pwc-archive/papers-with-abstracts huggingface dataset:
Benchmark Visual Question Answer Models by using Focus Map
Authors: Wenda Qiu, Yueyang Xianzang, Zhekai Zhang
Date: 2018-01-13 00:00:00
Tasks: Visual Reasoning

Inferring and Executing Programs for Visual Reasoning proposes a model for
visual reasoning that consists of a program generator and an execution engine
to avoid end-to-end models. To show that the model actually learns which
objects to focus on to answer the questions, the authors give a visualization
of the norm of the gradient of the sum of the predicted answer scores with
respect to the final feature map. However, the authors do not evaluate the
efficiency of focus map. This paper purposed a method for evaluating it. We
generate several kinds of questions to test different keywords. We infer focus
maps from the model by asking these questions and evaluate them by comparing
with the segmentation graph. Furthermore, this method can be applied to any
model if focus maps can be inferred from it. By evaluating focus map of
different models on the CLEVR dataset, we will show that CLEVR-iep model has
learned where to focus more than end-to-end models.
Cost-Sensitive Convolution based Neural Networks for Imbalanced Time-Series Classification
Authors: Yue Geng, Xinyu Luo
Date: 2018-01-13 00:00:00
Tasks: Deep Learning, General Classification, Temporal Sequences, Time Series, Time Series Analysis, Time Series Classification

Some deep convolutional neural networks were proposed for time-series
classification and class imbalanced problems. However, those models performed
degraded and even failed to recognize the minority class of an imbalanced
temporal sequences dataset. Minority samples would bring troubles for temporal
deep learning classifiers due to the equal treatments of majority and minority
class. Until recently, there were few works applying deep learning on
imbalanced time-series classification (ITSC) tasks. Here, this paper aimed at
tackling ITSC problems with deep learning. An adaptive cost-sensitive learning
strategy was proposed to modify temporal deep learning models. Through the
proposed strategy, classifiers could automatically assign misclassification
penalties to each class. In the experimental section, the proposed method was
utilized to modify five neural networks. They were evaluated on a large volume,
real-life and imbalanced time-series dataset with six metrics. Each single
network was also tested alone and combined with several mainstream data
samplers. Experimental results illustrated that the proposed cost-sensitive
modified networks worked well on ITSC tasks. Compared to other methods, the
cost-sensitive convolution neural network and residual network won out in the
terms of all metrics. Consequently, the proposed cost-sensitive learning
strategy can be used to modify deep learning classifiers from cost-insensitive
to cost-sensitive. Those cost-sensitive convolutional networks can be
effectively applied to address ITSC issues.
Convexification of Neural Graph
Authors: Han Xiao
Date: 2018-01-09 00:00:00

Traditionally, most complex intelligence architectures are extremely
non-convex, which could not be well performed by convex optimization. However,
this paper decomposes complex structures into three types of nodes: operators,
algorithms and functions. Iteratively, propagating from node to node along
edge, we prove that "regarding the tree-structured neural graph, it is nearly
convex in each variable, when the other variables are fixed." In fact, the
non-convex properties stem from circles and functions, which could be
transformed to be convex with our proposed \textit{\textbf{scale mechanism}}.
Experimentally, we justify our theoretical analysis by two practical
applications.
Autonomous Driving in Reality with Reinforcement Learning and Image Translation
Authors: Nayun Xu, Bowen Tan, Bingyu Kong
Date: 2018-01-13 00:00:00
Tasks: Autonomous Driving, Car Racing, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Semantic Segmentation, Translation

Supervised learning is widely used in training autonomous driving vehicle.
However, it is trained with large amount of supervised labeled data.
Reinforcement learning can be trained without abundant labeled data, but we
cannot train it in reality because it would involve many unpredictable
accidents. Nevertheless, training an agent with good performance in virtual
environment is relatively much easier. Because of the huge difference between
virtual and real, how to fill the gap between virtual and real is challenging.
In this paper, we proposed a novel framework of reinforcement learning with
image semantic segmentation network to make the whole model adaptable to
reality. The agent is trained in TORCS, a car racing simulator.
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition
Authors: Yangyang Xu, Lei Wang
Date: 2018-01-08 00:00:00
Tasks: Action Recognition, Diversity, Skeleton Based Action Recognition, Temporal Action Localization

In this paper, we proposed a effective but extensible residual
one-dimensional convolution neural network as base network, based on the this
network, we proposed four subnets to explore the features of skeleton sequences
from each aspect. Given a skeleton sequences, the spatial information are
encoded into the skeleton joints coordinate in a frame and the temporal
information are present by multiple frames. Limited by the skeleton sequence
representations, two-dimensional convolution neural network cannot be used
directly, we chose one-dimensional convolution layer as the basic layer. Each
sub network could extract discriminative features from different aspects. Our
first subnet is a two-stream network which could explore both temporal and
spatial information. The second is a body-parted network, which could gain
micro spatial features and macro temporal features. The third one is an
attention network, the main contribution of which is to focus the key frames
and feature channels which high related with the action classes in a skeleton
sequence. One frame-difference network, as the last subnet, mainly processes
the joints changes between the consecutive frames. Four subnets ensemble
together by late fusion, the key problem of ensemble method is each subnet
should have a certain performance and between the subnets, there are diversity
existing. Each subnet shares a wellperformance basenet and differences between
subnets guaranteed the diversity. Experimental results show that the ensemble
network gets a state-of-the-art performance on three widely used datasets.
SuperNeurons: Dynamic GPU Memory Management for Training Deep Neural Networks
Authors: Linnan Wang, Jinmian Ye, Yiyang Zhao, Wei Wu, Ang Li, Shuaiwen Leon Song, Zenglin Xu, Tim Kraska
Date: 2018-01-13 00:00:00
Tasks: GPU, Management, Scheduling

Going deeper and wider in neural architectures improves the accuracy, while
the limited GPU DRAM places an undesired restriction on the network design
domain. Deep Learning (DL) practitioners either need change to less desired
network architectures, or nontrivially dissect a network across multiGPUs.
These distract DL practitioners from concentrating on their original machine
learning tasks. We present SuperNeurons: a dynamic GPU memory scheduling
runtime to enable the network training far beyond the GPU DRAM capacity.
SuperNeurons features 3 memory optimizations, \textit{Liveness Analysis},
\textit{Unified Tensor Pool}, and \textit{Cost-Aware Recomputation}, all
together they effectively reduce the network-wide peak memory usage down to the
maximal memory usage among layers. We also address the performance issues in
those memory saving techniques. Given the limited GPU DRAM, SuperNeurons not
only provisions the necessary memory for the training, but also dynamically
allocates the memory for convolution workspaces to achieve the high
performance. Evaluations against Caffe, Torch, MXNet and TensorFlow have
demonstrated that SuperNeurons trains at least 3.2432 deeper network than
current ones with the leading performance. Particularly, SuperNeurons can train
ResNet2500 that has $10^4$ basic network layers on a 12GB K40c.
Fairness in Supervised Learning: An Information Theoretic Approach
Authors: AmirEmad Ghassami, Sajad Khodadadian, Negar Kiyavash
Date: 2018-01-13 00:00:00
Tasks: Attribute, Decision Making, Fairness

Automated decision making systems are increasingly being used in real-world
applications. In these systems for the most part, the decision rules are
derived by minimizing the training error on the available historical data.
Therefore, if there is a bias related to a sensitive attribute such as gender,
race, religion, etc. in the data, say, due to cultural/historical
discriminatory practices against a certain demographic, the system could
continue discrimination in decisions by including the said bias in its decision
rule. We present an information theoretic framework for designing fair
predictors from data, which aim to prevent discrimination against a specified
sensitive attribute in a supervised learning setting. We use equalized odds as
the criterion for discrimination, which demands that the prediction should be
independent of the protected attribute conditioned on the actual label. To
ensure fairness and generalization simultaneously, we compress the data to an
auxiliary variable, which is used for the prediction task. This auxiliary
variable is chosen such that it is decontaminated from the discriminatory
attribute in the sense of equalized odds. The final predictor is obtained by
applying a Bayesian decision rule to the auxiliary variable.
Encoding CNN Activations for Writer Recognition
Authors: Vincent Christlein, Andreas Maier
Date: 2017-12-21 00:00:00
Tasks: Retrieval, Writer Retrieval

The encoding of local features is an essential part for writer identification
and writer retrieval. While CNN activations have already been used as local
features in related works, the encoding of these features has attracted little
attention so far. In this work, we compare the established VLAD encoding with
triangulation embedding. We further investigate generalized max pooling as an
alternative to sum pooling and the impact of decorrelation and Exemplar SVMs.
With these techniques, we set new standards on two publicly available datasets
(ICDAR13, KHATT).
Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
Authors: Botao Hao, Will Wei Sun, Yufeng Liu, Guang Cheng
Date: 2016-11-28 00:00:00
Tasks: Clustering, Sparse Learning

We consider joint estimation of multiple graphical models arising from
heterogeneous and high-dimensional observations. Unlike most previous
approaches which assume that the cluster structure is given in advance, an
appealing feature of our method is to learn cluster structure while estimating
heterogeneous graphical models. This is achieved via a high dimensional version
of Expectation Conditional Maximization (ECM) algorithm (Meng and Rubin, 1993).
A joint graphical lasso penalty is imposed on the conditional maximization step
to extract both homogeneity and heterogeneity components across all clusters.
Our algorithm is computationally efficient due to fast sparse learning routines
and can be implemented without unsupervised learning knowledge. The superior
performance of our method is demonstrated by extensive experiments and its
application to a Glioblastoma cancer dataset reveals some new insights in
understanding the Glioblastoma cancer. In theory, a non-asymptotic error bound
is established for the output directly from our high dimensional ECM algorithm,
and it consists of two quantities: statistical error (statistical accuracy) and
optimization error (computational complexity). Such a result gives a
theoretical guideline in terminating our ECM iterations.
A Computational Model of Commonsense Moral Decision Making
Authors: Richard Kim, Max Kleiman-Weiner, Andres Abeliuk, Edmond Awad, Sohan Dsouza, Josh Tenenbaum, Iyad Rahwan
Date: 2018-01-12 00:00:00
Tasks: Autonomous Vehicles, Decision Making, model

We introduce a new computational model of moral decision making, drawing on a
recent theory of commonsense moral learning via social dynamics. Our model
describes moral dilemmas as a utility function that computes trade-offs in
values over abstract moral dimensions, which provide interpretable parameter
values when implemented in machine-led ethical decision-making. Moreover,
characterizing the social structures of individuals and groups as a
hierarchical Bayesian model, we show that a useful description of an
individual's moral values - as well as a group's shared values - can be
inferred from a limited amount of observed data. Finally, we apply and evaluate
our approach to data from the Moral Machine, a web application that collects
human judgments on moral dilemmas involving autonomous vehicles.
Engineering Cooperative Smart Things based on Embodied Cognition
Authors: Nathalia Moraes do Nascimento, Carlos Jose Pereira de Lucena
Date: 2018-01-12 00:00:00

The goal of the Internet of Things (IoT) is to transform any thing around us,
such as a trash can or a street light, into a smart thing. A smart thing has
the ability of sensing, processing, communicating and/or actuating. In order to
achieve the goal of a smart IoT application, such as minimizing waste
transportation costs or reducing energy consumption, the smart things in the
application scenario must cooperate with each other without a centralized
control. Inspired by known approaches to design swarm of cooperative and
autonomous robots, we modeled our smart things based on the embodied cognition
concept. Each smart thing is a physical agent with a body composed of a
microcontroller, sensors and actuators, and a brain that is represented by an
artificial neural network. This type of agent is commonly called an embodied
agent. The behavior of these embodied agents is autonomously configured through
an evolutionary algorithm that is triggered according to the application
performance. To illustrate, we have designed three homogeneous prototypes for
smart street lights based on an evolved network. This application has shown
that the proposed approach results in a feasible way of modeling decentralized
smart things with self-developed and cooperative capabilities.
Estimating the Number of Connected Components in a Graph via Subgraph Sampling
Authors: Jason M. Klusowski, Yihong Wu
Date: 2018-01-12 00:00:00

Learning properties of large graphs from samples has been an important problem in statistical network analysis since the early work of Goodman \cite{Goodman1949} and Frank \cite{Frank1978}. We revisit a problem formulated by Frank \cite{Frank1978} of estimating the number of connected components in a large graph based on the subgraph sampling model, in which we randomly sample a subset of the vertices and observe the induced subgraph. The key question is whether accurate estimation is achievable in the \emph{sublinear} regime where only a vanishing fraction of the vertices are sampled. We show that it is impossible if the parent graph is allowed to contain high-degree vertices or long induced cycles. For the class of chordal graphs, where induced cycles of length four or above are forbidden, we characterize the optimal sample complexity within constant factors and construct linear-time estimators that provably achieve these bounds. This significantly expands the scope of previous results which have focused on unbiased estimators and special classes of graphs such as forests or cliques. Both the construction and the analysis of the proposed methodology rely on combinatorial properties of chordal graphs and identities of induced subgraph counts. They, in turn, also play a key role in proving minimax lower bounds based on construction of random instances of graphs with matching structures of small subgraphs.
TieNet: Text-Image Embedding Network for Common Thorax Disease Classification and Reporting in Chest X-rays
Authors: Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Ronald M. Summers
Date: 2018-01-12 00:00:00
Proceeding: CVPR 2018 6
Tasks: General Classification

Chest X-rays are one of the most common radiological examinations in daily
clinical routines. Reporting thorax diseases using chest X-rays is often an
entry-level task for radiologist trainees. Yet, reading a chest X-ray image
remains a challenging job for learning-oriented machine intelligence, due to
(1) shortage of large-scale machine-learnable medical image datasets, and (2)
lack of techniques that can mimic the high-level reasoning of human
radiologists that requires years of knowledge accumulation and professional
training. In this paper, we show the clinical free-text radiological reports
can be utilized as a priori knowledge for tackling these two key problems. We
propose a novel Text-Image Embedding network (TieNet) for extracting the
distinctive image and text representations. Multi-level attention models are
integrated into an end-to-end trainable CNN-RNN architecture for highlighting
the meaningful text words and image regions. We first apply TieNet to classify
the chest X-rays by using both image features and text embeddings extracted
from associated reports. The proposed auto-annotation framework achieves high
accuracy (over 0.9 on average in AUCs) in assigning disease labels for our
hand-label evaluation dataset. Furthermore, we transform the TieNet into a
chest X-ray reporting system. It simulates the reporting process and can output
disease classification and a preliminary report together. The classification
results are significantly improved (6% increase on average in AUCs) compared to
the state-of-the-art baseline on an unseen and hand-labeled dataset (OpenI).
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks
Authors: Reuben A. Farrugia, Christine Guillemot
Date: 2018-01-12 00:00:00
Tasks: Optical Flow Estimation, Super-Resolution

Light field imaging has recently known a regain of interest due to the
availability of practical light field capturing systems that offer a wide range
of applications in the field of computer vision. However, capturing
high-resolution light fields remains technologically challenging since the
increase in angular resolution is often accompanied by a significant reduction
in spatial resolution. This paper describes a learning-based spatial light
field super-resolution method that allows the restoration of the entire light
field with consistency across all sub-aperture images. The algorithm first uses
optical flow to align the light field and then reduces its angular dimension
using low-rank approximation. We then consider the linearly independent columns
of the resulting low-rank model as an embedding, which is restored using a deep
convolutional neural network (DCNN). The super-resolved embedding is then used
to reconstruct the remaining sub-aperture images. The original disparities are
restored using inverse warping where missing pixels are approximated using a
novel light field inpainting algorithm. Experimental results show that the
proposed method outperforms existing light field super-resolution algorithms,
achieving PSNR gains of 0.23 dB over the second best performing method. This
performance can be further improved using iterative back-projection as a
post-processing step.
OptNet: Differentiable Optimization as a Layer in Neural Networks
Authors: Brandon Amos, J. Zico Kolter
Date: 2017-03-01 00:00:00
Proceeding: ICML 2017 8
Tasks: Bilevel Optimization, GPU

This paper presents OptNet, a network architecture that integrates optimization problems (here, specifically in the form of quadratic programs) as individual layers in larger end-to-end trainable deep networks. These layers encode constraints and complex dependencies between the hidden states that traditional convolutional and fully-connected layers often cannot capture. We explore the foundations for such an architecture: we show how techniques from sensitivity analysis, bilevel optimization, and implicit differentiation can be used to exactly differentiate through these layers and with respect to layer parameters; we develop a highly efficient solver for these layers that exploits fast GPU-based batch solves within a primal-dual interior point method, and which provides backpropagation gradients with virtually no additional cost on top of the solve; and we highlight the application of these approaches in several problems. In one notable example, the method is learns to play mini-Sudoku (4x4) given just input and output games, with no a-priori information about the rules of the game; this highlights the ability of OptNet to learn hard constraints better than other neural architectures.
Generalization Error Bounds for Noisy, Iterative Algorithms
Authors: Ankit Pensia, Varun Jog, Po-Ling Loh
Date: 2018-01-12 00:00:00
Tasks: Learning Theory

In statistical learning theory, generalization error is used to quantify the
degree to which a supervised machine learning algorithm may overfit to training
data. Recent work [Xu and Raginsky (2017)] has established a bound on the
generalization error of empirical risk minimization based on the mutual
information $I(S;W)$ between the algorithm input $S$ and the algorithm output
$W$, when the loss function is sub-Gaussian. We leverage these results to
derive generalization error bounds for a broad class of iterative algorithms
that are characterized by bounded, noisy updates with Markovian structure. Our
bounds are very general and are applicable to numerous settings of interest,
including stochastic gradient Langevin dynamics (SGLD) and variants of the
stochastic gradient Hamiltonian Monte Carlo (SGHMC) algorithm. Furthermore, our
error bounds hold for any output function computed over the path of iterates,
including the last iterate of the algorithm or the average of subsets of
iterates, and also allow for non-uniform sampling of data in successive updates
of the algorithm.
Asynchronous Stochastic Variational Inference
Authors: Saad Mohamad, Abdelhamid Bouchachia, Moamar Sayed-Mouchaweh
Date: 2018-01-12 00:00:00
Tasks: Stochastic Optimization, Variational Inference

Stochastic variational inference (SVI) employs stochastic optimization to
scale up Bayesian computation to massive data. Since SVI is at its core a
stochastic gradient-based algorithm, horizontal parallelism can be harnessed to
allow larger scale inference. We propose a lock-free parallel implementation
for SVI which allows distributed computations over multiple slaves in an
asynchronous style. We show that our implementation leads to linear speed-up
while guaranteeing an asymptotic ergodic convergence rate $O(1/\sqrt(T)$ )
given that the number of slaves is bounded by $\sqrt(T)$ ($T$ is the total
number of iterations). The implementation is done in a high-performance
computing (HPC) environment using message passing interface (MPI) for python
(MPI4py). The extensive empirical evaluation shows that our parallel SVI is
lossless, performing comparably well to its counterpart serial SVI with linear
speed-up.
Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision
Authors: Baris Gecer, Vassileios Balntas, Tae-Kyun Kim
Date: 2017-08-01 00:00:00
Tasks: Face Recognition

In this work, we investigate several methods and strategies to learn deep
embeddings for face recognition, using joint sample- and set-based
optimization. We explain our framework that expands traditional learning with
set-based supervision together with the strategies used to maintain set
characteristics. We, then, briefly review the related set-based loss functions,
and subsequently propose a novel Max-Margin Loss which maximizes maximum
possible inter-class margin with assistance of Support Vector Machines (SVMs).
It implicitly pushes all the samples towards correct side of the margin with a
vector perpendicular to the hyperplane and a strength exponentially growing
towards to negative side of the hyperplane. We show that the introduced loss
outperform the previous sample-based and set-based ones in terms verification
of faces on two commonly used benchmarks.