pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Authors: Oscar Li, Hao liu, Chaofan Chen, Cynthia Rudin
Date: 2017-10-13 00:00:00
Tasks: Decoder, General Classification

Deep neural networks are widely used for classification. These deep models
often suffer from a lack of interpretability -- they are particularly difficult
to understand because of their non-linear nature. As a result, neural networks
are often treated as "black box" models, and in the past, have been trained
purely to optimize the accuracy of predictions. In this work, we create a novel
network architecture for deep learning that naturally explains its own
reasoning for each prediction. This architecture contains an autoencoder and a
special prototype layer, where each unit of that layer stores a weight vector
that resembles an encoded training input. The encoder of the autoencoder allows
us to do comparisons within the latent space, while the decoder allows us to
visualize the learned prototypes. The training objective has four terms: an
accuracy term, a term that encourages every prototype to be similar to at least
one encoded input, a term that encourages every encoded input to be close to at
least one prototype, and a term that encourages faithful reconstruction by the
autoencoder. The distances computed in the prototype layer are used as part of
the classification process. Since the prototypes are learned during training,
the learned network naturally comes with explanations for each prediction, and
the explanations are loyal to what the network actually computes.
A deep learning-based method for relative location prediction in CT scan images
Authors: Jiajia Guo, Hongwei Du, Bensheng Qiu, Xiao Liang
Date: 2017-11-21 00:00:00
Tasks: Computed Tomography (CT), regression

Relative location prediction in computed tomography (CT) scan images is a
challenging problem. In this paper, a regression model based on one-dimensional
convolutional neural networks is proposed to determine the relative location of
a CT scan image both robustly and precisely. A public dataset is employed to
validate the performance of the study's proposed method using a 5-fold cross
validation. Experimental results demonstrate an excellent performance of the
proposed model when compared with the state-of-the-art techniques, achieving a
median absolute error of 1.04 cm and mean absolute error of 1.69 cm.
Groupwise Maximin Fair Allocation of Indivisible Goods
Authors: Siddharth Barman, Arpita Biswas, Sanath Kumar Krishnamurthy, Y. Narahari
Date: 2017-11-21 00:00:00
Tasks: Fairness

We study the problem of allocating indivisible goods among n agents in a fair
manner. For this problem, maximin share (MMS) is a well-studied solution
concept which provides a fairness threshold. Specifically, maximin share is
defined as the minimum utility that an agent can guarantee for herself when
asked to partition the set of goods into n bundles such that the remaining
(n-1) agents pick their bundles adversarially. An allocation is deemed to be
fair if every agent gets a bundle whose valuation is at least her maximin
share.
  Even though maximin shares provide a natural benchmark for fairness, it has
its own drawbacks and, in particular, it is not sufficient to rule out
unsatisfactory allocations. Motivated by these considerations, in this work we
define a stronger notion of fairness, called groupwise maximin share guarantee
(GMMS). In GMMS, we require that the maximin share guarantee is achieved not
just with respect to the grand bundle, but also among all the subgroups of
agents. Hence, this solution concept strengthens MMS and provides an ex-post
fairness guarantee. We show that in specific settings, GMMS allocations always
exist. We also establish the existence of approximate GMMS allocations under
additive valuations, and develop a polynomial-time algorithm to find such
allocations. Moreover, we establish a scale of fairness wherein we show that
GMMS implies approximate envy freeness.
  Finally, we empirically demonstrate the existence of GMMS allocations in a
large set of randomly generated instances. For the same set of instances, we
additionally show that our algorithm achieves an approximation factor better
than the established, worst-case bound.
S4Net: Single Stage Salient-Instance Segmentation
Authors: Ruochen Fan, Ming-Ming Cheng, Qibin Hou, Tai-Jiang Mu, Jingdong Wang, Shi-Min Hu
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We consider an interesting problem-salient instance segmentation in this
paper. Other than producing bounding boxes, our network also outputs
high-quality instance-level segments. Taking into account the
category-independent property of each target, we design a single stage salient
instance segmentation framework, with a novel segmentation branch. Our new
branch regards not only local context inside each detection window but also its
surrounding context, enabling us to distinguish the instances in the same scope
even with obstruction. Our network is end-to-end trainable and runs at a fast
speed (40 fps when processing an image with resolution 320x320). We evaluate
our approach on a publicly available benchmark and show that it outperforms
other alternative solutions. We also provide a thorough analysis of the design
choices to help readers better understand the functions of each part of our
network. The source code can be found at
\url{https://github.com/RuochenFan/S4Net}.
Deep Semi-Random Features for Nonlinear Function Approximation
Authors: Kenji Kawaguchi, Bo Xie, Vikas Verma, Le Song
Date: 2017-02-28 00:00:00

We propose semi-random features for nonlinear function approximation. The
flexibility of semi-random feature lies between the fully adjustable units in
deep learning and the random features used in kernel methods. For one hidden
layer models with semi-random features, we prove with no unrealistic
assumptions that the model classes contain an arbitrarily good function as the
width increases (universality), and despite non-convexity, we can find such a
good function (optimization theory) that generalizes to unseen new data
(generalization bound). For deep models, with no unrealistic assumptions, we
prove universal approximation ability, a lower bound on approximation error, a
partial optimization guarantee, and a generalization bound. Depending on the
problems, the generalization bound of deep semi-random features can be
exponentially better than the known bounds of deep ReLU nets; our
generalization error bound can be independent of the depth, the number of
trainable weights as well as the input dimensionality. In experiments, we show
that semi-random features can match the performance of neural networks by using
slightly more units, and it outperforms random features by using significantly
fewer units. Moreover, we introduce a new implicit ensemble method by using
semi-random features.
Finding Algebraic Structure of Care in Time: A Deep Learning Approach
Authors: Phuoc Nguyen, Truyen Tran, Svetha Venkatesh
Date: 2017-11-21 00:00:00

Understanding the latent processes from Electronic Medical Records could be a
game changer in modern healthcare. However, the processes are complex due to
the interaction between at least three dynamic components: the illness, the
care and the recording practice. Existing methods are inadequate in capturing
the dynamic structure of care. We propose an end-to-end model that reads
medical record and predicts future risk. The model adopts the algebraic view in
that discrete medical objects are embedded into continuous vectors lying in the
same space. The bag of disease and comorbidities recorded at each hospital
visit are modeled as function of sets. The same holds for the bag of
treatments. The interaction between diseases and treatments at a visit is
modeled as the residual of the diseases minus the treatments. Finally, the
health trajectory, which is a sequence of visits, is modeled using a recurrent
neural network. We report preliminary results on chronic diseases - diabetes
and mental health - for predicting unplanned readmission.
FLIC: Fast Linear Iterative Clustering with Active Search
Authors: Jia-Xing Zhao, Ren Bo, Qibin Hou, Ming-Ming Cheng, Paul L. Rosin
Date: 2016-12-06 00:00:00
Tasks: Clustering, CPU, Segmentation

Benefiting from its high efficiency and simplicity, Simple Linear Iterative
Clustering (SLIC) remains one of the most popular over-segmentation tools.
However, due to explicit enforcement of spatial similarity for region
continuity, the boundary adaptation of SLIC is sub-optimal. It also has
drawbacks on convergence rate as a result of both the fixed search region and
separately doing the assignment step and the update step. In this paper, we
propose an alternative approach to fix the inherent limitations of SLIC. In our
approach, each pixel actively searches its corresponding segment under the help
of its neighboring pixels, which naturally enables region coherence without
being harmful to boundary adaptation. We also jointly perform the assignment
and update steps, allowing high convergence rate. Extensive evaluations on
Berkeley segmentation benchmark verify that our method outperforms competitive
methods under various evaluation metrics. It also has the lowest time cost
among existing methods (approximately 30fps for a 481x321 image on a single CPU
core).
A Double Parametric Bootstrap Test for Topic Models
Authors: Skyler Seto, Sarah Tan, Giles Hooker, Martin T. Wells
Date: 2017-11-19 00:00:00
Tasks: Topic Models

Non-negative matrix factorization (NMF) is a technique for finding latent
representations of data. The method has been applied to corpora to construct
topic models. However, NMF has likelihood assumptions which are often violated
by real document corpora. We present a double parametric bootstrap test for
evaluating the fit of an NMF-based topic model based on the duality of the KL
divergence and Poisson maximum likelihood estimation. The test correctly
identifies whether a topic model based on an NMF approach yields reliable
results in simulated and real data.
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards
Authors: Jun-Jie Zhang, Qi Wu, Chunhua Shen, Jian Zhang, Jianfeng Lu, Anton Van Den Hengel
Date: 2017-11-21 00:00:00
Tasks: Deep Reinforcement Learning, Informativeness, Question Generation, Question-Generation, Reinforcement Learning, Reinforcement Learning (RL)

Despite significant progress in a variety of vision-and-language problems,
developing a method capable of asking intelligent, goal-oriented questions
about images is proven to be an inscrutable challenge. Towards this end, we
propose a Deep Reinforcement Learning framework based on three new intermediate
rewards, namely goal-achieved, progressive and informativeness that encourage
the generation of succinct questions, which in turn uncover valuable
information towards the overall goal. By directly optimizing for questions that
work quickly towards fulfilling the overall goal, we avoid the tendency of
existing methods to generate long series of insane queries that add little
value. We evaluate our model on the GuessWhat?! dataset and show that the
resulting questions can help a standard Guesser identify a specific object in
an image at a much higher success rate.
Are You Talking to Me? Reasoned Visual Dialog Generation through Adversarial Learning
Authors: Qi Wu, Peng Wang, Chunhua Shen, Ian Reid, Anton Van Den Hengel
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Reinforcement Learning, Visual Dialog, Visual Question Answering, Visual Question Answering (VQA)

The Visual Dialogue task requires an agent to engage in a conversation about
an image with a human. It represents an extension of the Visual Question
Answering task in that the agent needs to answer a question about an image, but
it needs to do so in light of the previous dialogue that has taken place. The
key challenge in Visual Dialogue is thus maintaining a consistent, and natural
dialogue while continuing to answer questions correctly. We present a novel
approach that combines Reinforcement Learning and Generative Adversarial
Networks (GANs) to generate more human-like responses to questions. The GAN
helps overcome the relative paucity of training data, and the tendency of the
typical MLE-based approach to generate overly terse answers. Critically, the
GAN is tightly integrated into the attention mechanism that generates
human-interpretable reasons for each answer. This means that the discriminative
model of the GAN has the task of assessing whether a candidate answer is
generated by a human or not, given the provided reason. This is significant
because it drives the generative model to produce high quality answers that are
well supported by the associated reasoning. The method also generates the
state-of-the-art results on the primary benchmark.
Event Representations with Tensor-based Compositions
Authors: Noah Weber, Niranjan Balasubramanian, Nathanael Chambers
Date: 2017-11-21 00:00:00

Robust and flexible event representations are important to many core areas in
language understanding. Scripts were proposed early on as a way of representing
sequences of events for such understanding, and has recently attracted renewed
attention. However, obtaining effective representations for modeling
script-like event sequences is challenging. It requires representations that
can capture event-level and scenario-level semantics. We propose a new
tensor-based composition method for creating event representations. The method
captures more subtle semantic interactions between an event and its entities
and yields representations that are effective at multiple event-related tasks.
With the continuous representations, we also devise a simple schema generation
method which produces better schemas compared to a prior discrete
representation based method. Our analysis shows that the tensors capture
distinct usages of a predicate even when there are only subtle differences in
their surface realizations.
StarSpace: Embed All The Things!
Authors: Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes, Jason Weston
Date: 2017-09-12 00:00:00
Tasks: All, Collaborative Filtering, Text Classification, Word Embeddings

A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Acquiring Common Sense Spatial Knowledge through Implicit Spatial Templates
Authors: Guillem Collell, Luc van Gool, Marie-Francine Moens
Date: 2017-11-18 00:00:00
Tasks: Common Sense Reasoning, Question Answering, Word Embeddings

Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that restricts spatial templates to explicit spatial prepositions (e.g., "glass on table"), here we extend this concept to implicit spatial language, i.e., those relationships (generally actions) for which the spatial arrangement of the objects is only implicitly implied (e.g., "man riding horse"). In contrast with explicit relationships, predicting spatial arrangements from implicit spatial language requires significant common sense spatial understanding. Here, we introduce the task of predicting spatial templates for two objects under a relationship, which can be seen as a spatial question-answering task with a (2D) continuous output ("where is the man w.r.t. a horse when the man is walking the horse?"). We present two simple neural-based models that leverage annotated images and structured text to learn this task. The good performance of these models reveals that spatial locations are to a large extent predictable from implicit spatial language. Crucially, the models attain similar performance in a challenging generalized setting, where the object-relation-object combinations (e.g.,"man walking dog") have never been seen before. Next, we go one step further by presenting the models with unseen objects (e.g., "dog"). In this scenario, we show that leveraging word embeddings enables the models to output accurate spatial predictions, proving that the models acquire solid common sense spatial knowledge allowing for such generalization.
Joint Learning of Set Cardinality and State Distribution
Authors: S. Hamid Rezatofighi, Anton Milan, Qinfeng Shi, Anthony Dick, Ian Reid
Date: 2017-09-13 00:00:00
Tasks: image-classification, Image Classification, Multi-Label Image Classification, valid

We present a novel approach for learning to predict sets using deep learning.
In recent years, deep neural networks have shown remarkable results in computer
vision, natural language processing and other related problems. Despite their
success, traditional architectures suffer from a serious limitation in that
they are built to deal with structured input and output data, i.e. vectors or
matrices. Many real-world problems, however, are naturally described as sets,
rather than vectors. Existing techniques that allow for sequential data, such
as recurrent neural networks, typically heavily depend on the input and output
order and do not guarantee a valid solution. Here, we derive in a principled
way, a mathematical formulation for set prediction where the output is
permutation invariant. In particular, our approach jointly learns both the
cardinality and the state distribution of the target set. We demonstrate the
validity of our method on the task of multi-label image classification and
achieve a new state of the art on the PASCAL VOC and MS COCO datasets.
Deletion-Robust Submodular Maximization at Scale
Authors: Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi
Date: 2017-11-20 00:00:00
Tasks: Fairness, feature selection

Can we efficiently extract useful information from a large user-generated
dataset while protecting the privacy of the users and/or ensuring fairness in
representation. We cast this problem as an instance of a deletion-robust
submodular maximization where part of the data may be deleted due to privacy
concerns or fairness criteria. We propose the first memory-efficient
centralized, streaming, and distributed methods with constant-factor
approximation guarantees against any number of adversarial deletions. We
extensively evaluate the performance of our algorithms against prior
state-of-the-art on real-world applications, including (i) Uber-pick up
locations with location privacy constraints; (ii) feature selection with
fairness constraints for income prediction and crime rate prediction; and (iii)
robust to deletion summarization of census data, consisting of 2,458,285
feature vectors.
Accountability of AI Under the Law: The Role of Explanation
Authors: Finale Doshi-Velez, Mason Kortz, Ryan Budish, Chris Bavitz, Sam Gershman, David O'Brien, Kate Scott, Stuart Schieber, James Waldo, David Weinberger, Adrian Weller, Alexandra Wood
Date: 2017-11-03 00:00:00
Tasks: Autonomous Driving

The ubiquity of systems using artificial intelligence or "AI" has brought increasing attention to how those systems should be regulated. The choice of how to regulate AI systems will require care. AI systems have the potential to synthesize large amounts of data, allowing for greater levels of personalization and precision than ever before---applications range from clinical decision support to autonomous driving and predictive policing. That said, there exist legitimate concerns about the intentional and unintentional negative consequences of AI systems. There are many ways to hold AI systems accountable. In this work, we focus on one: explanation. Questions about a legal right to explanation from AI systems was recently debated in the EU General Data Protection Regulation, and thus thinking carefully about when and how explanation from AI systems might improve accountability is timely. In this work, we review contexts in which explanation is currently required under the law, and then list the technical considerations that must be considered if we desired AI systems that could provide kinds of explanations that are currently required of humans.
Adversarial Attacks Beyond the Image Space
Authors: Xiaohui Zeng, Chenxi Liu, Yu-Siang Wang, Weichao Qiu, Lingxi Xie, Yu-Wing Tai, Chi Keung Tang, Alan L. Yuille
Date: 2017-11-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Generating adversarial examples is an intriguing problem and an important way
of understanding the working mechanism of deep neural networks. Most existing
approaches generated perturbations in the image space, i.e., each pixel can be
modified independently. However, in this paper we pay special attention to the
subset of adversarial examples that correspond to meaningful changes in 3D
physical properties (like rotation and translation, illumination condition,
etc.). These adversaries arguably pose a more serious concern, as they
demonstrate the possibility of causing neural network failure by easy
perturbations of real-world 3D objects and scenes.
  In the contexts of object classification and visual question answering, we
augment state-of-the-art deep neural networks that receive 2D input images with
a rendering module (either differentiable or not) in front, so that a 3D scene
(in the physical space) is rendered into a 2D image (in the image space), and
then mapped to a prediction (in the output space). The adversarial
perturbations can now go beyond the image space, and have clear meanings in the
3D physical world. Though image-space adversaries can be interpreted as
per-pixel albedo change, we verify that they cannot be well explained along
these physically meaningful dimensions, which often have a non-local effect.
But it is still possible to successfully attack beyond the image space on the
physical space, though this is more difficult than image-space attacks,
reflected in lower success rates and heavier perturbations required.
Pixie: A System for Recommending 3+ Billion Items to 200+ Million Users in Real-Time
Authors: Chantat Eksombatchai, Pranav Jindal, Jerry Zitao Liu, Yuchen Liu, Rahul Sharma, Charles Sugnet, Mark Ulrich, Jure Leskovec
Date: 2017-11-21 00:00:00
Tasks: Recommendation Systems

User experience in modern content discovery applications critically depends
on high-quality personalized recommendations. However, building systems that
provide such recommendations presents a major challenge due to a massive pool
of items, a large number of users, and requirements for recommendations to be
responsive to user actions and generated on demand in real-time. Here we
present Pixie, a scalable graph-based real-time recommender system that we
developed and deployed at Pinterest. Given a set of user-specific pins as a
query, Pixie selects in real-time from billions of possible pins those that are
most related to the query. To generate recommendations, we develop Pixie Random
Walk algorithm that utilizes the Pinterest object graph of 3 billion nodes and
17 billion edges. Experiments show that recommendations provided by Pixie lead
up to 50% higher user engagement when compared to the previous Hadoop-based
production system. Furthermore, we develop a graph pruning strategy at that
leads to an additional 58% improvement in recommendations. Last, we discuss
system aspects of Pixie, where a single server executes 1,200 recommendation
requests per second with 60 millisecond latency. Today, systems backed by Pixie
contribute to more than 80% of all user engagement on Pinterest.