pwc-archive/papers-with-abstracts huggingface dataset:
Improving Character-based Decoding Using Target-Side Morphological Information for Neural Machine Translation
Authors: Peyman Passban, Qun Liu, Andy Way
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Decoder, Machine Translation, NMT, Translation

Recently, neural machine translation (NMT) has emerged as a powerful
alternative to conventional statistical approaches. However, its performance
drops considerably in the presence of morphologically rich languages (MRLs).
Neural engines usually fail to tackle the large vocabulary and high
out-of-vocabulary (OOV) word rate of MRLs. Therefore, it is not suitable to
exploit existing word-based models to translate this set of languages. In this
paper, we propose an extension to the state-of-the-art model of Chung et al.
(2016), which works at the character level and boosts the decoder with
target-side morphological information. In our architecture, an additional
morphology table is plugged into the model. Each time the decoder samples from
a target vocabulary, the table sends auxiliary signals from the most relevant
affixes in order to enrich the decoder's current state and constrain it to
provide better predictions. We evaluated our model to translate English into
German, Russian, and Turkish as three MRLs and observed significant
improvements.
Complementary Attributes: A New Clue to Zero-Shot Learning
Authors: Xiaofeng Xu, Ivor W. Tsang, Chuancai Liu
Date: 2018-04-17 00:00:00
Tasks: Attribute, Style Generalization, Zero-Shot Learning

Zero-shot learning (ZSL) aims to recognize unseen objects using disjoint seen objects via sharing attributes. The generalization performance of ZSL is governed by the attributes, which transfer semantic information from seen classes to unseen classes. To take full advantage of the knowledge transferred by attributes, in this paper, we introduce the notion of complementary attributes (CA), as a supplement to the original attributes, to enhance the semantic representation ability. Theoretical analyses demonstrate that complementary attributes can improve the PAC-style generalization bound of original ZSL model. Since the proposed CA focuses on enhancing the semantic representation, CA can be easily applied to any existing attribute-based ZSL methods, including the label-embedding strategy based ZSL (LEZSL) and the probability-prediction strategy based ZSL (PPZSL). In PPZSL, there is a strong assumption that all the attributes are independent of each other, which is arguably unrealistic in practice. To solve this problem, a novel rank aggregation framework is proposed to circumvent the assumption. Extensive experiments on five ZSL benchmark datasets and the large-scale ImageNet dataset demonstrate that the proposed complementary attributes and rank aggregation can significantly and robustly improve existing ZSL methods and achieve the state-of-the-art performance.
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks
Authors: Pichao Wang, Wanqing Li, Zhimin Gao, Chang Tang, Philip Ogunbona
Date: 2018-03-17 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Gesture Recognition, Temporal Action Localization

This paper proposes three simple, compact yet effective representations of
depth sequences, referred to respectively as Dynamic Depth Images (DDI),
Dynamic Depth Normal Images (DDNI) and Dynamic Depth Motion Normal Images
(DDMNI), for both isolated and continuous action recognition. These dynamic
images are constructed from a segmented sequence of depth maps using
hierarchical bidirectional rank pooling to effectively capture the
spatial-temporal information. Specifically, DDI exploits the dynamics of
postures over time and DDNI and DDMNI exploit the 3D structural information
captured by depth maps. Upon the proposed representations, a ConvNet based
method is developed for action recognition. The image-based representations
enable us to fine-tune the existing Convolutional Neural Network (ConvNet)
models trained on image data without training a large number of parameters from
scratch. The proposed method achieved the state-of-art results on three large
datasets, namely, the Large-scale Continuous Gesture Recognition Dataset (means
Jaccard index 0.4109), the Large-scale Isolated Gesture Recognition Dataset
(59.21%), and the NTU RGB+D Dataset (87.08% cross-subject and 84.22%
cross-view) even though only the depth modality was used.
Sim-to-Real Optimization of Complex Real World Mobile Network with Imperfect Information via Deep Reinforcement Learning from Self-play
Authors: Yongxi Tan, Jin Yang, Xin Chen, Qitao Song, Yunjun Chen, Zhangxiang Ye, Zhenqiang Su
Date: 2018-02-18 00:00:00
Tasks: Deep Reinforcement Learning, Multi-Task Learning, Reinforcement Learning, Transfer Learning

Mobile network that millions of people use every day is one of the most
complex systems in the world. Optimization of mobile network to meet exploding
customer demand and reduce capital/operation expenditures poses great
challenges. Despite recent progress, application of deep reinforcement learning
(DRL) to complex real world problem still remains unsolved, given data
scarcity, partial observability, risk and complex rules/dynamics in real world,
as well as the huge reality gap between simulation and real world. To bridge
the reality gap, we introduce a Sim-to-Real framework to directly transfer
learning from simulation to real world via graph convolutional neural network
(CNN) - by abstracting partially observable mobile network into graph, then
distilling domain-variant irregular graph into domain-invariant tensor in
locally Euclidean space as input to CNN -, domain randomization and multi-task
learning. We use a novel self-play mechanism to encourage competition among DRL
agents for best record on multiple tasks via simulated annealing, just like
athletes compete for world record in decathlon. We also propose a decentralized
multi-agent, competitive and cooperative DRL method to coordinate the actions
of multi-cells to maximize global reward and minimize negative impact to
neighbor cells. Using 6 field trials on commercial mobile networks, we
demonstrate for the first time that a DRL agent can successfully transfer
learning from simulation to complex real world problem with imperfect
information, complex rules/dynamics, huge state/action space, and multi-agent
interactions, without any training in the real world.
Two-Player Games for Efficient Non-Convex Constrained Optimization
Authors: Andrew Cotter, Heinrich Jiang, Karthik Sridharan
Date: 2018-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Valence Prediction

In recent years, constrained optimization has become increasingly relevant to
the machine learning community, with applications including Neyman-Pearson
classification, robust optimization, and fair machine learning. A natural
approach to constrained optimization is to optimize the Lagrangian, but this is
not guaranteed to work in the non-convex setting, and, if using a first-order
method, cannot cope with non-differentiable constraints (e.g. constraints on
rates or proportions).
  The Lagrangian can be interpreted as a two-player game played between a
player who seeks to optimize over the model parameters, and a player who wishes
to maximize over the Lagrange multipliers. We propose a non-zero-sum variant of
the Lagrangian formulation that can cope with non-differentiable--even
discontinuous--constraints, which we call the "proxy-Lagrangian". The first
player minimizes external regret in terms of easy-to-optimize "proxy
constraints", while the second player enforces the original constraints by
minimizing swap regret.
  For this new formulation, as for the Lagrangian in the non-convex setting,
the result is a stochastic classifier. For both the proxy-Lagrangian and
Lagrangian formulations, however, we prove that this classifier, instead of
having unbounded size, can be taken to be a distribution over no more than m+1
models (where m is the number of constraints). This is a significant
improvement in practical terms.
Deep Multimodal Subspace Clustering Networks
Authors: Mahdi Abavisani, Vishal M. Patel
Date: 2018-04-17 00:00:00
Tasks: Clustering, Decoder, Image Clustering, Multi-modal Subspace Clustering, Multiview Learning, Multi-view Subspace Clustering

We present convolutional neural network (CNN) based approaches for
unsupervised multimodal subspace clustering. The proposed framework consists of
three main stages - multimodal encoder, self-expressive layer, and multimodal
decoder. The encoder takes multimodal data as input and fuses them to a latent
space representation. The self-expressive layer is responsible for enforcing
the self-expressiveness property and acquiring an affinity matrix corresponding
to the data points. The decoder reconstructs the original input data. The
network uses the distance between the decoder's reconstruction and the original
input in its training. We investigate early, late and intermediate fusion
techniques and propose three different encoders corresponding to them for
spatial fusion. The self-expressive layers and multimodal decoders are
essentially the same for different spatial fusion-based approaches. In addition
to various spatial fusion-based methods, an affinity fusion-based network is
also proposed in which the self-expressive layer corresponding to different
modalities is enforced to be the same. Extensive experiments on three datasets
show that the proposed methods significantly outperform the state-of-the-art
multimodal subspace clustering methods.
Multi-Level Factorisation Net for Person Re-Identification
Authors: Xiaobin Chang, Timothy M. Hospedales, Tao Xiang
Date: 2018-03-24 00:00:00
Proceeding: CVPR 2018 6
Tasks: Person Re-Identification

Key to effective person re-identification (Re-ID) is modelling discriminative
and view-invariant factors of person appearance at both high and low semantic
levels. Recently developed deep Re-ID models either learn a holistic single
semantic level feature representation and/or require laborious human annotation
of these factors as attributes. We propose Multi-Level Factorisation Net
(MLFN), a novel network architecture that factorises the visual appearance of a
person into latent discriminative factors at multiple semantic levels without
manual annotation. MLFN is composed of multiple stacked blocks. Each block
contains multiple factor modules to model latent factors at a specific level,
and factor selection modules that dynamically select the factor modules to
interpret the content of each input image. The outputs of the factor selection
modules also provide a compact latent factor descriptor that is complementary
to the conventional deeply learned features. MLFN achieves state-of-the-art
results on three Re-ID datasets, as well as compelling results on the general
object categorisation CIFAR-100 dataset.
Robust Machine Comprehension Models via Adversarial Training
Authors: Yicheng Wang, Mohit Bansal
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data Augmentation, Question Answering, Reading Comprehension

It is shown that many published models for the Stanford Question Answering
Dataset (Rajpurkar et al., 2016) lack robustness, suffering an over 50%
decrease in F1 score during adversarial evaluation based on the AddSent (Jia
and Liang, 2017) algorithm. It has also been shown that retraining models on
data generated by AddSent has limited effect on their robustness. We propose a
novel alternative adversary-generation algorithm, AddSentDiverse, that
significantly increases the variance within the adversarial training data by
providing effective examples that punish the model for making certain
superficial assumptions. Further, in order to improve robustness to AddSent's
semantic perturbations (e.g., antonyms), we jointly improve the model's
semantic-relationship learning capabilities in addition to our
AddSentDiverse-based adversarial training data augmentation. With these
additions, we show that we can make a state-of-the-art model significantly more
robust, achieving a 36.5% increase in F1 score under many different types of
adversarial evaluation while maintaining performance on the regular SQuAD task.
Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement
Authors: Jason Lee, Elman Mansimov, Kyunghyun Cho
Date: 2018-02-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Caption Generation, Denoising, Machine Translation, Translation

We propose a conditional non-autoregressive neural sequence model based on
iterative refinement. The proposed model is designed based on the principles of
latent variable models and denoising autoencoders, and is generally applicable
to any sequence generation task. We extensively evaluate the proposed model on
machine translation (En-De and En-Ro) and image caption generation, and observe
that it significantly speeds up decoding while maintaining the generation
quality comparable to the autoregressive counterpart.
An Adaptive Clipping Approach for Proximal Policy Optimization
Authors: Gang Chen, Yiming Peng, Mengjie Zhang
Date: 2018-04-17 00:00:00
Tasks: Learning Theory, Reinforcement Learning

Very recently proximal policy optimization (PPO) algorithms have been
proposed as first-order optimization methods for effective reinforcement
learning. While PPO is inspired by the same learning theory that justifies
trust region policy optimization (TRPO), PPO substantially simplifies algorithm
design and improves data efficiency by performing multiple epochs of
\emph{clipped policy optimization} from sampled data. Although clipping in PPO
stands for an important new mechanism for efficient and reliable policy update,
it may fail to adaptively improve learning performance in accordance with the
importance of each sampled state. To address this issue, a new surrogate
learning objective featuring an adaptive clipping mechanism is proposed in this
paper, enabling us to develop a new algorithm, known as PPO-$\lambda$.
PPO-$\lambda$ optimizes policies repeatedly based on a theoretical target for
adaptive policy improvement. Meanwhile, destructively large policy update can
be effectively prevented through both clipping and adaptive control of a
hyperparameter $\lambda$ in PPO-$\lambda$, ensuring high learning reliability.
PPO-$\lambda$ enjoys the same simple and efficient design as PPO. Empirically
on several Atari game playing tasks and benchmark control tasks, PPO-$\lambda$
also achieved clearly better performance than PPO.
On Learning Intrinsic Rewards for Policy Gradient Methods
Authors: Zeyu Zheng, Junhyuk Oh, Satinder Singh
Date: 2018-04-17 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Atari Games, Decision Making, MuJoCo, Policy Gradient Methods, Sequential Decision Making

In many sequential decision making tasks, it is challenging to design reward
functions that help an RL agent efficiently learn behavior that is considered
good by the agent designer. A number of different formulations of the
reward-design problem, or close variants thereof, have been proposed in the
literature. In this paper we build on the Optimal Rewards Framework of Singh
et.al. that defines the optimal intrinsic reward function as one that when used
by an RL agent achieves behavior that optimizes the task-specifying or
extrinsic reward function. Previous work in this framework has shown how good
intrinsic reward functions can be learned for lookahead search based planning
agents. Whether it is possible to learn intrinsic reward functions for learning
agents remains an open problem. In this paper we derive a novel algorithm for
learning intrinsic rewards for policy-gradient based learning agents. We
compare the performance of an augmented agent that uses our algorithm to
provide additive intrinsic rewards to an A2C-based policy learner (for Atari
games) and a PPO-based policy learner (for Mujoco domains) with a baseline
agent that uses the same policy learners but with only extrinsic rewards. Our
results show improved performance on most but not all of the domains.
Deep Probabilistic Programming Languages: A Qualitative Study
Authors: Guillaume Baudart, Martin Hirzel, Louis Mandel
Date: 2018-04-17 00:00:00
Tasks: BIG-bench Machine Learning, Probabilistic Programming

Deep probabilistic programming languages try to combine the advantages of
deep learning with those of probabilistic programming languages. If successful,
this would be a big step forward in machine learning and programming languages.
Unfortunately, as of now, this new crop of languages is hard to use and
understand. This paper addresses this problem directly by explaining deep
probabilistic programming languages and indirectly by characterizing their
current strengths and weaknesses.
Local Descriptors Optimized for Average Precision
Authors: Kun He, Yan Lu, Stan Sclaroff
Date: 2018-04-15 00:00:00
Proceeding: CVPR 2018 6
Tasks: Learning-To-Rank, Retrieval

Extraction of local feature descriptors is a vital stage in the solution
pipelines for numerous computer vision tasks. Learning-based approaches improve
performance in certain tasks, but still cannot replace handcrafted features in
general. In this paper, we improve the learning of local feature descriptors by
optimizing the performance of descriptor matching, which is a common stage that
follows descriptor extraction in local feature based pipelines, and can be
formulated as nearest neighbor retrieval. Specifically, we directly optimize a
ranking-based retrieval performance metric, Average Precision, using deep
neural networks. This general-purpose solution can also be viewed as a listwise
learning to rank approach, which is advantageous compared to recent local
ranking approaches. On standard benchmarks, descriptors learned with our
formulation achieve state-of-the-art results in patch verification, patch
retrieval, and image matching.
Detecting Linguistic Characteristics of Alzheimer's Dementia by Interpreting Neural Models
Authors: Sweta Karlekar, Tong Niu, Mohit Bansal
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Clustering

Alzheimer's disease (AD) is an irreversible and progressive brain disease
that can be stopped or slowed down with medical treatment. Language changes
serve as a sign that a patient's cognitive functions have been impacted,
potentially leading to early diagnosis. In this work, we use NLP techniques to
classify and analyze the linguistic characteristics of AD patients using the
DementiaBank dataset. We apply three neural models based on CNNs, LSTM-RNNs,
and their combination, to distinguish between language samples from AD and
control patients. We achieve a new independent benchmark accuracy for the AD
classification task. More importantly, we next interpret what these neural
models have learned about the linguistic characteristics of AD patients, via
analysis based on activation clustering and first-derivative saliency
techniques. We then perform novel automatic pattern discovery inside activation
clusters, and consolidate AD patients' distinctive grammar patterns.
Additionally, we show that first derivative saliency can not only rediscover
previous language patterns of AD patients, but also shed light on the
limitations of neural models. Lastly, we also include analysis of
gender-separated AD data.
MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
Authors: Ariel Gordon, Elad Eban, Ofir Nachum, Bo Chen, Hao Wu, Tien-Ju Yang, Edward Choi
Date: 2017-11-18 00:00:00
Proceeding: CVPR 2018 6
Tasks: Neural Architecture Search

We present MorphNet, an approach to automate the design of neural network
structures. MorphNet iteratively shrinks and expands a network, shrinking via a
resource-weighted sparsifying regularizer on activations and expanding via a
uniform multiplicative factor on all layers. In contrast to previous
approaches, our method is scalable to large networks, adaptable to specific
resource constraints (e.g. the number of floating-point operations per
inference), and capable of increasing the network's performance. When applied
to standard network architectures on a wide variety of datasets, our approach
discovers novel structures in each domain, obtaining higher performance while
respecting the resource constraint.
Vision Based Dynamic Offside Line Marker for Soccer Games
Authors: Karthik Muthuraman, Pranav Joshi, Suraj Kiran Raman
Date: 2018-04-17 00:00:00
Tasks: Quantization

Offside detection in soccer has emerged as one of the most important
decisions with an average of 50 offside decisions every game. False detections
and rash calls adversely affect game conditions and in many cases drastically
change the outcome of the game. The human eye has finite precision and can only
discern a limited amount of detail in a given instance. Current offside
decisions are made manually by sideline referees and tend to remain
controversial in many games. This calls for automated offside detection
techniques in order to assist accurate refereeing. In this work, we have
explicitly used computer vision and image processing techniques like Hough
transform, color similarity (quantization), graph connected components, and
vanishing point ideas to identify the probable offside regions.
  Keywords: Hough transform, connected components, KLT tracking, color
similarity.
Delete, Retrieve, Generate: A Simple Approach to Sentiment and Style Transfer
Authors: Juncen Li, Robin Jia, He He, Percy Liang
Date: 2018-04-17 00:00:00
Proceeding: NAACL 2018 6
Tasks: Attribute, Image Captioning, Sentence, Style Transfer, Text Attribute Transfer, Unsupervised Text Style Transfer

We consider the task of text attribute transfer: transforming a sentence to
alter a specific attribute (e.g., sentiment) while preserving its
attribute-independent content (e.g., changing "screen is just the right size"
to "screen is too small"). Our training data includes only sentences labeled
with their attribute (e.g., positive or negative), but not pairs of sentences
that differ only in their attributes, so we must learn to disentangle
attributes from attribute-independent content in an unsupervised way. Previous
work using adversarial methods has struggled to produce high-quality outputs.
In this paper, we propose simpler methods motivated by the observation that
text attributes are often marked by distinctive phrases (e.g., "too small").
Our strongest method extracts content words by deleting phrases associated with
the sentence's original attribute value, retrieves new phrases associated with
the target attribute, and uses a neural model to fluently combine these into a
final output. On human evaluation, our best method generates grammatical and
appropriate responses on 22% more inputs than the best previous system,
averaged over three attribute transfer datasets: altering sentiment of reviews
on Yelp, altering sentiment of reviews on Amazon, and altering image captions
to be more romantic or humorous.
Evolutionary Architecture Search For Deep Multitask Networks
Authors: Jason Liang, Elliot Meyerson, Risto Miikkulainen
Date: 2018-03-10 00:00:00
Tasks: Neural Architecture Search

Multitask learning, i.e. learning several tasks at once with the same neural
network, can improve performance in each of the tasks. Designing deep neural
network architectures for multitask learning is a challenge: There are many
ways to tie the tasks together, and the design choices matter. The size and
complexity of this problem exceeds human design ability, making it a compelling
domain for evolutionary optimization. Using the existing state of the art soft
ordering architecture as the starting point, methods for evolving the modules
of this architecture and for evolving the overall topology or routing between
modules are evaluated in this paper. A synergetic approach of evolving custom
routings with evolved, shared modules for each task is found to be very
powerful, significantly improving the state of the art in the Omniglot
multitask, multialphabet character recognition domain. This result demonstrates
how evolution can be instrumental in advancing deep neural network and complex
system design in general.