving CNN Training using Disentanglement for Liver Lesion Classification in CT
Authors: Avi Ben-Cohen, Roey Mechrez, Noa Yedidia, Hayit Greenspan
Date: 2018-11-01 00:00:00
Tasks: Disentanglement, General Classification, Image Generation, Lesion Classification, Medical Image Analysis

Training data is the key component in designing algorithms for medical image
analysis and in many cases it is the main bottleneck in achieving good results.
Recent progress in image generation has enabled the training of neural network
based solutions using synthetic data. A key factor in the generation of new
samples is controlling the important appearance features and potentially being
able to generate a new sample of a specific class with different variants. In
this work we suggest the synthesis of new data by mixing the class specified
and unspecified representation of different factors in the training data. Our
experiments on liver lesion classification in CT show an average improvement of
7.4% in accuracy over the baseline training scheme.
Multilingual NMT with a language-independent attention bridge
Authors: Raúl Vázquez, Alessandro Raganato, Jörg Tiedemann, Mathias Creutz
Date: 2018-11-01 00:00:00
Proceeding: WS 2019 8
Tasks: Decoder, NMT, Sentence, Transfer Learning, Translation

In this paper, we propose a multilingual encoder-decoder architecture capable
of obtaining multilingual sentence representations by means of incorporating an
intermediate {\em attention bridge} that is shared across all languages. That
is, we train the model with language-specific encoders and decoders that are
connected via self-attention with a shared layer that we call attention bridge.
This layer exploits the semantics from each language for performing translation
and develops into a language-independent meaning representation that can
efficiently be used for transfer learning. We present a new framework for the
efficient development of multilingual NMT using this model and scheduled
training. We have tested the approach in a systematic way with a multi-parallel
data set. We show that the model achieves substantial improvements over strong
bilingual models and that it also works well for zero-shot translation, which
demonstrates its ability of abstraction and transfer learning.
Modeling Attention Flow on Graphs
Authors: Xiaoran Xu, Songpeng Zu, Chengliang Gao, Yuan Zhang, Wei Feng
Date: 2018-11-01 00:00:00

Real-world scenarios demand reasoning about process, more than final outcome
prediction, to discover latent causal chains and better understand complex
systems. It requires the learning algorithms to offer both accurate predictions
and clear interpretations. We design a set of trajectory reasoning tasks on
graphs with only the source and the destination observed. We present the
attention flow mechanism to explicitly model the reasoning process, leveraging
the relational inductive biases by basing our models on graph networks. We
study the way attention flow can effectively act on the underlying information
flow implemented by message passing. Experiments demonstrate that the attention
flow driven by and interacting with graph networks can provide higher accuracy
in prediction and better interpretation for trajectory reasoning.
A Corpus for Reasoning About Natural Language Grounded in Photographs
Authors: Alane Suhr, Stephanie Zhou, Ally Zhang, Iris Zhang, Huajun Bai, Yoav Artzi
Date: 2018-11-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Diversity, Visual Reasoning

We introduce a new dataset for joint reasoning about natural language and images, with a focus on semantic diversity, compositionality, and visual reasoning challenges. The data contains 107,292 examples of English sentences paired with web photographs. The task is to determine whether a natural language caption is true about a pair of photographs. We crowdsource the data using sets of visually rich images and a compare-and-contrast task to elicit linguistically diverse language. Qualitative analysis shows the data requires compositional joint reasoning, including about quantities, comparisons, and relations. Evaluation using state-of-the-art visual reasoning methods shows the data presents a strong challenge.
Hybrid Pruning: Thinner Sparse Networks for Fast Inference on Edge Devices
Authors: Xiaofan Xu, Mi Sun Park, Cormac Brick
Date: 2018-11-01 00:00:00
Proceeding: NIPS Workshop CDNNRIA 2018
Tasks: Sensitivity

We introduce hybrid pruning which combines both coarse-grained channel and
fine-grained weight pruning to reduce model size, computation and power demands
with no to little loss in accuracy for enabling modern networks deployment on
resource-constrained devices, such as always-on security cameras and drones.
Additionally, to effectively perform channel pruning, we propose a fast
sensitivity test that helps us quickly identify the sensitivity of within and
across layers of a network to the output accuracy for target multiplier
accumulators (MACs) or accuracy tolerance. Our experiment shows significantly
better results on ResNet50 on ImageNet compared to existing work, even with an
additional constraint of channels be hardware-friendly number.
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis
Authors: Gabriel B. Cavallari, Leonardo Sampaio Ferraz Ribeiro, Moacir Antonelli Ponti
Date: 2018-11-01 00:00:00
Tasks: Dimensionality Reduction, Representation Learning

A feature learning task involves training models that are capable of
inferring good representations (transformations of the original space) from
input data alone. When working with limited or unlabelled data, and also when
multiple visual domains are considered, methods that rely on large annotated
datasets, such as Convolutional Neural Networks (CNNs), cannot be employed. In
this paper we investigate different auto-encoder (AE) architectures, which
require no labels, and explore training strategies to learn representations
from images. The models are evaluated considering both the reconstruction error
of the images and the feature spaces in terms of their discriminative power. We
study the role of dense and convolutional layers on the results, as well as the
depth and capacity of the networks, since those are shown to affect both the
dimensionality reduction and the capability of generalising for different
visual domains. Classification results with AE features were as discriminative
as pre-trained CNN features. Our findings can be used as guidelines for the
design of unsupervised representation learning methods within and across
domains.
Class-Agnostic Counting
Authors: Erika Lu, Weidi Xie, Andrew Zisserman
Date: 2018-11-01 00:00:00
Tasks: Crowd Counting, Few-Shot Learning, Object, Object Counting

Nearly all existing counting methods are designed for a specific object
class. Our work, however, aims to create a counting model able to count any
class of object. To achieve this goal, we formulate counting as a matching
problem, enabling us to exploit the image self-similarity property that
naturally exists in object counting problems. We make the following three
contributions: first, a Generic Matching Network (GMN) architecture that can
potentially count any object in a class-agnostic manner; second, by
reformulating the counting problem as one of matching objects, we can take
advantage of the abundance of video data labeled for tracking, which contains
natural repetitions suitable for training a counting model. Such data enables
us to train the GMN. Third, to customize the GMN to different user
requirements, an adapter module is used to specialize the model with minimal
effort, i.e. using a few labeled examples, and adapting only a small fraction
of the trained parameters. This is a form of few-shot learning, which is
practical for domains where labels are limited due to requiring expert
knowledge (e.g. microbiology). We demonstrate the flexibility of our method on
a diverse set of existing counting benchmarks: specifically cells, cars, and
human crowds. The model achieves competitive performance on cell and crowd
counting datasets, and surpasses the state-of-the-art on the car dataset using
only three training images. When training on the entire dataset, the proposed
method outperforms all previous methods by a large margin.
A latent topic model for mining heterogenous non-randomly missing electronic health records data
Authors: Yue Li, Manolis Kellis
Date: 2018-11-01 00:00:00
Tasks: Collaborative Filtering, Topic Models

Electronic health records (EHR) are rich heterogeneous collection of patient
health information, whose broad adoption provides great opportunities for
systematic health data mining. However, heterogeneous EHR data types and biased
ascertainment impose computational challenges. Here, we present mixEHR, an
unsupervised generative model integrating collaborative filtering and latent
topic models, which jointly models the discrete distributions of data
observation bias and actual data using latent disease-topic distributions. We
apply mixEHR on 12.8 million phenotypic observations from the MIMIC dataset,
and use it to reveal latent disease topics, interpret EHR results, impute
missing data, and predict mortality in intensive care units. Using both
simulation and real data, we show that mixEHR outperforms previous methods and
reveals meaningful multi-disease insights.
Bias Reduction via End-to-End Shift Learning: Application to Citizen Science
Authors: Di Chen, Carla P. Gomes
Date: 2018-11-01 00:00:00

Citizen science projects are successful at gathering rich datasets for
various applications. However, the data collected by citizen scientists are
often biased --- in particular, aligned more with the citizens' preferences
than with scientific objectives. We propose the Shift Compensation Network
(SCN), an end-to-end learning scheme which learns the shift from the scientific
objectives to the biased data while compensating for the shift by re-weighting
the training data. Applied to bird observational data from the citizen science
project eBird, we demonstrate how SCN quantifies the data distribution shift
and outperforms supervised learning models that do not address the data bias.
Compared with competing models in the context of covariate shift, we further
demonstrate the advantage of SCN in both its effectiveness and its capability
of handling massive high-dimensional data.
Referenceless Performance Evaluation of Audio Source Separation using Deep Neural Networks
Authors: Emad M. Grais, Hagen Wierstorf, Dominic Ward, Russell Mason, Mark D. Plumbley
Date: 2018-11-01 00:00:00
Tasks: Audio Source Separation, blind source separation

Current performance evaluation for audio source separation depends on
comparing the processed or separated signals with reference signals. Therefore,
common performance evaluation toolkits are not applicable to real-world
situations where the ground truth audio is unavailable. In this paper, we
propose a performance evaluation technique that does not require reference
signals in order to assess separation quality. The proposed technique uses a
deep neural network (DNN) to map the processed audio into its quality score.
Our experiment results show that the DNN is capable of predicting the
sources-to-artifacts ratio from the blind source separation evaluation toolkit
without the need for reference signals.
CariGAN: Caricature Generation through Weakly Paired Adversarial Learning
Authors: Wenbin Li, Wei Xiong, Haofu Liao, Jing Huo, Yang Gao, Jiebo Luo
Date: 2018-11-01 00:00:00
Tasks: Caricature, Diversity

Caricature generation is an interesting yet challenging task. The primary
goal is to generate plausible caricatures with reasonable exaggerations given
face images. Conventional caricature generation approaches mainly use low-level
geometric transformations such as image warping to generate exaggerated images,
which lack richness and diversity in terms of content and style. The recent
progress in generative adversarial networks (GANs) makes it possible to learn
an image-to-image transformation from data, so that richer contents and styles
can be generated. However, directly applying the GAN-based models to this task
leads to unsatisfactory results because there is a large variance in the
caricature distribution. Moreover, some models require strictly paired training
data which largely limits their usage scenarios. In this paper, we propose
CariGAN overcome these problems. Instead of training on paired data, CariGAN
learns transformations only from weakly paired images. Specifically, to enforce
reasonable exaggeration and facial deformation, facial landmarks are adopted as
an additional condition to constrain the generated image. Furthermore, an
attention mechanism is introduced to encourage our model to focus on the key
facial parts so that more vivid details in these regions can be generated.
Finally, a Diversity Loss is proposed to encourage the model to produce diverse
results to help alleviate the `mode collapse' problem of the conventional
GAN-based models. Extensive experiments on a new large-scale `WebCaricature'
dataset show that the proposed CariGAN can generate more plausible caricatures
with larger diversity compared with the state-of-the-art models.
An Improved Learning Framework for Covariant Local Feature Detection
Authors: Nehal Doiphode, Rahul Mitra, Shuaib Ahmed, Arjun Jain
Date: 2018-11-01 00:00:00

Learning feature detection has been largely an unexplored area when compared
to handcrafted feature detection. Recent learning formulations use the
covariant constraint in their loss function to learn covariant detectors.
However, just learning from covariant constraint can lead to detection of
unstable features. To impart further, stability detec- tors are trained to
extract pre-determined features obtained by hand- crafted detectors. However,
in the process they lose the ability to detect novel features. In an attempt to
overcome the above limitations, we pro- pose an improved scheme by
incorporating covariant constraints in form of triplets with addition to an
affine covariant constraint. We show that using these additional constraints
one can learn to detect novel and sta- ble features without using
pre-determined features for training. Extensive experiments show our model
achieves state-of-the-art performance in re- peatability score on the well
known datasets such as Vgg-Affine, EF, and Webcam.
Unsupervised Dual-Cascade Learning with Pseudo-Feedback Distillation for Query-based Extractive Summarization
Authors: Haggai Roitman, Guy Feigenblat, David Konopnicki, Doron Cohen, Odellia Boni
Date: 2018-11-01 00:00:00
Tasks: Extractive Summarization, Query-Based Extractive Summarization

We propose Dual-CES -- a novel unsupervised, query-focused, multi-document
extractive summarizer. Dual-CES is designed to better handle the tradeoff
between saliency and focus in summarization. To this end, Dual-CES employs a
two-step dual-cascade optimization approach with saliency-based pseudo-feedback
distillation. Overall, Dual-CES significantly outperforms all other
state-of-the-art unsupervised alternatives. Dual-CES is even shown to be able
to outperform strong supervised summarizers.
Temporal Regularization in Markov Decision Process
Authors: Pierre Thodoroff, Audrey Durand, Joelle Pineau, Doina Precup
Date: 2018-11-01 00:00:00
Tasks: Atari Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Several applications of Reinforcement Learning suffer from instability due to
high variance. This is especially prevalent in high dimensional domains.
Regularization is a commonly used technique in machine learning to reduce
variance, at the cost of introducing some bias. Most existing regularization
techniques focus on spatial (perceptual) regularization. Yet in reinforcement
learning, due to the nature of the Bellman equation, there is an opportunity to
also exploit temporal regularization based on smoothness in value estimates
over trajectories. This paper explores a class of methods for temporal
regularization. We formally characterize the bias induced by this technique
using Markov chain concepts. We illustrate the various characteristics of
temporal regularization via a sequence of simple discrete and continuous MDPs,
and show that the technique provides improvement even in high-dimensional Atari
games.