pwc-archive/papers-with-abstracts huggingface dataset:
Local Rule-Based Explanations of Black Box Decision Systems
Authors: Riccardo Guidotti, Anna Monreale, Salvatore Ruggieri, Dino Pedreschi, Franco Turini, Fosca Giannotti
Date: 2018-05-28 00:00:00
Tasks: counterfactual

The recent years have witnessed the rise of accurate but obscure decision
systems which hide the logic of their internal decision processes to the users.
The lack of explanations for the decisions of black box systems is a key
ethical issue, and a limitation to the adoption of machine learning components
in socially sensitive and safety-critical contexts. %Therefore, we need
explanations that reveals the reasons why a predictor takes a certain decision.
In this paper we focus on the problem of black box outcome explanation, i.e.,
explaining the reasons of the decision taken on a specific instance. We propose
LORE, an agnostic method able to provide interpretable and faithful
explanations. LORE first leans a local interpretable predictor on a synthetic
neighborhood generated by a genetic algorithm. Then it derives from the logic
of the local interpretable predictor a meaningful explanation consisting of: a
decision rule, which explains the reasons of the decision; and a set of
counterfactual rules, suggesting the changes in the instance's features that
lead to a different outcome. Wide experiments show that LORE outperforms
existing methods and baselines both in the quality of explanations and in the
accuracy in mimicking the black box.
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid Flow
Authors: You Xie, Erik Franz, Mengyu Chu, Nils Thuerey
Date: 2018-01-29 00:00:00
Tasks: Data Augmentation, Generative Adversarial Network, Super-Resolution

We propose a temporally coherent generative model addressing the
super-resolution problem for fluid flows. Our work represents a first approach
to synthesize four-dimensional physics fields with neural networks. Based on a
conditional generative adversarial network that is designed for the inference
of three-dimensional volumetric data, our model generates consistent and
detailed results by using a novel temporal discriminator, in addition to the
commonly used spatial one. Our experiments show that the generator is able to
infer more realistic high-resolution details by using additional physical
quantities, such as low-resolution velocities or vorticities. Besides
improvements in the training process and in the generated outputs, these inputs
offer means for artistic control as well. We additionally employ a
physics-aware data augmentation step, which is crucial to avoid overfitting and
to reduce memory requirements. In this way, our network learns to generate
advected quantities with highly detailed, realistic, and temporally coherent
features. Our method works instantaneously, using only a single time-step of
low-resolution fluid data. We demonstrate the abilities of our method using a
variety of complex inputs and applications in two and three dimensions.
GPGPU Linear Complexity t-SNE Optimization
Authors: Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev, Thomas Hollt, Baldur van Lew, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova
Date: 2018-05-28 00:00:00

The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data. tSNE reveals clusters of high-dimensional data points at different scales while it requires only minimal tuning of its parameters. Despite these advantages, the computational complexity of the algorithm limits its application to relatively small datasets. To address this problem, several evolutions of tSNE have been developed in recent years, mainly focusing on the scalability of the similarity computations between data points. However, these contributions are insufficient to achieve interactive rates when visualizing the evolution of the tSNE embedding for large datasets. In this work, we present a novel approach to the minimization of the tSNE objective function that heavily relies on modern graphics hardware and has linear computational complexity. Our technique does not only beat the state of the art, but can even be executed on the client side in a browser. We propose to approximate the repulsion forces between data points using adaptive-resolution textures that are drawn at every iteration with WebGL. This approximation allows us to reformulate the tSNE minimization problem as a series of tensor operation that are computed with TensorFlow.js, a JavaScript library for scalable tensor computations.
A Renewal Model of Intrusion
Authors: David Tolpin
Date: 2017-09-24 00:00:00
Tasks: Intrusion Detection, model

We present a probabilistic model of an intrusion in a renewal process. Given
a process and a sequence of events, an intrusion is a subsequence of events
that is not produced by the process. Applications of the model are, for
example, online payment fraud with the fraudster taking over a user's account
and performing payments on the user's behalf, or unexpected equipment failures
due to unintended use.
  We adopt Bayesian approach to infer the probability of an intrusion in a
sequence of events, a MAP subsequence of events constituting the intrusion, and
the marginal probability of each event in a sequence to belong to the
intrusion. We evaluate the model for intrusion detection on synthetic data and
on anonymized data from an online payment system.
On Formalizing Fairness in Prediction with Machine Learning
Authors: Pratik Gajane, Mykola Pechenizkiy
Date: 2017-10-09 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, Prediction

Machine learning algorithms for prediction are increasingly being used in
critical decisions affecting human lives. Various fairness formalizations, with
no firm consensus yet, are employed to prevent such algorithms from
systematically discriminating against people based on certain attributes
protected by law. The aim of this article is to survey how fairness is
formalized in the machine learning literature for the task of prediction and
present these formalizations with their corresponding notions of distributive
justice from the social sciences literature. We provide theoretical as well as
empirical critiques of these notions from the social sciences literature and
explain how these critiques limit the suitability of the corresponding fairness
formalizations to certain domains. We also suggest two notions of distributive
justice which address some of these critiques and discuss avenues for
prospective fairness formalizations.
Fast Dynamic Routing Based on Weighted Kernel Density Estimation
Authors: Suofei Zhang, Wei Zhao, Xiaofu Wu, Quan Zhou
Date: 2018-05-28 00:00:00
Tasks: Density Estimation, Image Classification

Capsules as well as dynamic routing between them are most recently proposed
structures for deep neural networks. A capsule groups data into vectors or
matrices as poses rather than conventional scalars to represent specific
properties of target instance. Besides of pose, a capsule should be attached
with a probability (often denoted as activation) for its presence. The dynamic
routing helps capsules achieve more generalization capacity with many fewer
model parameters. However, the bottleneck that prevents widespread applications
of capsule is the expense of computation during routing. To address this
problem, we generalize existing routing methods within the framework of
weighted kernel density estimation, and propose two fast routing methods with
different optimization strategies. Our methods prompt the time efficiency of
routing by nearly 40\% with negligible performance degradation. By stacking a
hybrid of convolutional layers and capsule layers, we construct a network
architecture to handle inputs at a resolution of $64\times{64}$ pixels. The
proposed models achieve a parallel performance with other leading methods in
multiple benchmarks.
Visual Relationship Detection Based on Guided Proposals and Semantic Knowledge Distillation
Authors: François Plesse, Alexandru Ginsca, Bertrand Delezoide, Françoise Prêteux
Date: 2018-05-28 00:00:00
Tasks: Common Sense Reasoning, Knowledge Distillation, Relationship Detection, Visual Relationship Detection

A thorough comprehension of image content demands a complex grasp of the
interactions that may occur in the natural world. One of the key issues is to
describe the visual relationships between objects. When dealing with real world
data, capturing these very diverse interactions is a difficult problem. It can
be alleviated by incorporating common sense in a network. For this, we propose
a framework that makes use of semantic knowledge and estimates the relevance of
object pairs during both training and test phases. Extracted from precomputed
models and training annotations, this information is distilled into the neural
network dedicated to this task. Using this approach, we observe a significant
improvement on all classes of Visual Genome, a challenging visual relationship
dataset. A 68.5% relative gain on the recall at 100 is directly related to the
relevance estimate and a 32.7% gain to the knowledge distillation.
OpenNMT: Neural Machine Translation Toolkit
Authors: Guillaume Klein, Yoon Kim, Yuntian Deng, Vincent Nguyen, Jean Senellart, Alexander M. Rush
Date: 2018-05-28 00:00:00
Proceeding: WS 2018 3
Tasks: Machine Translation, NMT, Translation

OpenNMT is an open-source toolkit for neural machine translation (NMT). The
system prioritizes efficiency, modularity, and extensibility with the goal of
supporting NMT research into model architectures, feature representations, and
source modalities, while maintaining competitive performance and reasonable
training requirements. The toolkit consists of modeling and translation
support, as well as detailed pedagogical documentation about the underlying
techniques. OpenNMT has been used in several production MT systems, modified
for numerous research papers, and is implemented across several deep learning
frameworks.
Interactive Text2Pickup Network for Natural Language based Human-Robot Collaboration
Authors: Hyemin Ahn, Sungjoon Choi, Nuri Kim, Geonho Cha, Songhwai Oh
Date: 2018-05-28 00:00:00
Tasks: Object, Position

In this paper, we propose the Interactive Text2Pickup (IT2P) network for
human-robot collaboration which enables an effective interaction with a human
user despite the ambiguity in user's commands. We focus on the task where a
robot is expected to pick up an object instructed by a human, and to interact
with the human when the given instruction is vague. The proposed network
understands the command from the human user and estimates the position of the
desired object first. To handle the inherent ambiguity in human language
commands, a suitable question which can resolve the ambiguity is generated. The
user's answer to the question is combined with the initial command and given
back to the network, resulting in more accurate estimation. The experiment
results show that given unambiguous commands, the proposed method can estimate
the position of the requested object with an accuracy of 98.49% based on our
test dataset. Given ambiguous language commands, we show that the accuracy of
the pick up task increases by 1.94 times after incorporating the information
obtained from the interaction.
Convolutional neural network compression for natural language processing
Authors: Krzysztof Wróbel, Marcin Pietroń, Maciej Wielgosz, Michał Karwatowski, Kazimierz Wiatr
Date: 2018-05-28 00:00:00
Tasks: Neural Network Compression, Quantization, Sentiment Analysis

Convolutional neural networks are modern models that are very efficient in
many classification tasks. They were originally created for image processing
purposes. Then some trials were performed to use them in different domains like
natural language processing. The artificial intelligence systems (like humanoid
robots) are very often based on embedded systems with constraints on memory,
power consumption etc. Therefore convolutional neural network because of its
memory capacity should be reduced to be mapped to given hardware. In this
paper, results are presented of compressing the efficient convolutional neural
networks for sentiment analysis. The main steps are quantization and pruning
processes. The method responsible for mapping compressed network to FPGA and
results of this implementation are presented. The described simulations showed
that 5-bit width is enough to have no drop in accuracy from floating point
version of the network. Additionally, significant memory footprint reduction
was achieved (from 85% up to 93%).
Deep Discriminative Latent Space for Clustering
Authors: Elad Tzoreff, Olga Kogan, Yoni Choukroun
Date: 2018-05-28 00:00:00
Tasks: Clustering

Clustering is one of the most fundamental tasks in data analysis and machine
learning. It is central to many data-driven applications that aim to separate
the data into groups with similar patterns. Moreover, clustering is a complex
procedure that is affected significantly by the choice of the data
representation method. Recent research has demonstrated encouraging clustering
results by learning effectively these representations. In most of these works a
deep auto-encoder is initially pre-trained to minimize a reconstruction loss,
and then jointly optimized with clustering centroids in order to improve the
clustering objective. Those works focus mainly on the clustering phase of the
procedure, while not utilizing the potential benefit out of the initial phase.
In this paper we propose to optimize an auto-encoder with respect to a
discriminative pairwise loss function during the auto-encoder pre-training
phase. We demonstrate the high accuracy obtained by the proposed method as well
as its rapid convergence (e.g. reaching above 92% accuracy on MNIST during the
pre-training phase, in less than 50 epochs), even with small networks.
Deep CT to MR Synthesis using Paired and Unpaired Data
Authors: Cheng-Bin Jin, Hakil Kim, Wonmo Jung, Seongsu Joo, Ensik Park, Ahn Young Saem, In Ho Han, Jae Il Lee, Xuenan Cui
Date: 2018-05-28 00:00:00
Tasks: Generative Adversarial Network

MR imaging will play a very important role in radiotherapy treatment planning
for segmentation of tumor volumes and organs. However, the use of MR-based
radiotherapy is limited because of the high cost and the increased use of metal
implants such as cardiac pacemakers and artificial joints in aging society. To
improve the accuracy of CT-based radiotherapy planning, we propose a synthetic
approach that translates a CT image into an MR image using paired and unpaired
training data. In contrast to the current synthetic methods for medical images,
which depend on sparse pairwise-aligned data or plentiful unpaired data, the
proposed approach alleviates the rigid registration challenge of paired
training and overcomes the context-misalignment problem of the unpaired
training. A generative adversarial network was trained to transform 2D brain CT
image slices into 2D brain MR image slices, combining adversarial loss, dual
cycle-consistent loss, and voxel-wise loss. The experiments were analyzed using
CT and MR images of 202 patients. Qualitative and quantitative comparisons
against independent paired training and unpaired training methods demonstrate
the superiority of our approach.
DNN or k-NN: That is the Generalize vs. Memorize Question
Authors: Gilad Cohen, Guillermo Sapiro, Raja Giryes
Date: 2018-05-17 00:00:00
Tasks: Memorization

This paper studies the relationship between the classification performed by
deep neural networks (DNNs) and the decision of various classical classifiers,
namely k-nearest neighbours (k-NN), support vector machines (SVM) and logistic
regression (LR), at various layers of the network. This comparison provides us
with new insights as to the ability of neural networks to both memorize the
training data and generalize to new data at the same time, where k-NN serves as
the ideal estimator that perfectly memorizes the data. We show that
memorization of non-generalizing networks happens only at the last layers.
Moreover, the behavior of DNNs compared to the linear classifiers SVM and LR is
quite the same on the training and test data regardless of whether the network
generalizes. On the other hand, the similarity to k-NN holds only at the
absence of overfitting. Our results suggests that k-NN behavior of the network
on new data is a sign of generalization. Moreover, it shows that memorization
and generalization, which are traditionally considered to be contradicting to
each other, are compatible and complementary.
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Authors: Moustafa Alzantot, Yash Sharma, Supriyo Chakraborty, huan zhang, Cho-Jui Hsieh, Mani Srivastava
Date: 2018-05-28 00:00:00
Tasks: Adversarial Attack, Adversarial Robustness, Evolutionary Algorithms

Deep neural networks are vulnerable to adversarial examples, even in the black-box setting, where the attacker is restricted solely to query access. Existing black-box approaches to generating adversarial examples typically require a significant number of queries, either for training a substitute network or performing gradient estimation. We introduce GenAttack, a gradient-free optimization technique that uses genetic algorithms for synthesizing adversarial examples in the black-box setting. Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches. Against MNIST and CIFAR-10 models, GenAttack required roughly 2,126 and 2,568 times fewer queries respectively, than ZOO, the prior state-of-the-art black-box attack. In order to scale up the attack to large-scale high-dimensional ImageNet models, we perform a series of optimizations that further improve the query efficiency of our attack leading to 237 times fewer queries against the Inception-v3 model than ZOO. Furthermore, we show that GenAttack can successfully attack some state-of-the-art ImageNet defenses, including ensemble adversarial training and non-differentiable or randomized input transformations. Our results suggest that evolutionary algorithms open up a promising area of research into effective black-box attacks.
Adaptive Scaling for Sparse Detection in Information Extraction
Authors: Hongyu Lin, Yaojie Lu, Xianpei Han, Le Sun
Date: 2018-05-01 00:00:00
Proceeding: ACL 2018 7

This paper focuses on detection tasks in information extraction, where
positive instances are sparsely distributed and models are usually evaluated
using F-measure on positive classes. These characteristics often result in
deficient performance of neural network based detection models. In this paper,
we propose adaptive scaling, an algorithm which can handle the positive
sparsity problem and directly optimize over F-measure via dynamic
cost-sensitive learning. To this end, we borrow the idea of marginal utility
from economics and propose a theoretical framework for instance importance
measuring without introducing any additional hyper-parameters. Experiments show
that our algorithm leads to a more effective and stable training of neural
network based detection models.
Keep and Learn: Continual Learning by Constraining the Latent Space for Knowledge Preservation in Neural Networks
Authors: Hyo-Eun Kim, SeungWook Kim, Jaehwan Lee
Date: 2018-05-28 00:00:00
Tasks: Continual Learning

Data is one of the most important factors in machine learning. However, even
if we have high-quality data, there is a situation in which access to the data
is restricted. For example, access to the medical data from outside is strictly
limited due to the privacy issues. In this case, we have to learn a model
sequentially only with the data accessible in the corresponding stage. In this
work, we propose a new method for preserving learned knowledge by modeling the
high-level feature space and the output space to be mutually informative, and
constraining feature vectors to lie in the modeled space during training. The
proposed method is easy to implement as it can be applied by simply adding a
reconstruction loss to an objective function. We evaluate the proposed method
on CIFAR-10/100 and a chest X-ray dataset, and show benefits in terms of
knowledge preservation compared to previous approaches.
Strength Factors: An Uncertainty System for a Quantified Modal Logic
Authors: Naveen Sundar Govindarajulu, Selmer Bringsjord
Date: 2017-05-30 00:00:00
Tasks: counterfactual

We present a new system S for handling uncertainty in a quantified modal
logic (first-order modal logic). The system is based on both probability theory
and proof theory. The system is derived from Chisholm's epistemology. We
concretize Chisholm's system by grounding his undefined and primitive (i.e.
foundational) concept of reasonablenes in probability and proof theory. S can
be useful in systems that have to interact with humans and provide
justifications for their uncertainty. As a demonstration of the system, we
apply the system to provide a solution to the lottery paradox. Another
advantage of the system is that it can be used to provide uncertainty values
for counterfactual statements. Counterfactuals are statements that an agent
knows for sure are false. Among other cases, counterfactuals are useful when
systems have to explain their actions to users. Uncertainties for
counterfactuals fall out naturally from our system.
  Efficient reasoning in just simple first-order logic is a hard problem.
Resolution-based first-order reasoning systems have made significant progress
over the last several decades in building systems that have solved non-trivial
tasks (even unsolved conjectures in mathematics). We present a sketch of a
novel algorithm for reasoning that extends first-order resolution.
  Finally, while there have been many systems of uncertainty for propositional
logics, first-order logics and propositional modal logics, there has been very
little work in building systems of uncertainty for first-order modal logics.
The work described below is in progress; and once finished will address this
lack.
Object-Level Representation Learning for Few-Shot Image Classification
Authors: Liangqu Long, Wei Wang, Jun Wen, Meihui Zhang, Qian Lin, Beng Chin Ooi
Date: 2018-05-28 00:00:00
Tasks: Classification, Few-Shot Image Classification, Few-Shot Learning, General Classification, image-classification, Image Classification, Representation Learning

Few-shot learning that trains image classifiers over few labeled examples per
category is a challenging task. In this paper, we propose to exploit an
additional big dataset with different categories to improve the accuracy of
few-shot learning over our target dataset. Our approach is based on the
observation that images can be decomposed into objects, which may appear in
images from both the additional dataset and our target dataset. We use the
object-level relation learned from the additional dataset to infer the
similarity of images in our target dataset with unseen categories. Nearest
neighbor search is applied to do image classification, which is a
non-parametric model and thus does not need fine-tuning. We evaluate our
algorithm on two popular datasets, namely Omniglot and MiniImagenet. We obtain
8.5\% and 2.7\% absolute improvements for 5-way 1-shot and 5-way 5-shot
experiments on MiniImagenet, respectively. Source code will be published upon
acceptance.