pwc-archive/papers-with-abstracts huggingface dataset:
MTFH: A Matrix Tri-Factorization Hashing Framework for Efficient Cross-Modal Retrieval
Authors: Xin Liu, Zhikai Hu, Haibin Ling, Yiu-ming Cheung
Date: 2018-05-04 00:00:00
Tasks: Cross-Modal Retrieval, Retrieval, Semantic Textual Similarity

Hashing has recently sparked a great revolution in cross-modal retrieval because of its low storage cost and high query speed. Recent cross-modal hashing methods often learn unified or equal-length hash codes to represent the multi-modal data and make them intuitively comparable. However, such unified or equal-length hash representations could inherently sacrifice their representation scalability because the data from different modalities may not have one-to-one correspondence and could be encoded more efficiently by different hash codes of unequal lengths. To mitigate these problems, this paper exploits a related and relatively unexplored problem: encode the heterogeneous data with varying hash lengths and generalize the cross-modal retrieval in various challenging scenarios. To this end, a generalized and flexible cross-modal hashing framework, termed Matrix Tri-Factorization Hashing (MTFH), is proposed to work seamlessly in various settings including paired or unpaired multi-modal data, and equal or varying hash length encoding scenarios. More specifically, MTFH exploits an efficient objective function to flexibly learn the modality-specific hash codes with different length settings, while synchronously learning two semantic correlation matrices to semantically correlate the different hash representations for heterogeneous data comparable. As a result, the derived hash codes are more semantically meaningful for various challenging cross-modal retrieval tasks. Extensive experiments evaluated on public benchmark datasets highlight the superiority of MTFH under various retrieval scenarios and show its competitive performance with the state-of-the-arts.
Deep Communicating Agents for Abstractive Summarization
Authors: Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, Yejin Choi
Date: 2018-03-27 00:00:00
Proceeding: NAACL 2018 6
Tasks: Abstractive Text Summarization, Decoder, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present deep communicating agents in an encoder-decoder architecture to
address the challenges of representing a long document for abstractive
summarization. With deep communicating agents, the task of encoding a long text
is divided across multiple collaborating agents, each in charge of a subsection
of the input text. These encoders are connected to a single decoder, trained
end-to-end using reinforcement learning to generate a focused and coherent
summary. Empirical results demonstrate that multiple communicating encoders
lead to a higher quality summary compared to several strong baselines,
including those based on a single encoder or multiple non-communicating
encoders.
Causal programming: inference with structural causal models as finding instances of a relation
Authors: Joshua Brulé
Date: 2018-05-04 00:00:00
Tasks: Causal Discovery, Causal Inference, Relation, Selection bias

This paper proposes a causal inference relation and causal programming as
general frameworks for causal inference with structural causal models. A tuple,
$\langle M, I, Q, F \rangle$, is an instance of the relation if a formula, $F$,
computes a causal query, $Q$, as a function of known population probabilities,
$I$, in every model entailed by a set of model assumptions, $M$. Many problems
in causal inference can be viewed as the problem of enumerating instances of
the relation that satisfy given criteria. This unifies a number of previously
studied problems, including causal effect identification, causal discovery and
recovery from selection bias. In addition, the relation supports formalizing
new problems in causal inference with structural causal models, such as the
problem of research design. Causal programming is proposed as a further
generalization of causal inference as the problem of finding optimal instances
of the relation, with respect to a cost function.
Surrogate Scoring Rules
Authors: Yang Liu, Juntao Wang, Yi-Ling Chen
Date: 2018-02-26 00:00:00

Strictly proper scoring rules (SPSR) are incentive compatible for eliciting information about random variables from strategic agents when the principal can reward agents after the realization of the random variables. They also quantify the quality of elicited information, with more accurate predictions receiving higher scores in expectation. In this paper, we extend such scoring rules to settings where a principal elicits private probabilistic beliefs but only has access to agents' reports. We name our solution \emph{Surrogate Scoring Rules} (SSR). SSR build on a bias correction step and an error rate estimation procedure for a reference answer defined using agents' reports. We show that, with a single bit of information about the prior distribution of the random variables, SSR in a multi-task setting recover SPSR in expectation, as if having access to the ground truth. Therefore, a salient feature of SSR is that they quantify the quality of information despite the lack of ground truth, just as SPSR do for the setting \emph{with} ground truth. As a by-product, SSR induce \emph{dominant truthfulness} in reporting. Our method is verified both theoretically and empirically using data collected from real human forecasters.
Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning
Authors: Michael Everett, Yu Fan Chen, Jonathan P. How
Date: 2018-05-04 00:00:00
Tasks: Collision Avoidance, Decision Making, Deep Reinforcement Learning, Motion Planning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Robots that navigate among pedestrians use collision avoidance algorithms to
enable safe and efficient operation. Recent works present deep reinforcement
learning as a framework to model the complex interactions and cooperation.
However, they are implemented using key assumptions about other agents'
behavior that deviate from reality as the number of agents in the environment
increases. This work extends our previous approach to develop an algorithm that
learns collision avoidance among a variety of types of dynamic agents without
assuming they follow any particular behavior rules. This work also introduces a
strategy using LSTM that enables the algorithm to use observations of an
arbitrary number of other agents, instead of previous methods that have a fixed
observation size. The proposed algorithm outperforms our previous approach in
simulation as the number of agents increases, and the algorithm is demonstrated
on a fully autonomous robotic vehicle traveling at human walking speed, without
the use of a 3D Lidar.
Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting
Authors: Samuel Yeom, Irene Giacomelli, Matt Fredrikson, Somesh Jha
Date: 2017-09-05 00:00:00
Tasks: Attribute, BIG-bench Machine Learning

Machine learning algorithms, when applied to sensitive data, pose a distinct
threat to privacy. A growing body of prior work demonstrates that models
produced by these algorithms may leak specific private information in the
training data to an attacker, either through the models' structure or their
observable behavior. However, the underlying cause of this privacy risk is not
well understood beyond a handful of anecdotal accounts that suggest overfitting
and influence might play a role.
  This paper examines the effect that overfitting and influence have on the
ability of an attacker to learn information about the training data from
machine learning models, either through training set membership inference or
attribute inference attacks. Using both formal and empirical analyses, we
illustrate a clear relationship between these factors and the privacy risk that
arises in several popular machine learning algorithms. We find that overfitting
is sufficient to allow an attacker to perform membership inference and, when
the target attribute meets certain conditions about its influence, attribute
inference attacks. Interestingly, our formal analysis also shows that
overfitting is not necessary for these attacks and begins to shed light on what
other factors may be in play. Finally, we explore the connection between
membership inference and attribute inference, showing that there are deep
connections between the two that lead to effective new attacks.
Advanced local motion patterns for macro and micro facial expression recognition
Authors: B. Allaert, IM. Bilasco, C. Djeraba
Date: 2018-05-04 00:00:00
Tasks: Facial Expression Recognition, Facial Expression Recognition (FER), Micro Expression Recognition, Micro-Expression Recognition

In this paper, we develop a new method that recognizes facial expressions, on
the basis of an innovative local motion patterns feature, with three main
contributions. The first one is the analysis of the face skin temporal
elasticity and face deformations during expression. The second one is a unified
approach for both macro and micro expression recognition. And, the third one is
the step forward towards in-the-wild expression recognition, dealing with
challenges such as various intensity and various expression activation
patterns, illumination variation and small head pose variations. Our method
outperforms state-of-the-art methods for micro expression recognition and
positions itself among top-rank state-of-the-art methods for macro expression
recognition.
Circuit designs for superconducting optoelectronic loop neurons
Authors: Jeffrey M. Shainline, Sonia M. Buckley, Adam N. McCaughan, Jeff Chiles, Richard P. Mirin, Sae Woo Nam
Date: 2018-05-04 00:00:00

Optical communication achieves high fanout and short delay advantageous for
information integration in neural systems. Superconducting detectors enable
signaling with single photons for maximal energy efficiency. We present designs
of superconducting optoelectronic neurons based on superconducting
single-photon detectors, Josephson junctions, semiconductor light sources, and
multi-planar dielectric waveguides. These circuits achieve complex synaptic and
neuronal functions with high energy efficiency, leveraging the strengths of
light for communication and superconducting electronics for computation. The
neurons send few-photon signals to synaptic connections. These signals
communicate neuronal firing events as well as update synaptic weights.
Spike-timing-dependent plasticity is implemented with a single photon
triggering each step of the process. Microscale light-emitting diodes and
waveguide networks enable connectivity from a neuron to thousands of synaptic
connections, and the use of light for communication enables synchronization of
neurons across an area limited only by the distance light can travel within the
period of a network oscillation. Experimentally, each of the requisite circuit
elements has been demonstrated, yet a hardware platform combining them all has
not been attempted. Compared to digital logic or quantum computing, device
tolerances are relaxed. For this neural application, optical sources providing
incoherent pulses with 10,000 photons produced with efficiency of 10$^{-3}$
operating at 20\,MHz at 4.2\,K are sufficient to enable a massively scalable
neural computing platform with connectivity comparable to the brain and thirty
thousand times higher speed.
Reliability Map Estimation For CNN-Based Camera Model Attribution
Authors: David Güera, Sri Kalyan Yarlagadda, Paolo Bestagini, Fengqing Zhu, Stefano Tubaro, Edward J. Delp
Date: 2018-05-04 00:00:00

Among the image forensic issues investigated in the last few years, great
attention has been devoted to blind camera model attribution. This refers to
the problem of detecting which camera model has been used to acquire an image
by only exploiting pixel information. Solving this problem has great impact on
image integrity assessment as well as on authenticity verification. Recent
advancements that use convolutional neural networks (CNNs) in the media
forensic field have enabled camera model attribution methods to work well even
on small image patches. These improvements are also important for determining
forgery localization. Some patches of an image may not contain enough
information related to the camera model (e.g., saturated patches). In this
paper, we propose a CNN-based solution to estimate the camera model attribution
reliability of a given image patch. We show that we can estimate a
reliability-map indicating which portions of the image contain reliable camera
traces. Testing using a well known dataset confirms that by using this
information, it is possible to increase small patch camera model attribution
accuracy by more than 8% on a single patch.
A Signaling Game Approach to Databases Querying and Interaction
Authors: Ben McCamish, Vahid Ghadakchi, Arash Termehchy, Behrouz Touri
Date: 2016-03-13 00:00:00
Tasks: Data Interaction, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

As most users do not precisely know the structure and/or the content of
databases, their queries do not exactly reflect their information needs. The
database management systems (DBMS) may interact with users and use their
feedback on the returned results to learn the information needs behind their
queries. Current query interfaces assume that users do not learn and modify the
way way they express their information needs in form of queries during their
interaction with the DBMS. Using a real-world interaction workload, we show
that users learn and modify how to express their information needs during their
interactions with the DBMS and their learning is accurately modeled by a
well-known reinforcement learning mechanism. As current data interaction
systems assume that users do not modify their strategies, they cannot discover
the information needs behind users' queries effectively. We model the
interaction between users and DBMS as a game with identical interest between
two rational agents whose goal is to establish a common language for
representing information needs in form of queries. We propose a reinforcement
learning method that learns and answers the information needs behind queries
and adapts to the changes in users' strategies and prove that it improves the
effectiveness of answering queries stochastically speaking. We propose two
efficient implementation of this method over large relational databases. Our
extensive empirical studies over real-world query workloads indicate that our
algorithms are efficient and effective.
Learning to See in the Dark
Authors: Chen Chen, Qifeng Chen, Jia Xu, Vladlen Koltun
Date: 2018-05-04 00:00:00
Proceeding: CVPR 2018 6
Tasks: Deblurring, Denoising, Image Denoising

Imaging in low light is challenging due to low photon count and low SNR.
Short-exposure images suffer from noise, while long exposure can induce blur
and is often impractical. A variety of denoising, deblurring, and enhancement
techniques have been proposed, but their effectiveness is limited in extreme
conditions, such as video-rate imaging at night. To support the development of
learning-based pipelines for low-light image processing, we introduce a dataset
of raw short-exposure low-light images, with corresponding long-exposure
reference images. Using the presented dataset, we develop a pipeline for
processing low-light images, based on end-to-end training of a
fully-convolutional network. The network operates directly on raw sensor data
and replaces much of the traditional image processing pipeline, which tends to
perform poorly on such data. We report promising results on the new dataset,
analyze factors that affect performance, and highlight opportunities for future
work. The results are shown in the supplementary video at
https://youtu.be/qWKUFK7MWvg
Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Authors: Brian Bartoldson, Adrian Barbu, Gordon Erlebacher
Date: 2018-05-04 00:00:00
Tasks: General Classification, image-classification, Image Classification

Artificial neural networks (ANNs) may not be worth their computational/memory
costs when used in mobile phones or embedded devices. Parameter-pruning
algorithms combat these costs, with some algorithms capable of removing over
90% of an ANN's weights without harming the ANN's performance. Removing weights
from an ANN is a form of regularization, but existing pruning algorithms do not
significantly improve generalization error. We show that pruning ANNs can
improve generalization if pruning targets large weights instead of small
weights. Applying our pruning algorithm to an ANN leads to a higher image
classification accuracy on CIFAR-10 data than applying the popular regularizer
dropout. The pruning couples this higher accuracy with an 85% reduction of the
ANN's parameter count.
Learning to Represent Programs with Graphs
Authors: Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi
Date: 2017-11-01 00:00:00
Proceeding: ICLR 2018 1

Learning tasks on source code (i.e., formal languages) have been considered
recently, but most work has tried to transfer natural language methods and does
not capitalize on the unique opportunities offered by code's known syntax. For
example, long-range dependencies induced by using the same variable or function
in distant locations are often not considered. We propose to use graphs to
represent both the syntactic and semantic structure of code and use graph-based
deep learning methods to learn to reason over program structures.
  In this work, we present how to construct graphs from source code and how to
scale Gated Graph Neural Networks training to such large graphs. We evaluate
our method on two tasks: VarNaming, in which a network attempts to predict the
name of a variable given its usage, and VarMisuse, in which the network learns
to reason about selecting the correct variable that should be used at a given
program location. Our comparison to methods that use less structured program
representations shows the advantages of modeling known structure, and suggests
that our models learn to infer meaningful names and to solve the VarMisuse task
in many cases. Additionally, our testing showed that VarMisuse identifies a
number of bugs in mature open-source projects.
Low-Rank RNN Adaptation for Context-Aware Language Modeling
Authors: Aaron Jaech, Mari Ostendorf
Date: 2017-10-06 00:00:00
Proceeding: TACL 2018 1
Tasks: General Classification, Language Modeling, Language Modelling

A context-aware language model uses location, user and/or domain metadata
(context) to adapt its predictions. In neural language models, context
information is typically represented as an embedding and it is given to the RNN
as an additional input, which has been shown to be useful in many applications.
We introduce a more powerful mechanism for using context to adapt an RNN by
letting the context vector control a low-rank transformation of the recurrent
layer weight matrix. Experiments show that allowing a greater fraction of the
model parameters to be adjusted has benefits in terms of perplexity and
classification for several different types of context.
Human-In-The-Loop Person Re-Identification
Authors: Hanxiao Wang, Shaogang Gong, Xiatian Zhu, Tao Xiang
Date: 2016-12-05 00:00:00
Tasks: Ensemble Learning, Incremental Learning, Person Re-Identification

Current person re-identification (re-id) methods assume that (1) pre-labelled
training data are available for every camera pair, (2) the gallery size for
re-identification is moderate. Both assumptions scale poorly to real-world
applications when camera network size increases and gallery size becomes large.
Human verification of automatic model ranked re-id results becomes inevitable.
In this work, a novel human-in-the-loop re-id model based on Human Verification
Incremental Learning (HVIL) is formulated which does not require any
pre-labelled training data to learn a model, therefore readily scalable to new
camera pairs. This HVIL model learns cumulatively from human feedback to
provide instant improvement to re-id ranking of each probe on-the-fly enabling
the model scalable to large gallery sizes. We further formulate a Regularised
Metric Ensemble Learning (RMEL) model to combine a series of incrementally
learned HVIL models into a single ensemble model to be used when human feedback
becomes unavailable.
A Rank-Based Similarity Metric for Word Embeddings
Authors: Enrico Santus, Hongmin Wang, Emmanuele Chersoni, Yue Zhang
Date: 2018-05-04 00:00:00
Proceeding: ACL 2018 7
Tasks: Clustering, Outlier Detection, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

Word Embeddings have recently imposed themselves as a standard for
representing word meaning in NLP. Semantic similarity between word pairs has
become the most common evaluation benchmark for these representations, with
vector cosine being typically used as the only similarity metric. In this
paper, we report experiments with a rank-based metric for WE, which performs
comparably to vector cosine in similarity estimation and outperforms it in the
recently-introduced and challenging task of outlier detection, thus suggesting
that rank-based measures can improve clustering quality.
Semi-supervised multi-task learning for lung cancer diagnosis
Authors: Naji Khosravan, Ulas Bagci
Date: 2018-02-17 00:00:00
Tasks: Lung Cancer Diagnosis, Multi-Task Learning, Segmentation

Early detection of lung nodules is of great importance in lung cancer
screening. Existing research recognizes the critical role played by CAD systems
in early detection and diagnosis of lung nodules. However, many CAD systems,
which are used as cancer detection tools, produce a lot of false positives (FP)
and require a further FP reduction step. Furthermore, guidelines for early
diagnosis and treatment of lung cancer are consist of different shape and
volume measurements of abnormalities. Segmentation is at the heart of our
understanding of nodules morphology making it a major area of interest within
the field of computer aided diagnosis systems. This study set out to test the
hypothesis that joint learning of false positive (FP) nodule reduction and
nodule segmentation can improve the computer aided diagnosis (CAD) systems'
performance on both tasks. To support this hypothesis we propose a 3D deep
multi-task CNN to tackle these two problems jointly. We tested our system on
LUNA16 dataset and achieved an average dice similarity coefficient (DSC) of 91%
as segmentation accuracy and a score of nearly 92% for FP reduction. As a proof
of our hypothesis, we showed improvements of segmentation and FP reduction
tasks over two baselines. Our results support that joint training of these two
tasks through a multi-task learning approach improves system performance on
both. We also showed that a semi-supervised approach can be used to overcome
the limitation of lack of labeled data for the 3D segmentation task.