pwc-archive/papers-with-abstracts huggingface dataset:
A hybrid approach of interpolations and CNN to obtain super-resolution
Authors: Ram Krishna Pandey, A. G. Ramakrishnan
Date: 2018-05-23 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

We propose a novel architecture that learns an end-to-end mapping function to
improve the spatial resolution of the input natural images. The model is unique
in forming a nonlinear combination of three traditional interpolation
techniques using the convolutional neural network. Another proposed
architecture uses a skip connection with nearest neighbor interpolation,
achieving almost similar results. The architectures have been carefully
designed to ensure that the reconstructed images lie precisely in the manifold
of high-resolution images, thereby preserving the high-frequency components
with fine details. We have compared with the state of the art and recent deep
learning based natural image super-resolution techniques and found that our
methods are able to preserve the sharp details in the image, while also
obtaining comparable or better PSNR than them. Since our methods use only
traditional interpolations and a shallow CNN with less number of smaller
filters, the computational cost is kept low. We have reported the results of
two proposed architectures on five standard datasets, for an upscale factor of
2. Our methods generalize well in most cases, which is evident from the better
results obtained with increasingly complex datasets. For 4-times upscaling, we
have designed similar architectures for comparing with other methods.
Markov Chain Importance Sampling -- a highly efficient estimator for MCMC
Authors: Ingmar Schuster, Ilja Klebanov
Date: 2018-05-18 00:00:00
Tasks: BIG-bench Machine Learning, CPU

Markov chain (MC) algorithms are ubiquitous in machine learning and statistics and many other disciplines. Typically, these algorithms can be formulated as acceptance rejection methods. In this work we present a novel estimator applicable to these methods, dubbed Markov chain importance sampling (MCIS), which efficiently makes use of rejected proposals. For the unadjusted Langevin algorithm, it provides a novel way of correcting the discretization error. Our estimator satisfies a central limit theorem and improves on error per CPU cycle, often to a large extent. As a by-product it enables estimating the normalizing constant, an important quantity in Bayesian machine learning and statistics.
A convergence framework for inexact nonconvex and nonsmooth algorithms and its applications to several iterations
Authors: Tao Sun, Hao Jiang, Li-Zhi Cheng, Wei Zhu
Date: 2017-09-12 00:00:00

In this paper, we consider the convergence of an abstract inexact nonconvex
and nonsmooth algorithm. We promise a pseudo sufficient descent condition and a
pseudo relative error condition, which are both related to an auxiliary
sequence, for the algorithm; and a continuity condition is assumed to hold. In
fact, a lot of classical inexact nonconvex and nonsmooth algorithms allow these
three conditions. Under a special kind of summable assumption on the auxiliary
sequence, we prove the sequence generated by the general algorithm converges to
a critical point of the objective function if being assumed Kurdyka-
Lojasiewicz property. The core of the proofs lies in building a new Lyapunov
function, whose successive difference provides a bound for the successive
difference of the points generated by the algorithm. And then, we apply our
findings to several classical nonconvex iterative algorithms and derive the
corresponding convergence results
A Generative Model For Electron Paths
Authors: John Bradshaw, Matt J. Kusner, Brooks Paige, Marwin H. S. Segler, José Miguel Hernández-Lobato
Date: 2018-05-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: model, One-Shot Learning

Chemical reactions can be described as the stepwise redistribution of
electrons in molecules. As such, reactions are often depicted using
`arrow-pushing' diagrams which show this movement as a sequence of arrows. We
propose an electron path prediction model (ELECTRO) to learn these sequences
directly from raw reaction data. Instead of predicting product molecules
directly from reactant molecules in one shot, learning a model of electron
movement has the benefits of (a) being easy for chemists to interpret, (b)
incorporating constraints of chemistry, such as balanced atom counts before and
after the reaction, and (c) naturally encoding the sparsity of chemical
reactions, which usually involve changes in only a small number of atoms in the
reactants.We design a method to extract approximate reaction paths from any
dataset of atom-mapped reaction SMILES strings. Our model achieves excellent
performance on an important subset of the USPTO reaction dataset, comparing
favorably to the strongest baselines. Furthermore, we show that our model
recovers a basic knowledge of chemistry without being explicitly trained to do
so.
Embedding Syntax and Semantics of Prepositions via Tensor Decomposition
Authors: Hongyu Gong, Suma Bhat, Pramod Viswanath
Date: 2018-05-23 00:00:00
Proceeding: NAACL 2018 6
Tasks: Tensor Decomposition

Prepositions are among the most frequent words in English and play complex
roles in the syntax and semantics of sentences. Not surprisingly, they pose
well-known difficulties in automatic processing of sentences (prepositional
attachment ambiguities and idiosyncratic uses in phrases). Existing methods on
preposition representation treat prepositions no different from content words
(e.g., word2vec and GloVe). In addition, recent studies aiming at solving
prepositional attachment and preposition selection problems depend heavily on
external linguistic resources and use dataset-specific word representations. In
this paper we use word-triple counts (one of the triples being a preposition)
to capture a preposition's interaction with its attachment and complement. We
then derive preposition embeddings via tensor decomposition on a large
unlabeled corpus. We reveal a new geometry involving Hadamard products and
empirically demonstrate its utility in paraphrasing phrasal verbs. Furthermore,
our preposition embeddings are used as simple features in two challenging
downstream tasks: preposition selection and prepositional attachment
disambiguation. We achieve results comparable to or better than the
state-of-the-art on multiple standardized datasets.
Regret Bounds for Robust Adaptive Control of the Linear Quadratic Regulator
Authors: Sarah Dean, Horia Mania, Nikolai Matni, Benjamin Recht, Stephen Tu
Date: 2018-05-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Demand Forecasting, parameter estimation

We consider adaptive control of the Linear Quadratic Regulator (LQR), where
an unknown linear system is controlled subject to quadratic costs. Leveraging
recent developments in the estimation of linear systems and in robust
controller synthesis, we present the first provably polynomial time algorithm
that provides high probability guarantees of sub-linear regret on this problem.
We further study the interplay between regret minimization and parameter
estimation by proving a lower bound on the expected regret in terms of the
exploration schedule used by any algorithm. Finally, we conduct a numerical
study comparing our robust adaptive algorithm to other methods from the
adaptive LQR literature, and demonstrate the flexibility of our proposed method
by extending it to a demand forecasting problem subject to state constraints.
Quantifying the visual concreteness of words and topics in multimodal datasets
Authors: Jack Hessel, David Mimno, Lillian Lee
Date: 2018-04-18 00:00:00
Proceeding: NAACL 2018 6
Tasks: BIG-bench Machine Learning, Image Captioning

Multimodal machine learning algorithms aim to learn visual-textual
correspondences. Previous work suggests that concepts with concrete visual
manifestations may be easier to learn than concepts with abstract ones. We give
an algorithm for automatically computing the visual concreteness of words and
topics within multimodal datasets. We apply the approach in four settings,
ranging from image captions to images/text scraped from historical books. In
addition to enabling explorations of concepts in multimodal datasets, our
concreteness scores predict the capacity of machine learning algorithms to
learn textual/visual relationships. We find that 1) concrete concepts are
indeed easier to learn; 2) the large number of algorithms we consider have
similar failure cases; 3) the precise positive relationship between
concreteness and performance varies between datasets. We conclude with
recommendations for using concreteness scores to facilitate future multimodal
research.
Predictive Local Smoothness for Stochastic Gradient Methods
Authors: Jun Li, Hongfu Liu, Bineng Zhong, Yue Wu, Yun Fu
Date: 2018-05-23 00:00:00
Proceeding: ICLR 2019 5

Stochastic gradient methods are dominant in nonconvex optimization especially
for deep models but have low asymptotical convergence due to the fixed
smoothness. To address this problem, we propose a simple yet effective method
for improving stochastic gradient methods named predictive local smoothness
(PLS). First, we create a convergence condition to build a learning rate which
varies adaptively with local smoothness. Second, the local smoothness can be
predicted by the latest gradients. Third, we use the adaptive learning rate to
update the stochastic gradients for exploring linear convergence rates. By
applying the PLS method, we implement new variants of three popular algorithms:
PLS-stochastic gradient descent (PLS-SGD), PLS-accelerated SGD (PLS-AccSGD),
and PLS-AMSGrad. Moreover, we provide much simpler proofs to ensure their
linear convergence. Empirical results show that the variants have better
performance gains than the popular algorithms, such as, faster convergence and
alleviating explosion and vanish of gradients.
Anonymizing k-Facial Attributes via Adversarial Perturbations
Authors: Saheb Chhabra, Richa Singh, Mayank Vatsa, Gaurav Gupta
Date: 2018-05-23 00:00:00
Tasks: Attribute

A face image not only provides details about the identity of a subject but
also reveals several attributes such as gender, race, sexual orientation, and
age. Advancements in machine learning algorithms and popularity of sharing
images on the World Wide Web, including social media websites, have increased
the scope of data analytics and information profiling from photo collections.
This poses a serious privacy threat for individuals who do not want to be
profiled. This research presents a novel algorithm for anonymizing selective
attributes which an individual does not want to share without affecting the
visual quality of images. Using the proposed algorithm, a user can select
single or multiple attributes to be surpassed while preserving identity
information and visual content. The proposed adversarial perturbation based
algorithm embeds imperceptible noise in an image such that attribute prediction
algorithm for the selected attribute yields incorrect classification result,
thereby preserving the information according to user's choice. Experiments on
three popular databases i.e. MUCT, LFWcrop, and CelebA show that the proposed
algorithm not only anonymizes k-attributes, but also preserves image quality
and identity information.
Tell Me Something New: A New Framework for Asynchronous Parallel Learning
Authors: Julaiti Alafate, Yoav Freund
Date: 2018-05-19 00:00:00
Tasks: BIG-bench Machine Learning, Splice Site Prediction

We present a novel approach for parallel computation in the context of
machine learning that we call "Tell Me Something New" (TMSN). This approach
involves a set of independent workers that use broadcast to update each other
when they observe "something new". TMSN does not require synchronization or a
head node and is highly resilient against failing machines or laggards. We
demonstrate the utility of TMSN by applying it to learning boosted trees. We
show that our implementation is 10 times faster than XGBoost and LightGBM on
the splice-site prediction problem.
Tree-CNN: A Hierarchical Deep Convolutional Neural Network for Incremental Learning
Authors: Deboleena Roy, Priyadarshini Panda, Kaushik Roy
Date: 2018-02-15 00:00:00
Tasks: Incremental Learning, Object Recognition

Over the past decade, Deep Convolutional Neural Networks (DCNNs) have shown remarkable performance in most computer vision tasks. These tasks traditionally use a fixed dataset, and the model, once trained, is deployed as is. Adding new information to such a model presents a challenge due to complex training issues, such as "catastrophic forgetting", and sensitivity to hyper-parameter tuning. However, in this modern world, data is constantly evolving, and our deep learning models are required to adapt to these changes. In this paper, we propose an adaptive hierarchical network structure composed of DCNNs that can grow and learn as new data becomes available. The network grows in a tree-like fashion to accommodate new classes of data, while preserving the ability to distinguish the previously trained classes. The network organizes the incrementally available data into feature-driven super-classes and improves upon existing hierarchical CNN models by adding the capability of self-growth. The proposed hierarchical model, when compared against fine-tuning a deep network, achieves significant reduction of training effort, while maintaining competitive accuracy on CIFAR-10 and CIFAR-100.
Learning Depthwise Separable Graph Convolution from Data Manifold
Authors: Guokun Lai, Hanxiao Liu, Yiming Yang
Date: 2017-10-31 00:00:00
Tasks: Computational Efficiency

Convolution Neural Network (CNN) has gained tremendous success in computer
vision tasks with its outstanding ability to capture the local latent features.
Recently, there has been an increasing interest in extending convolution
operations to the non-Euclidean geometry. Although various types of convolution
operations have been proposed for graphs or manifolds, their connections with
traditional convolution over grid-structured data are not well-understood. In
this paper, we show that depthwise separable convolution can be successfully
generalized for the unification of both graph-based and grid-based convolution
methods. Based on this insight we propose a novel Depthwise Separable Graph
Convolution (DSGC) approach which is compatible with the tradition convolution
network and subsumes existing convolution methods as special cases. It is
equipped with the combined strengths in model expressiveness, compatibility
(relatively small number of parameters), modularity and computational
efficiency in training. Extensive experiments show the outstanding performance
of DSGC in comparison with strong baselines on multi-domain benchmark datasets.
Semi-supervised classification by reaching consensus among modalities
Authors: Zining Zhu, Jekaterina Novikova, Frank Rudzicz
Date: 2018-05-23 00:00:00
Tasks: Classification, General Classification, Marketing

Deep learning has demonstrated abilities to learn complex structures, but
they can be restricted by available data. Recently, Consensus Networks (CNs)
were proposed to alleviate data sparsity by utilizing features from multiple
modalities, but they too have been limited by the size of labeled data. In this
paper, we extend CN to Transductive Consensus Networks (TCNs), suitable for
semi-supervised learning. In TCNs, different modalities of input are compressed
into latent representations, which we encourage to become indistinguishable
during iterative adversarial training. To understand TCNs two mechanisms,
consensus and classification, we put forward its three variants in ablation
studies on these mechanisms. To further investigate TCN models, we treat the
latent representations as probability distributions and measure their
similarities as the negative relative Jensen-Shannon divergences. We show that
a consensus state beneficial for classification desires a stable but imperfect
similarity between the representations. Overall, TCNs outperform or align with
the best benchmark algorithms given 20 to 200 labeled samples on the Bank
Marketing and the DementiaBank datasets.
Learning Contextual Bandits in a Non-stationary Environment
Authors: Qingyun Wu, Naveen Iyer, Hongning Wang
Date: 2018-05-23 00:00:00
Tasks: Multi-Armed Bandits, Recommendation Systems

Multi-armed bandit algorithms have become a reference solution for handling
the explore/exploit dilemma in recommender systems, and many other important
real-world problems, such as display advertisement. However, such algorithms
usually assume a stationary reward distribution, which hardly holds in practice
as users' preferences are dynamic. This inevitably costs a recommender system
consistent suboptimal performance. In this paper, we consider the situation
where the underlying distribution of reward remains unchanged over (possibly
short) epochs and shifts at unknown time instants. In accordance, we propose a
contextual bandit algorithm that detects possible changes of environment based
on its reward estimation confidence and updates its arm selection strategy
respectively. Rigorous upper regret bound analysis of the proposed algorithm
demonstrates its learning effectiveness in such a non-trivial environment.
Extensive empirical evaluations on both synthetic and real-world datasets for
recommendation confirm its practical utility in a changing environment.
More Robust Doubly Robust Off-policy Evaluation
Authors: Mehrdad Farajtabar, Yin-Lam Chow, Mohammad Ghavamzadeh
Date: 2018-02-10 00:00:00
Proceeding: ICML 2018 7
Tasks: Multi-Armed Bandits, Off-policy evaluation, Reinforcement Learning, Reinforcement Learning (RL)

We study the problem of off-policy evaluation (OPE) in reinforcement learning
(RL), where the goal is to estimate the performance of a policy from the data
generated by another policy(ies). In particular, we focus on the doubly robust
(DR) estimators that consist of an importance sampling (IS) component and a
performance model, and utilize the low (or zero) bias of IS and low variance of
the model at the same time. Although the accuracy of the model has a huge
impact on the overall performance of DR, most of the work on using the DR
estimators in OPE has been focused on improving the IS part, and not much on
how to learn the model. In this paper, we propose alternative DR estimators,
called more robust doubly robust (MRDR), that learn the model parameter by
minimizing the variance of the DR estimator. We first present a formulation for
learning the DR model in RL. We then derive formulas for the variance of the DR
estimator in both contextual bandits and RL, such that their gradients
w.r.t.~the model parameters can be estimated from the samples, and propose
methods to efficiently minimize the variance. We prove that the MRDR estimators
are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR
in bandits and RL benchmark problems, and compare its performance with the
existing methods.
A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research
Authors: Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth
Date: 2018-02-26 00:00:00
Tasks: Vocal Bursts Type Prediction

Having a quality annotated corpus is essential especially for applied
research. Despite the recent focus of Web science community on researching
about cyberbullying, the community dose not still have standard benchmarks. In
this paper, we publish first, a quality annotated corpus and second, an
offensive words lexicon capturing different types type of harassment as (i)
sexual harassment, (ii) racial harassment, (iii) appearance-related harassment,
(iv) intellectual harassment, and (v) political harassment.We crawled data from
Twitter using our offensive lexicon. Then relied on the human judge to annotate
the collected tweets w.r.t. the contextual types because using offensive words
is not sufficient to reliably detect harassment. Our corpus consists of 25,000
annotated tweets in five contextual types. We are pleased to share this novel
annotated corpus and the lexicon with the research community. The instruction
to acquire the corpus has been published on the Git repository.
Deep Reinforcement Learning of Marked Temporal Point Processes
Authors: Utkarsh Upadhyay, Abir De, Manuel Gomez-Rodriguez
Date: 2018-05-23 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Deep Reinforcement Learning, Marketing, Point Processes, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In a wide variety of applications, humans interact with a complex environment
by means of asynchronous stochastic discrete events in continuous time. Can we
design online interventions that will help humans achieve certain goals in such
asynchronous setting? In this paper, we address the above problem from the
perspective of deep reinforcement learning of marked temporal point processes,
where both the actions taken by an agent and the feedback it receives from the
environment are asynchronous stochastic discrete events characterized using
marked temporal point processes. In doing so, we define the agent's policy
using the intensity and mark distribution of the corresponding process and then
derive a flexible policy gradient method, which embeds the agent's actions and
the feedback it receives into real-valued vectors using deep recurrent neural
networks. Our method does not make any assumptions on the functional form of
the intensity and mark distribution of the feedback and it allows for
arbitrarily complex reward functions. We apply our methodology to two different
applications in personalized teaching and viral marketing and, using data
gathered from Duolingo and Twitter, we show that it may be able to find
interventions to help learners and marketers achieve their goals more
effectively than alternatives.
Scoring Lexical Entailment with a Supervised Directional Similarity Network
Authors: Marek Rei, Daniela Gerz, Ivan Vulić
Date: 2018-05-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Lexical Entailment, Word Embeddings

We present the Supervised Directional Similarity Network (SDSN), a novel
neural architecture for learning task-specific transformation functions on top
of general-purpose word embeddings. Relying on only a limited amount of
supervision from task-specific scores on a subset of the vocabulary, our
architecture is able to generalise and transform a general-purpose
distributional vector space to model the relation of lexical entailment.
Experiments show excellent performance on scoring graded lexical entailment,
raising the state-of-the-art on the HyperLex dataset by approximately 25%.
Working Memory Networks: Augmenting Memory Networks with a Relational Reasoning Module
Authors: Juan Pavez, Héctor Allende, Héctor Allende-Cid
Date: 2018-05-23 00:00:00
Proceeding: ACL 2018 7
Tasks: Relational Reasoning

During the last years, there has been a lot of interest in achieving some
kind of complex reasoning using deep neural networks. To do that, models like
Memory Networks (MemNNs) have combined external memory storages and attention
mechanisms. These architectures, however, lack of more complex reasoning
mechanisms that could allow, for instance, relational reasoning. Relation
Networks (RNs), on the other hand, have shown outstanding results in relational
reasoning tasks. Unfortunately, their computational cost grows quadratically
with the number of memories, something prohibitive for larger problems. To
solve these issues, we introduce the Working Memory Network, a MemNN
architecture with a novel working memory storage and reasoning module. Our
model retains the relational reasoning abilities of the RN while reducing its
computational complexity from quadratic to linear. We tested our model on the
text QA dataset bAbI and the visual QA dataset NLVR. In the jointly trained
bAbI-10k, we set a new state-of-the-art, achieving a mean error of less than
0.5%. Moreover, a simple ensemble of two of our models solves all 20 tasks in
the joint version of the benchmark.