pwc-archive/papers-with-abstracts huggingface dataset:
Texture Classification in Extreme Scale Variations using GANet
Authors: Li Liu, Jie Chen, Guoying Zhao, Paul Fieguth, Xilin Chen, Matti Pietikäinen
Date: 2018-02-13 00:00:00
Tasks: Classification, General Classification, Texture Classification

Research in texture recognition often concentrates on recognizing textures
with intraclass variations such as illumination, rotation, viewpoint and small
scale changes. In contrast, in real-world applications a change in scale can
have a dramatic impact on texture appearance, to the point of changing
completely from one texture category to another. As a result, texture
variations due to changes in scale are amongst the hardest to handle. In this
work we conduct the first study of classifying textures with extreme variations
in scale. To address this issue, we first propose and then reduce scale
proposals on the basis of dominant texture patterns. Motivated by the
challenges posed by this problem, we propose a new GANet network where we use a
Genetic Algorithm to change the units in the hidden layers during network
training, in order to promote the learning of more informative semantic texture
patterns. Finally, we adopt a FVCNN (Fisher Vector pooling of a Convolutional
Neural Network filter bank) feature encoder for global texture representation.
  Because extreme scale variations are not necessarily present in most standard
texture databases, to support the proposed extreme-scale aspects of texture
understanding we are developing a new dataset, the Extreme Scale Variation
Textures (ESVaT), to test the performance of our framework. It is demonstrated
that the proposed framework significantly outperforms gold-standard texture
features by more than 10% on ESVaT. We also test the performance of our
proposed approach on the KTHTIPS2b and OS datasets and a further dataset
synthetically derived from Forrest, showing superior performance compared to
the state of the art.
signSGD: Compressed Optimisation for Non-Convex Problems
Authors: Jeremy Bernstein, Yu-Xiang Wang, Kamyar Azizzadenesheli, Anima Anandkumar
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7

Training large neural networks requires distributing learning across multiple
workers, where the cost of communicating gradients can be a significant
bottleneck. signSGD alleviates this problem by transmitting just the sign of
each minibatch stochastic gradient. We prove that it can get the best of both
worlds: compressed gradients and SGD-level convergence rate. The relative
$\ell_1/\ell_2$ geometry of gradients, noise and curvature informs whether
signSGD or SGD is theoretically better suited to a particular problem. On the
practical side we find that the momentum counterpart of signSGD is able to
match the accuracy and convergence speed of Adam on deep Imagenet models. We
extend our theory to the distributed setting, where the parameter server uses
majority vote to aggregate gradient signs from each worker enabling 1-bit
compression of worker-server communication in both directions. Using a theorem
by Gauss we prove that majority vote can achieve the same reduction in variance
as full precision distributed SGD. Thus, there is great promise for sign-based
optimisation schemes to achieve fast communication and fast convergence. Code
to reproduce experiments is to be found at https://github.com/jxbz/signSGD .
Beyond Shared Hierarchies: Deep Multitask Learning through Soft Layer Ordering
Authors: Elliot Meyerson, Risto Miikkulainen
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1

Existing deep multitask learning (MTL) approaches align layers shared between
tasks in a parallel ordering. Such an organization significantly constricts the
types of shared structure that can be learned. The necessity of parallel
ordering for deep MTL is first tested by comparing it with permuted ordering of
shared layers. The results indicate that a flexible ordering can enable more
effective sharing, thus motivating the development of a soft ordering approach,
which learns how shared layers are applied in different ways for different
tasks. Deep MTL with soft ordering outperforms parallel ordering methods across
a series of domains. These results suggest that the power of deep MTL comes
from learning highly general building blocks that can be assembled to meet the
demands of each task.
Stream Clipper: Scalable Submodular Maximization on Stream
Authors: Tianyi Zhou, Jeff Bilmes
Date: 2016-06-01 00:00:00
Tasks: Video Summarization

We propose a streaming submodular maximization algorithm "stream clipper"
that performs as well as the offline greedy algorithm on document/video
summarization in practice. It adds elements from a stream either to a solution
set $S$ or to an extra buffer $B$ based on two adaptive thresholds, and
improves $S$ by a final greedy step that starts from $S$ adding elements from
$B$. During this process, swapping elements out of $S$ can occur if doing so
yields improvements. The thresholds adapt based on if current memory
utilization exceeds a budget, e.g., it increases the lower threshold, and
removes from the buffer $B$ elements below the new lower threshold. We show
that, while our approximation factor in the worst case is $1/2$ (like in
previous work, and corresponding to the tight bound), we show that there are
data-dependent conditions where our bound falls within the range $[1/2,
1-1/e]$. In news and video summarization experiments, the algorithm
consistently outperforms other streaming methods, and, while using
significantly less computation and memory, performs similarly to the offline
greedy algorithm.
"How Was Your Weekend?" A Generative Model of Phatic Conversation
Authors: Hannah Morrison, Chris Martens
Date: 2018-02-13 00:00:00

Unspoken social rules, such as those that govern choosing a proper discussion
topic and when to change discussion topics, guide conversational behaviors. We
propose a computational model of conversation that can follow or break such
rules, with participant agents that respond accordingly. Additionally, we
demonstrate an application of the model: the Experimental Social Tutor (EST), a
first step toward a social skills training tool that generates human-readable
conversation and a conversational guideline at each point in the dialogue.
Finally, we discuss the design and results of a pilot study evaluating the EST.
Results show that our model is capable of producing conversations that follow
social norms.
Estimation and Optimization of Composite Outcomes
Authors: Daniel J. Luckett, Eric B. Laber, Michael R. Kosorok
Date: 2017-11-28 00:00:00

There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often must balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the proposed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression.
A comparative study of fairness-enhancing interventions in machine learning
Authors: Sorelle A. Friedler, Carlos Scheidegger, Suresh Venkatasubramanian, Sonam Choudhary, Evan P. Hamilton, Derek Roth
Date: 2018-02-13 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Computers are increasingly used to make decisions that have significant
impact in people's lives. Often, these predictions can affect different
population subgroups disproportionately. As a result, the issue of fairness has
received much recent interest, and a number of fairness-enhanced classifiers
and predictors have appeared in the literature. This paper seeks to study the
following questions: how do these different techniques fundamentally compare to
one another, and what accounts for the differences? Specifically, we seek to
bring attention to many under-appreciated aspects of such fairness-enhancing
interventions. Concretely, we present the results of an open benchmark we have
developed that lets us compare a number of different algorithms under a variety
of fairness measures, and a large number of existing datasets. We find that
although different algorithms tend to prefer specific formulations of fairness
preservations, many of these measures strongly correlate with one another. In
addition, we find that fairness-preserving algorithms tend to be sensitive to
fluctuations in dataset composition (simulated in our benchmark by varying
training-test splits), indicating that fairness interventions might be more
brittle than previously thought.
Towards Understanding the Generalization Bias of Two Layer Convolutional Linear Classifiers with Gradient Descent
Authors: Yifan Wu, Barnabas Poczos, Aarti Singh
Date: 2018-02-13 00:00:00

A major challenge in understanding the generalization of deep learning is to
explain why (stochastic) gradient descent can exploit the network architecture
to find solutions that have good generalization performance when using high
capacity models. We find simple but realistic examples showing that this
phenomenon exists even when learning linear classifiers --- between two linear
networks with the same capacity, the one with a convolutional layer can
generalize better than the other when the data distribution has some underlying
spatial structure. We argue that this difference results from a combination of
the convolution architecture, data distribution and gradient descent, all of
which are necessary to be included in a meaningful analysis. We provide a
general analysis of the generalization performance as a function of data
distribution and convolutional filter size, given gradient descent as the
optimization algorithm, then interpret the results using concrete examples.
Experimental results show that our analysis is able to explain what happens in
our introduced examples.
Neural Tensor Factorization
Authors: Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh Chawla
Date: 2018-02-13 00:00:00
Tasks: Collaborative Filtering, Link Prediction, Recommendation Systems

Neural collaborative filtering (NCF) and recurrent recommender systems (RRN)
have been successful in modeling user-item relational data. However, they are
also limited in their assumption of static or sequential modeling of relational
data as they do not account for evolving users' preference over time as well as
changes in the underlying factors that drive the change in user-item
relationship over time. We address these limitations by proposing a Neural
Tensor Factorization (NTF) model for predictive tasks on dynamic relational
data. The NTF model generalizes conventional tensor factorization from two
perspectives: First, it leverages the long short-term memory architecture to
characterize the multi-dimensional temporal interactions on relational data.
Second, it incorporates the multi-layer perceptron structure for learning the
non-linearities between different latent factors. Our extensive experiments
demonstrate the significant improvement in rating prediction and link
prediction on dynamic relational data by our NTF model over both neural network
based factorization models and other traditional methods.
Maturation Trajectories of Cortical Resting-State Networks Depend on the Mediating Frequency Band
Authors: Sheraz Khan, Javeria Hashmi, Fahimeh Mamashli, Konstantinos Michmizos, Manfred Kitzbichler, Hari Bharadwaj, Yousra Bekhti, Santosh Ganesan, Keri A Garel, Susan Whitfield-Gabrieli, Randy Gollub, Jian Kong, Lucia M Vaina, Kunjan Rana, Steven Stufflebeam, Matti Hamalainen, Tal Kenet
Date: 2018-02-13 00:00:00

The functional significance of resting state networks and their abnormal
manifestations in psychiatric disorders are firmly established, as is the
importance of the cortical rhythms in mediating these networks. Resting state
networks are known to undergo substantial reorganization from childhood to
adulthood, but whether distinct cortical rhythms, which are generated by
separable neural mechanisms and are often manifested abnormally in psychiatric
conditions, mediate maturation differentially, remains unknown. Using
magnetoencephalography (MEG) to map frequency band specific maturation of
resting state networks from age 7 to 29 in 162 participants (31 independent),
we found significant changes with age in networks mediated by the beta
(13-30Hz) and gamma (31-80Hz) bands. More specifically, gamma band mediated
networks followed an expected asymptotic trajectory, but beta band mediated
networks followed a linear trajectory. Network integration increased with age
in gamma band mediated networks, while local segregation increased with age in
beta band mediated networks. Spatially, the hubs that changed in importance
with age in the beta band mediated networks had relatively little overlap with
those that showed the greatest changes in the gamma band mediated networks.
These findings are relevant for our understanding of the neural mechanisms of
cortical maturation, in both typical and atypical development.
Combinatorial Inference for Graphical Models
Authors: Matey Neykov, Junwei Lu, Han Liu
Date: 2016-08-10 00:00:00

We propose a new family of combinatorial inference problems for graphical
models. Unlike classical statistical inference where the main interest is point
estimation or parameter testing, combinatorial inference aims at testing the
global structure of the underlying graph. Examples include testing the graph
connectivity, the presence of a cycle of certain size, or the maximum degree of
the graph. To begin with, we develop a unified theory for the fundamental
limits of a large family of combinatorial inference problems. We propose new
concepts including structural packing and buffer entropies to characterize how
the complexity of combinatorial graph structures impacts the corresponding
minimax lower bounds. On the other hand, we propose a family of novel and
practical structural testing algorithms to match the lower bounds. We provide
thorough numerical results on both synthetic graphical models and brain
networks to illustrate the usefulness of these proposed methods.
Efficient Exploration through Bayesian Deep Q-Networks
Authors: Kamyar Azizzadenesheli, Animashree Anandkumar
Date: 2018-02-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Atari Games, Efficient Exploration, regression, Reinforcement Learning, Reinforcement Learning (RL), Thompson Sampling

We study reinforcement learning (RL) in high dimensional episodic Markov decision processes (MDP). We consider value-based RL when the optimal Q-value is a linear function of d-dimensional state-action feature representation. For instance, in deep-Q networks (DQN), the Q-value is a linear function of the feature representation layer (output layer). We propose two algorithms, one based on optimism, LINUCB, and another based on posterior sampling, LINPSRL. We guarantee frequentist and Bayesian regret upper bounds of O(d sqrt{T}) for these two algorithms, where T is the number of episodes. We extend these methods to deep RL and propose Bayesian deep Q-networks (BDQN), which uses an efficient Thompson sampling algorithm for high dimensional RL. We deploy the double DQN (DDQN) approach, and instead of learning the last layer of Q-network using linear regression, we use Bayesian linear regression, resulting in an approximated posterior over Q-function. This allows us to directly incorporate the uncertainty over the Q-function and deploy Thompson sampling on the learned posterior distribution resulting in efficient exploration/exploitation trade-off. We empirically study the behavior of BDQN on a wide range of Atari games. Since BDQN carries out more efficient exploration and exploitation, it is able to reach higher return substantially faster compared to DDQN.
REAS: Combining Numerical Optimization with SAT Solving
Authors: Jeevana Priya Inala, Sicun Gao, Soonho Kong, Armando Solar-Lezama
Date: 2018-02-13 00:00:00

In this paper, we present ReaS, a technique that combines numerical
optimization with SAT solving to synthesize unknowns in a program that involves
discrete and floating point computation. ReaS makes the program end-to-end
differentiable by smoothing any Boolean expression that introduces
discontinuity such as conditionals and relaxing the Boolean unknowns so that
numerical optimization can be performed. On top of this, ReaS uses a SAT solver
to help the numerical search overcome local solutions by incrementally fixing
values to the Boolean expressions. We evaluated the approach on 5 case studies
involving hybrid systems and show that ReaS can synthesize programs that could
not be solved by previous SMT approaches.
Adversarially Regularized Graph Autoencoder for Graph Embedding
Authors: Shirui Pan, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, Chengqi Zhang
Date: 2018-02-13 00:00:00
Tasks: Clustering, Decoder, Graph Clustering, Graph Embedding, Link Prediction

Graph embedding is an effective method to represent graph data in a low
dimensional space for graph analytics. Most existing embedding algorithms
typically focus on preserving the topological structure or minimizing the
reconstruction errors of graph data, but they have mostly ignored the data
distribution of the latent codes from the graphs, which often results in
inferior embedding in real-world graph data. In this paper, we propose a novel
adversarial graph embedding framework for graph data. The framework encodes the
topological structure and node content in a graph to a compact representation,
on which a decoder is trained to reconstruct the graph structure. Furthermore,
the latent representation is enforced to match a prior distribution via an
adversarial training scheme. To learn a robust embedding, two variants of
adversarial approaches, adversarially regularized graph autoencoder (ARGA) and
adversarially regularized variational graph autoencoder (ARVGA), are developed.
Experimental studies on real-world graphs validate our design and demonstrate
that our algorithms outperform baselines by a wide margin in link prediction,
graph clustering, and graph visualization tasks.
LSALSA: Accelerated Source Separation via Learned Sparse Coding
Authors: Benjamin Cowen, Apoorva Nandini Saridena, Anna Choromanska
Date: 2018-02-13 00:00:00

We propose an efficient algorithm for the generalized sparse coding (SC) inference problem. The proposed framework applies to both the single dictionary setting, where each data point is represented as a sparse combination of the columns of one dictionary matrix, as well as the multiple dictionary setting as given in morphological component analysis (MCA), where the goal is to separate a signal into additive parts such that each part has distinct sparse representation within a corresponding dictionary. Both the SC task and its generalization via MCA have been cast as $\ell_1$-regularized least-squares optimization problems. To accelerate traditional acquisition of sparse codes, we propose a deep learning architecture that constitutes a trainable time-unfolded version of the Split Augmented Lagrangian Shrinkage Algorithm (SALSA), a special case of the Alternating Direction Method of Multipliers (ADMM). We empirically validate both variants of the algorithm, that we refer to as LSALSA (learned-SALSA), on image vision tasks and demonstrate that at inference our networks achieve vast improvements in terms of the running time, the quality of estimated sparse codes, and visual clarity on both classic SC and MCA problems. Finally, we present a theoretical framework for analyzing LSALSA network: we show that the proposed approach exactly implements a truncated ADMM applied to a new, learned cost function with curvature modified by one of the learned parameterized matrices. We extend a very recent Stochastic Alternating Optimization analysis framework to show that a gradient descent step along this learned loss landscape is equivalent to a modified gradient descent step along the original loss landscape. In this framework, the acceleration achieved by LSALSA could potentially be explained by the network's ability to learn a correction to the gradient direction of steeper descent.
CASSL: Curriculum Accelerated Self-Supervised Learning
Authors: Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, Abhinav Gupta
Date: 2017-08-04 00:00:00
Tasks: Self-Supervised Learning

Recent self-supervised learning approaches focus on using a few thousand data
points to learn policies for high-level, low-dimensional action spaces.
However, scaling this framework for high-dimensional control require either
scaling up the data collection efforts or using a clever sampling strategy for
training. We present a novel approach - Curriculum Accelerated Self-Supervised
Learning (CASSL) - to train policies that map visual information to high-level,
higher- dimensional action spaces. CASSL orders the sampling of training data
based on control dimensions: the learning and sampling are focused on few
control parameters before other parameters. The right curriculum for learning
is suggested by variance-based global sensitivity analysis of the control
space. We apply our CASSL framework to learning how to grasp using an adaptive,
underactuated multi-fingered gripper, a challenging system to control. Our
experimental results indicate that CASSL provides significant improvement and
generalization compared to baseline methods such as staged curriculum learning
(8% increase) and complete end-to-end learning with random exploration (14%
improvement) tested on a set of novel objects.
Image Retargetability
Authors: Fan Tang, Wei-Ming Dong, Yiping Meng, Chongyang Ma, Fuzhang Wu, Xinrui Li, Tong-Yee Lee
Date: 2018-02-12 00:00:00
Tasks: Image Retargeting

Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all images can be equally well processed that way. In this work, we introduce the notion of image retargetability to describe how well a particular image can be handled by content-aware image retargeting. We propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function. Our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem. To train and analyze this model, we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters. Experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels. To further validate our model, we show applications of image retargetability in retargeting method selection, retargeting method assessment and photo collage generation.