pwc-archive/papers-with-abstracts huggingface dataset:
Dependence Measure for non-additive model
Authors: Hangjin Jiang, Yiming Ding
Date: 2013-10-06 00:00:00
Tasks: Additive models, model

We proposed a new statistical dependency measure called Copula Dependency
Coefficient(CDC) for two sets of variables based on copula. It is robust to
outliers, easy to implement, powerful and appropriate to high-dimensional
variables. These properties are important in many applications. Experimental
results show that CDC can detect the dependence between variables in both
additive and non-additive models.
Goldbach's Function Approximation Using Deep Learning
Authors: Avigail Stekel, Merav Chkroun, Amos Azaria
Date: 2018-03-25 00:00:00
Tasks: Deep Learning

Goldbach conjecture is one of the most famous open mathematical problems. It
states that every even number, bigger than two, can be presented as a sum of 2
prime numbers. % In this work we present a deep learning based model that
predicts the number of Goldbach partitions for a given even number.
Surprisingly, our model outperforms all state-of-the-art analytically derived
estimations for the number of couples, while not requiring prime factorization
of the given number. We believe that building a model that can accurately
predict the number of couples brings us one step closer to solving one of the
world most famous open problems. To the best of our knowledge, this is the
first attempt to consider machine learning based data-driven methods to
approximate open mathematical problems in the field of number theory, and hope
that this work will encourage such attempts.
Pay More Attention - Neural Architectures for Question-Answering
Authors: Zia Hasan, Sebastian Fischer
Date: 2018-03-25 00:00:00
Tasks: Natural Language Understanding, Question Answering, Reading Comprehension

Machine comprehension is a representative task of natural language
understanding. Typically, we are given context paragraph and the objective is
to answer a question that depends on the context. Such a problem requires to
model the complex interactions between the context paragraph and the question.
Lately, attention mechanisms have been found to be quite successful at these
tasks and in particular, attention mechanisms with attention flow from both
context-to-question and question-to-context have been proven to be quite
useful. In this paper, we study two state-of-the-art attention mechanisms
called Bi-Directional Attention Flow (BiDAF) and Dynamic Co-Attention Network
(DCN) and propose a hybrid scheme combining these two architectures that gives
better overall performance. Moreover, we also suggest a new simpler attention
mechanism that we call Double Cross Attention (DCA) that provides better
results compared to both BiDAF and Co-Attention mechanisms while providing
similar performance as the hybrid scheme. The objective of our paper is to
focus particularly on the attention layer and to suggest improvements on that.
Our experimental evaluations show that both our proposed models achieve
superior results on the Stanford Question Answering Dataset (SQuAD) compared to
BiDAF and DCN attention mechanisms.
Visual Question Answering with Memory-Augmented Networks
Authors: Chao Ma, Chunhua Shen, Anthony Dick, Qi Wu, Peng Wang, Anton Van Den Hengel, Ian Reid
Date: 2017-07-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

In this paper, we exploit a memory-augmented neural network to predict
accurate answers to visual questions, even when those answers occur rarely in
the training set. The memory network incorporates both internal and external
memory blocks and selectively pays attention to each training exemplar. We show
that memory-augmented neural networks are able to maintain a relatively
long-term memory of scarce training exemplars, which is important for visual
question answering due to the heavy-tailed distribution of answers in a general
VQA setting. Experimental results on two large-scale benchmark datasets show
the favorable performance of the proposed algorithm with a comparison to state
of the art.
Tensorial Mixture Models
Authors: Or Sharir, Ronen Tamari, Nadav Cohen, Amnon Shashua
Date: 2016-10-13 00:00:00

Casting neural networks in generative frameworks is a highly sought-after
endeavor these days. Contemporary methods, such as Generative Adversarial
Networks, capture some of the generative capabilities, but not all. In
particular, they lack the ability of tractable marginalization, and thus are
not suitable for many tasks. Other methods, based on arithmetic circuits and
sum-product networks, do allow tractable marginalization, but their performance
is challenged by the need to learn the structure of a circuit. Building on the
tractability of arithmetic circuits, we leverage concepts from tensor analysis,
and derive a family of generative models we call Tensorial Mixture Models
(TMMs). TMMs assume a simple convolutional network structure, and in addition,
lend themselves to theoretical analyses that allow comprehensive understanding
of the relation between their structure and their expressive properties. We
thus obtain a generative model that is tractable on one hand, and on the other
hand, allows effective representation of rich distributions in an easily
controlled manner. These two capabilities are brought together in the task of
classification under missing data, where TMMs deliver state of the art
accuracies with seamless implementation and design.
clcNet: Improving the Efficiency of Convolutional Neural Network using Channel Local Convolutions
Authors: Dong-Qing Zhang
Date: 2017-12-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Computational Efficiency

Depthwise convolution and grouped convolution has been successfully applied
to improve the efficiency of convolutional neural network (CNN). We suggest
that these models can be considered as special cases of a generalized
convolution operation, named channel local convolution(CLC), where an output
channel is computed using a subset of the input channels. This definition
entails computation dependency relations between input and output channels,
which can be represented by a channel dependency graph(CDG). By modifying the
CDG of grouped convolution, a new CLC kernel named interlaced grouped
convolution (IGC) is created. Stacking IGC and GC kernels results in a
convolution block (named CLC Block) for approximating regular convolution. By
resorting to the CDG as an analysis tool, we derive the rule for setting the
meta-parameters of IGC and GC and the framework for minimizing the
computational cost. A new CNN model named clcNet is then constructed using CLC
blocks, which shows significantly higher computational efficiency and fewer
parameters compared to state-of-the-art networks, when being tested using the
ImageNet-1K dataset. Source code is available at
https://github.com/dqzhang17/clcnet.torch .
Image Recognition Using Scale Recurrent Neural Networks
Authors: Dong-Qing Zhang
Date: 2018-03-25 00:00:00

Convolutional Neural Network(CNN) has been widely used for image recognition
with great success. However, there are a number of limitations of the current
CNN based image recognition paradigm. First, the receptive field of CNN is
generally fixed, which limits its recognition capacity when the input image is
very large. Second, it lacks the computational scalability for dealing with
images with different sizes. Third, it is quite different from human visual
system for image recognition, which involves both feadforward and recurrent
proprocessing. This paper proposes a different paradigm of image recognition,
which can take advantages of variable scales of the input images, has more
computational scalabilities, and is more similar to image recognition by human
visual system. It is based on recurrent neural network (RNN) defined on image
scale with an embeded base CNN, which is named Scale Recurrent Neural
Network(SRNN). This RNN based approach makes it easier to deal with images with
variable sizes, and allows us to borrow existing RNN techniques, such as LSTM
and GRU, to further enhance the recognition accuracy. Our experiments show that
the recognition accuracy of a base CNN can be significantly boosted using the
proposed SRNN models. It also significantly outperforms the scale ensemble
method, which integrate the results of performing CNN to the input image at
different scales, although the computational overhead of using SRNN is
negligible.
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
Authors: Idan Kligvasser, Tamar Rott Shaham, Tomer Michaeli
Date: 2017-11-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: Denoising, Image Restoration, Super-Resolution

In recent years, deep neural networks (DNNs) achieved unprecedented
performance in many low-level vision tasks. However, state-of-the-art results
are typically achieved by very deep networks, which can reach tens of layers
with tens of millions of parameters. To make DNNs implementable on platforms
with limited resources, it is necessary to weaken the tradeoff between
performance and efficiency. In this paper, we propose a new activation unit,
which is particularly suitable for image restoration problems. In contrast to
the widespread per-pixel activation units, like ReLUs and sigmoids, our unit
implements a learnable nonlinear function with spatial connections. This
enables the net to capture much more complex features, thus requiring a
significantly smaller number of layers in order to reach the same performance.
We illustrate the effectiveness of our units through experiments with
state-of-the-art nets for denoising, de-raining, and super resolution, which
are already considered to be very small. With our approach, we are able to
further reduce these models by nearly 50% without incurring any degradation in
performance.
Bernoulli Embeddings for Graphs
Authors: Vinith Misra, Sumit Bhatia
Date: 2018-03-25 00:00:00
Tasks: Information Retrieval, Quantization, Retrieval

Just as semantic hashing can accelerate information retrieval, binary valued
embeddings can significantly reduce latency in the retrieval of graphical data.
We introduce a simple but effective model for learning such binary vectors for
nodes in a graph. By imagining the embeddings as independent coin flips of
varying bias, continuous optimization techniques can be applied to the
approximate expected loss. Embeddings optimized in this fashion consistently
outperform the quantization of both spectral graph embeddings and various
learned real-valued embeddings, on both ranking and pre-ranking tasks for a
variety of datasets.
Unsupervised Domain Adaptation: A Multi-task Learning-based Method
Authors: Jing Zhang, Wanqing Li, Philip Ogunbona
Date: 2018-03-25 00:00:00
Tasks: Domain Adaptation, Multi-Task Learning, Unsupervised Domain Adaptation

This paper presents a novel multi-task learning-based method for unsupervised
domain adaptation. Specifically, the source and target domain classifiers are
jointly learned by considering the geometry of target domain and the divergence
between the source and target domains based on the concept of multi-task
learning. Two novel algorithms are proposed upon the method using Regularized
Least Squares and Support Vector Machines respectively. Experiments on both
synthetic and real world cross domain recognition tasks have shown that the
proposed methods outperform several state-of-the-art domain adaptation methods.
Autonomous Ramp Merge Maneuver Based on Reinforcement Learning with Continuous Action Space
Authors: Pin Wang, Ching-Yao Chan
Date: 2018-03-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Ramp merging is a critical maneuver for road safety and traffic efficiency.
Most of the current automated driving systems developed by multiple automobile
manufacturers and suppliers are typically limited to restricted access freeways
only. Extending the automated mode to ramp merging zones presents substantial
challenges. One is that the automated vehicle needs to incorporate a future
objective (e.g. a successful and smooth merge) and optimize a long-term reward
that is impacted by subsequent actions when executing the current action.
Furthermore, the merging process involves interaction between the merging
vehicle and its surrounding vehicles whose behavior may be cooperative or
adversarial, leading to distinct merging countermeasures that are crucial to
successfully complete the merge. In place of the conventional rule-based
approaches, we propose to apply reinforcement learning algorithm on the
automated vehicle agent to find an optimal driving policy by maximizing the
long-term reward in an interactive driving environment. Most importantly, in
contrast to most reinforcement learning applications in which the action space
is resolved as discrete, our approach treats the action space as well as the
state space as continuous without incurring additional computational costs. Our
unique contribution is the design of the Q-function approximation whose format
is structured as a quadratic function, by which simple but effective neural
networks are used to estimate its coefficients. The results obtained through
the implementation of our training platform demonstrate that the vehicle agent
is able to learn a safe, smooth and timely merging policy, indicating the
effectiveness and practicality of our approach.
Learning Type-Aware Embeddings for Fashion Compatibility
Authors: Mariya I. Vasileva, Bryan A. Plummer, Krishna Dusad, Shreya Rajpal, Ranjitha Kumar, David Forsyth
Date: 2018-03-25 00:00:00
Proceeding: ECCV 2018 9
Tasks: Vocal Bursts Type Prediction

Outfits in online fashion data are composed of items of many different types
(e.g. top, bottom, shoes) that share some stylistic relationship with one
another. A representation for building outfits requires a method that can learn
both notions of similarity (for example, when two tops are interchangeable) and
compatibility (items of possibly different type that can go together in an
outfit). This paper presents an approach to learning an image embedding that
respects item type, and jointly learns notions of item similarity and
compatibility in an end-to-end model. To evaluate the learned representation,
we crawled 68,306 outfits created by users on the Polyvore website. Our
approach obtains 3-5% improvement over the state-of-the-art on outfit
compatibility prediction and fill-in-the-blank tasks using our dataset, as well
as an established smaller dataset, while supporting a variety of useful
queries.
Phase transition in the recoverability of network history
Authors: Jean-Gabriel Young, Guillaume St-Onge, Edward Laurence, Charles Murphy, Laurent Hébert-Dufresne, Patrick Desrosiers
Date: 2018-03-25 00:00:00

Network growth processes can be understood as generative models of the structure and history of complex networks. This point of view naturally leads to the problem of network archaeology: reconstructing all the past states of a network from its structure---a difficult permutation inference problem. In this paper, we introduce a Bayesian formulation of network archaeology, with a generalization of preferential attachment as our generative mechanism. We develop a sequential Monte Carlo algorithm to evaluate the posterior averages of this model, as well as an efficient heuristic that uncovers a history well correlated with the true one, in polynomial time. We use these methods to identify and characterize a phase transition in the quality of the reconstructed history, when they are applied to artificial networks generated by the model itself. Despite the existence of a no-recovery phase, we find that nontrivial inference is possible in a large portion of the parameter space as well as on empirical data.
Scene Graph Parsing as Dependency Parsing
Authors: Yu-Siang Wang, Chenxi Liu, Xiaohui Zeng, Alan Yuille
Date: 2018-03-25 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, Image Retrieval, Knowledge Graphs, Retrieval

In this paper, we study the problem of parsing structured knowledge graphs
from textual descriptions. In particular, we consider the scene graph
representation that considers objects together with their attributes and
relations: this representation has been proved useful across a variety of
vision and language applications. We begin by introducing an alternative but
equivalent edge-centric view of scene graphs that connect to dependency parses.
Together with a careful redesign of label and action space, we combine the
two-stage pipeline used in prior work (generic dependency parsing followed by
simple post-processing) into one, enabling end-to-end training. The scene
graphs generated by our learned neural dependency parser achieve an F-score
similarity of 49.67% to ground truth graphs on our evaluation set, surpassing
best previous approaches by 5%. We further demonstrate the effectiveness of our
learned parser on image retrieval applications.
Finite-Data Performance Guarantees for the Output-Feedback Control of an Unknown System
Authors: Ross Boczar, Nikolai Matni, Benjamin Recht
Date: 2018-03-25 00:00:00

As the systems we control become more complex, first-principle modeling
becomes either impossible or intractable, motivating the use of machine
learning techniques for the control of systems with continuous action spaces.
As impressive as the empirical success of these methods have been, strong
theoretical guarantees of performance, safety, or robustness are few and far
between. This paper takes a step towards such providing such guarantees by
establishing finite-data performance guarantees for the robust output-feedback
control of an unknown FIR SISO system. In particular, we introduce the
"Coarse-ID control" pipeline, which is composed of a system identification step
followed by a robust controller synthesis procedure, and analyze its end-to-end
performance, providing quantitative bounds on the performance degradation
suffered due to model uncertainty as a function of the number of experiments
run to identify the system. We conclude with numerical examples demonstrating
the effectiveness of our method.
Unsupervised Domain Adaptation: from Simulation Engine to the RealWorld
Authors: Sicheng Zhao, Bichen Wu, Joseph Gonzalez, Sanjit A. Seshia, Kurt Keutzer
Date: 2018-03-24 00:00:00
Tasks: Domain Adaptation, Unsupervised Domain Adaptation

Large-scale labeled training datasets have enabled deep neural networks to
excel on a wide range of benchmark vision tasks. However, in many applications
it is prohibitively expensive or time-consuming to obtain large quantities of
labeled data. To cope with limited labeled training data, many have attempted
to directly apply models trained on a large-scale labeled source domain to
another sparsely labeled target domain. Unfortunately, direct transfer across
domains often performs poorly due to domain shift and dataset bias. Domain
adaptation is the machine learning paradigm that aims to learn a model from a
source domain that can perform well on a different (but related) target domain.
In this paper, we summarize and compare the latest unsupervised domain
adaptation methods in computer vision applications. We classify the non-deep
approaches into sample re-weighting and intermediate subspace transformation
categories, while the deep strategy includes discrepancy-based methods,
adversarial generative models, adversarial discriminative models and
reconstruction-based methods. We also discuss some potential directions.
FaceForensics: A Large-scale Video Dataset for Forgery Detection in Human Faces
Authors: Andreas Rössler, Davide Cozzolino, Luisa Verdoliva, Christian Riess, Justus Thies, Matthias Nießner
Date: 2018-03-24 00:00:00
Tasks: Image Manipulation Detection

With recent advances in computer vision and graphics, it is now possible to
generate videos with extremely realistic synthetic faces, even in real time.
Countless applications are possible, some of which raise a legitimate alarm,
calling for reliable detectors of fake videos. In fact, distinguishing between
original and manipulated video can be a challenge for humans and computers
alike, especially when the videos are compressed or have low resolution, as it
often happens on social networks. Research on the detection of face
manipulations has been seriously hampered by the lack of adequate datasets. To
this end, we introduce a novel face manipulation dataset of about half a
million edited images (from over 1000 videos). The manipulations have been
generated with a state-of-the-art face editing approach. It exceeds all
existing video manipulation datasets by at least an order of magnitude. Using
our new dataset, we introduce benchmarks for classical image forensic tasks,
including classification and segmentation, considering videos compressed at
various quality levels. In addition, we introduce a benchmark evaluation for
creating indistinguishable forgeries with known ground truth; for instance with
generative refinement models.
Multiple Sclerosis Lesion Segmentation from Brain MRI via Fully Convolutional Neural Networks
Authors: Snehashis Roy, John A. Butman, Daniel S. Reich, Peter A. Calabresi, Dzung L. Pham
Date: 2018-03-24 00:00:00
Tasks: Lesion Segmentation, Segmentation

Multiple Sclerosis (MS) is an autoimmune disease that leads to lesions in the
central nervous system. Magnetic resonance (MR) images provide sufficient
imaging contrast to visualize and detect lesions, particularly those in the
white matter. Quantitative measures based on various features of lesions have
been shown to be useful in clinical trials for evaluating therapies. Therefore
robust and accurate segmentation of white matter lesions from MR images can
provide important information about the disease status and progression. In this
paper, we propose a fully convolutional neural network (CNN) based method to
segment white matter lesions from multi-contrast MR images. The proposed CNN
based method contains two convolutional pathways. The first pathway consists of
multiple parallel convolutional filter banks catering to multiple MR
modalities. In the second pathway, the outputs of the first one are
concatenated and another set of convolutional filters are applied. The output
of this last pathway produces a membership function for lesions that may be
thresholded to obtain a binary segmentation. The proposed method is evaluated
on a dataset of 100 MS patients, as well as the ISBI 2015 challenge data
consisting of 14 patients. The comparison is performed against four publicly
available MS lesion segmentation methods. Significant improvement in
segmentation quality over the competing methods is demonstrated on various
metrics, such as Dice and false positive ratio. While evaluating on the ISBI
2015 challenge data, our method produces a score of 90.48, where a score of 90
is considered to be comparable to a human rater.
Noise generation for compression algorithms
Authors: Renata Khasanova, Jan Wassenberg, Jyrki Alakuijala
Date: 2018-03-24 00:00:00

In various Computer Vision and Signal Processing applications, noise is
typically perceived as a drawback of the image capturing system that ought to
be removed. We, on the other hand, claim that image noise, just as texture, is
important for visual perception and, therefore, critical for lossy compression
algorithms that tend to make decompressed images look less realistic by
removing small image details. In this paper we propose a physically and
biologically inspired technique that learns a noise model at the encoding step
of the compression algorithm and then generates the appropriate amount of
additive noise at the decoding step. Our method can significantly increase the
realism of the decompressed image at the cost of few bytes of additional memory
space regardless of the original image size. The implementation of our method
is open-sourced and available at https://github.com/google/pik.