pwc-archive/papers-with-abstracts huggingface dataset:
UCT: Learning Unified Convolutional Networks for Real-time Visual Tracking
Authors: Zheng Zhu, Guan Huang, Wei Zou, Dalong Du, Chang Huang
Date: 2017-11-10 00:00:00
Tasks: Real-Time Visual Tracking, Visual Tracking

Convolutional neural networks (CNN) based tracking approaches have shown
favorable performance in recent benchmarks. Nonetheless, the chosen CNN
features are always pre-trained in different task and individual components in
tracking systems are learned separately, thus the achieved tracking performance
may be suboptimal. Besides, most of these trackers are not designed towards
real-time applications because of their time-consuming feature extraction and
complex optimization details.In this paper, we propose an end-to-end framework
to learn the convolutional features and perform the tracking process
simultaneously, namely, a unified convolutional tracker (UCT). Specifically,
The UCT treats feature extractor and tracking process both as convolution
operation and trains them jointly, enabling learned CNN features are tightly
coupled to tracking process. In online tracking, an efficient updating method
is proposed by introducing peak-versus-noise ratio (PNR) criterion, and scale
changes are handled efficiently by incorporating a scale branch into network.
The proposed approach results in superior tracking performance, while
maintaining real-time speed. The standard UCT and UCT-Lite can track generic
objects at 41 FPS and 154 FPS without further optimization, respectively.
Experiments are performed on four challenging benchmark tracking datasets:
OTB2013, OTB2015, VOT2014 and VOT2015, and our method achieves state-of-the-art
results on these benchmarks compared with other real-time trackers.
Joint Sentiment/Topic Modeling on Text Data Using Boosted Restricted Boltzmann Machine
Authors: Masoud Fatemi, Mehran Safayani
Date: 2017-11-10 00:00:00
Tasks: General Classification, Information Retrieval, Retrieval, Sentiment Analysis, Sentiment Classification

Recently by the development of the Internet and the Web, different types of
social media such as web blogs become an immense source of text data. Through
the processing of these data, it is possible to discover practical information
about different topics, individuals opinions and a thorough understanding of
the society. Therefore, applying models which can automatically extract the
subjective information from the documents would be efficient and helpful. Topic
modeling methods, also sentiment analysis are the most raised topics in the
natural language processing and text mining fields. In this paper a new
structure for joint sentiment-topic modeling based on Restricted Boltzmann
Machine (RBM) which is a type of neural networks is proposed. By modifying the
structure of RBM as well as appending a layer which is analogous to sentiment
of text data to it, we propose a generative structure for joint sentiment topic
modeling based on neutral networks. The proposed method is supervised and
trained by the Contrastive Divergence algorithm. The new attached layer in the
proposed model is a layer with the multinomial probability distribution which
can be used in text data sentiment classification or any other supervised
application. The proposed model is compared with existing models in the
experiments such as evaluating as a generative model, sentiment classification,
information retrieval and the corresponding results demonstrate the efficiency
of the method.
Leveraging Distributional Semantics for Multi-Label Learning
Authors: Rahul Wadbude, Vivek Gupta, Piyush Rai, Nagarajan Natarajan, Harish Karnick, Prateek Jain
Date: 2017-09-18 00:00:00
Tasks: Document Embedding, Missing Labels, Multi-Label Learning, Word Embeddings

We present a novel and scalable label embedding framework for large-scale
multi-label learning a.k.a ExMLDS (Extreme Multi-Label Learning using
Distributional Semantics). Our approach draws inspiration from ideas rooted in
distributional semantics, specifically the Skip Gram Negative Sampling (SGNS)
approach, widely used to learn word embeddings for natural language processing
tasks. Learning such embeddings can be reduced to a certain matrix
factorization. Our approach is novel in that it highlights interesting
connections between label embedding methods used for multi-label learning and
paragraph/document embedding methods commonly used for learning representations
of text data. The framework can also be easily extended to incorporate
auxiliary information such as label-label correlations; this is crucial
especially when there are a lot of missing labels in the training data. We
demonstrate the effectiveness of our approach through an extensive set of
experiments on a variety of benchmark datasets, and show that the proposed
learning methods perform favorably compared to several baselines and
state-of-the-art methods for large-scale multi-label learning. To facilitate
end-to-end learning, we develop a joint learning algorithm that can learn the
embeddings as well as a regression model that predicts these embeddings given
input features, via efficient gradient-based methods.
Unfolding Hidden Barriers by Active Enhanced Sampling
Authors: Jing Zhang, Ming Chen
Date: 2017-05-21 00:00:00
Tasks: Active Learning

Collective variable (CV) or order parameter based enhanced sampling
algorithms have achieved great success due to their ability to efficiently
explore the rough potential energy landscapes of complex systems. However, the
degeneracy of microscopic configurations, originating from the orthogonal space
perpendicular to the CVs, is likely to shadow "hidden barriers" and greatly
reduce the efficiency of CV-based sampling. Here we demonstrate that systematic
machine learning CV, through enhanced sampling, can iteratively lift such
degeneracies on the fly. We introduce an active learning scheme that consists
of a parametric CV learner based on deep neural network and a CV-based enhanced
sampler. Our active enhanced sampling (AES) algorithm is capable of identifying
the least informative regions based on a historical sample, forming a positive
feedback loop between the CV learner and sampler. This approach is able to
globally preserve kinetic characteristics by incrementally enhancing both
sample completeness and CV quality.
Learning to Inpaint for Image Compression
Authors: Mohammad Haris Baig, Vladlen Koltun, Lorenzo Torresani
Date: 2017-09-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Image Compression

We study the design of deep architectures for lossy image compression. We
present two architectural recipes in the context of multi-stage progressive
encoders and empirically demonstrate their importance on compression
performance. Specifically, we show that: (a) predicting the original image data
from residuals in a multi-stage progressive architecture facilitates learning
and leads to improved performance at approximating the original content and (b)
learning to inpaint (from neighboring image pixels) before performing
compression reduces the amount of information that must be stored to achieve a
high-quality approximation. Incorporating these design choices in a baseline
progressive encoder yields an average reduction of over $60\%$ in file size
with similar quality compared to the original residual encoder.
Quantized Memory-Augmented Neural Networks
Authors: Seongsik Park, Seijoon Kim, Seil Lee, Ho Bae, Sungroh Yoon
Date: 2017-11-10 00:00:00
Tasks: Quantization

Memory-augmented neural networks (MANNs) refer to a class of neural network
models equipped with external memory (such as neural Turing machines and memory
networks). These neural networks outperform conventional recurrent neural
networks (RNNs) in terms of learning long-term dependency, allowing them to
solve intriguing AI tasks that would otherwise be hard to address. This paper
concerns the problem of quantizing MANNs. Quantization is known to be effective
when we deploy deep models on embedded systems with limited resources.
Furthermore, quantization can substantially reduce the energy consumption of
the inference procedure. These benefits justify recent developments of
quantized multi layer perceptrons, convolutional networks, and RNNs. However,
no prior work has reported the successful quantization of MANNs. The in-depth
analysis presented here reveals various challenges that do not appear in the
quantization of the other networks. Without addressing them properly, quantized
MANNs would normally suffer from excessive quantization error which leads to
degraded performance. In this paper, we identify memory addressing
(specifically, content-based addressing) as the main reason for the performance
degradation and propose a robust quantization method for MANNs to address the
challenge. In our experiments, we achieved a computation-energy gain of 22x
with 8-bit fixed-point and binary quantization compared to the floating-point
implementation. Measured on the bAbI dataset, the resulting model, named the
quantized MANN (Q-MANN), improved the error rate by 46% and 30% with 8-bit
fixed-point and binary quantization, respectively, compared to the MANN
quantized using conventional techniques.
ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games
Authors: Yuandong Tian, Qucheng Gong, Wenling Shang, Yuxin Wu, C. Lawrence Zitnick
Date: 2017-07-04 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Atari Games, GPU, Real-Time Strategy Games, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Starcraft

In this paper, we propose ELF, an Extensive, Lightweight and Flexible
platform for fundamental reinforcement learning research. Using ELF, we
implement a highly customizable real-time strategy (RTS) engine with three game
environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a
miniature version of StarCraft, captures key game dynamics and runs at 40K
frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with
modern reinforcement learning methods, the system can train a full-game bot
against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition,
our platform is flexible in terms of environment-agent communication
topologies, choices of RL methods, changes in game parameters, and can host
existing C/C++-based game environments like Arcade Learning Environment. Using
ELF, we thoroughly explore training parameters and show that a network with
Leaky ReLU and Batch Normalization coupled with long-horizon training and
progressive curriculum beats the rule-based built-in AI more than $70\%$ of the
time in the full game of Mini-RTS. Strong performance is also achieved on the
other two games. In game replays, we show our agents learn interesting
strategies. ELF, along with its RL platform, is open-sourced at
https://github.com/facebookresearch/ELF.
AppTechMiner: Mining Applications and Techniques from Scientific Articles
Authors: Mayank Singh, Soham Dan, Sanyam Agarwal, Pawan Goyal, Animesh Mukherjee
Date: 2017-09-10 00:00:00
Tasks: Articles, Information Retrieval, Retrieval

This paper presents AppTechMiner, a rule-based information extraction
framework that automatically constructs a knowledge base of all application
areas and problem solving techniques. Techniques include tools, methods,
datasets or evaluation metrics. We also categorize individual research articles
based on their application areas and the techniques proposed/improved in the
article. Our system achieves high average precision (~82%) and recall (~84%) in
knowledge base creation. It also performs well in application and technique
assignment to an individual article (average accuracy ~66%). In the end, we
further present two use cases presenting a trivial information retrieval system
and an extensive temporal analysis of the usage of techniques and application
areas. At present, we demonstrate the framework for the domain of computational
linguistics but this can be easily generalized to any other field of research.
Online Deep Learning: Learning Deep Neural Networks on the Fly
Authors: Doyen Sahoo, Quang Pham, Jing Lu, Steven C. H. Hoi
Date: 2017-11-10 00:00:00
Tasks: Deep Learning

Deep Neural Networks (DNNs) are typically trained by backpropagation in a
batch learning setting, which requires the entire training data to be made
available prior to the learning task. This is not scalable for many real-world
scenarios where new data arrives sequentially in a stream form. We aim to
address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on
the fly in an online setting. Unlike traditional online learning that often
optimizes some convex objective function with respect to a shallow model (e.g.,
a linear/kernel-based hypothesis), ODL is significantly more challenging since
the optimization of the DNN objective function is non-convex, and regular
backpropagation does not work well in practice, especially for online learning
settings. In this paper, we present a new online deep learning framework that
attempts to tackle the challenges by learning DNN models of adaptive depth from
a sequence of training data in an online learning setting. In particular, we
propose a novel Hedge Backpropagation (HBP) method for online updating the
parameters of DNN effectively, and validate the efficacy of our method on
large-scale data sets, including both stationary and concept drifting
scenarios.
Integrating User and Agent Models: A Deep Task-Oriented Dialogue System
Authors: Weiyan Wang, Yuxiang Wu, Yu Zhang, Zhongqi Lu, Kaixiang Mo, Qiang Yang
Date: 2017-11-10 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Task-Oriented Dialogue Systems

Task-oriented dialogue systems can efficiently serve a large number of
customers and relieve people from tedious works. However, existing
task-oriented dialogue systems depend on handcrafted actions and states or
extra semantic labels, which sometimes degrades user experience despite the
intensive human intervention. Moreover, current user simulators have limited
expressive ability so that deep reinforcement Seq2Seq models have to rely on
selfplay and only work in some special cases. To address those problems, we
propose a uSer and Agent Model IntegrAtion (SAMIA) framework inspired by an
observation that the roles of the user and agent models are asymmetric.
Firstly, this SAMIA framework model the user model as a Seq2Seq learning
problem instead of ranking or designing rules. Then the built user model is
used as a leverage to train the agent model by deep reinforcement learning. In
the test phase, the output of the agent model is filtered by the user model to
enhance the stability and robustness. Experiments on a real-world coffee
ordering dataset verify the effectiveness of the proposed SAMIA framework.
Phrase Pair Mappings for Hindi-English Statistical Machine Translation
Authors: Sreelekha. S, Pushpak Bhattacharyya
Date: 2017-10-05 00:00:00
Tasks: Machine Translation, Translation

In this paper, we present our work on the creation of lexical resources for
the Machine Translation between English and Hindi. We describes the development
of phrase pair mappings for our experiments and the comparative performance
evaluation between different trained models on top of the baseline Statistical
Machine Translation system. We focused on augmenting the parallel corpus with
more vocabulary as well as with various inflected forms by exploring different
ways. We have augmented the training corpus with various lexical resources such
as lexical words, synset words, function words and verb phrases. We have
described the case studies, automatic and subjective evaluations, detailed
error analysis for both the English to Hindi and Hindi to English machine
translation systems. We further analyzed that, there is an incremental growth
in the quality of machine translation with the usage of various lexical
resources. Thus lexical resources do help uplift the translation quality of
resource poor langugaes.
Bilingual Words and Phrase Mappings for Marathi and Hindi SMT
Authors: Sreelekha. S, Pushpak Bhattacharyya
Date: 2017-10-05 00:00:00
Tasks: Machine Translation, Translation

Lack of proper linguistic resources is the major challenges faced by the
Machine Translation system developments when dealing with the resource poor
languages. In this paper, we describe effective ways to utilize the lexical
resources to improve the quality of statistical machine translation. Our
research on the usage of lexical resources mainly focused on two ways, such as;
augmenting the parallel corpus with more vocabulary and to provide various word
forms. We have augmented the training corpus with various lexical resources
such as lexical words, function words, kridanta pairs and verb phrases. We have
described the case studies, evaluations and detailed error analysis for both
Marathi to Hindi and Hindi to Marathi machine translation systems. From the
evaluations we observed that, there is an incremental growth in the quality of
machine translation as the usage of various lexical resources increases.
Moreover, usage of various lexical resources helps to improve the coverage and
quality of machine translation where limited parallel corpus is available.
Morphology Generation for Statistical Machine Translation
Authors: Sreelekha. S, Pushpak Bhattacharyya
Date: 2017-10-05 00:00:00
Tasks: Machine Translation, MORPH, Translation

When translating into morphologically rich languages, Statistical MT
approaches face the problem of data sparsity. The severity of the sparseness
problem will be high when the corpus size of morphologically richer language is
less. Even though we can use factored models to correctly generate
morphological forms of words, the problem of data sparseness limits their
performance. In this paper, we describe a simple and effective solution which
is based on enriching the input corpora with various morphological forms of
words. We use this method with the phrase-based and factor-based experiments on
two morphologically rich languages: Hindi and Marathi when translating from
English. We evaluate the performance of our experiments both in terms automatic
evaluation and subjective evaluation such as adequacy and fluency. We observe
that the morphology injection method helps in improving the quality of
translation. We further analyze that the morph injection method helps in
handling the data sparseness problem to a great level.
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection
Authors: Taku Kato, Takahiro Shinozaki
Date: 2017-11-10 00:00:00
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), speech-recognition, Speech Recognition

Speech recognition systems have achieved high recognition performance for
several tasks. However, the performance of such systems is dependent on the
tremendously costly development work of preparing vast amounts of task-matched
transcribed speech data for supervised training. The key problem here is the
cost of transcribing speech data. The cost is repeatedly required to support
new languages and new tasks. Assuming broad network services for transcribing
speech data for many users, a system would become more self-sufficient and more
useful if it possessed the ability to learn from very light feedback from the
users without annoying them. In this paper, we propose a general reinforcement
learning framework for speech recognition systems based on the policy gradient
method. As a particular instance of the framework, we also propose a hypothesis
selection-based reinforcement learning method. The proposed framework provides
a new view for several existing training and adaptation methods. The
experimental results show that the proposed method improves the recognition
performance compared to unsupervised adaptation.
Learning Vector Autoregressive Models with Latent Processes
Authors: Saber Salehkaleybar, Jalal Etesami, Negar Kiyavash, Kun Zhang
Date: 2017-02-27 00:00:00

We study the problem of learning the support of transition matrix between
random processes in a Vector Autoregressive (VAR) model from samples when a
subset of the processes are latent. It is well known that ignoring the effect
of the latent processes may lead to very different estimates of the influences
among observed processes, and we are concerned with identifying the influences
among the observed processes, those between the latent ones, and those from the
latent to the observed ones. We show that the support of transition matrix
among the observed processes and lengths of all latent paths between any two
observed processes can be identified successfully under some conditions on the
VAR model. From the lengths of latent paths, we reconstruct the latent subgraph
(representing the influences among the latent processes) with a minimum number
of variables uniquely if its topology is a directed tree. Furthermore, we
propose an algorithm that finds all possible minimal latent graphs under some
conditions on the lengths of latent paths. Our results apply to both
non-Gaussian and Gaussian cases, and experimental results on various synthetic
and real-world datasets validate our theoretical results.
Matrix-normal models for fMRI analysis
Authors: Michael Shvartsman, Narayanan Sundaram, Mikio C. Aoi, Adam Charles, Theodore C. Wilke, Jonathan D. Cohen
Date: 2017-11-08 00:00:00
Tasks: Functional Connectivity

Multivariate analysis of fMRI data has benefited substantially from advances
in machine learning. Most recently, a range of probabilistic latent variable
models applied to fMRI data have been successful in a variety of tasks,
including identifying similarity patterns in neural data (Representational
Similarity Analysis and its empirical Bayes variant, RSA and BRSA; Intersubject
Functional Connectivity, ISFC), combining multi-subject datasets (Shared
Response Mapping; SRM), and mapping between brain and behavior (Joint
Modeling). Although these methods share some underpinnings, they have been
developed as distinct methods, with distinct algorithms and software tools. We
show how the matrix-variate normal (MN) formalism can unify some of these
methods into a single framework. In doing so, we gain the ability to reuse
noise modeling assumptions, algorithms, and code across models. Our primary
theoretical contribution shows how some of these methods can be written as
instantiations of the same model, allowing us to generalize them to flexibly
modeling structured noise covariances. Our formalism permits novel model
variants and improved estimation strategies: in contrast to SRM, the number of
parameters for MN-SRM does not scale with the number of voxels or subjects; in
contrast to BRSA, the number of parameters for MN-RSA scales additively rather
than multiplicatively in the number of voxels. We empirically demonstrate
advantages of two new methods derived in the formalism: for MN-RSA, we show up
to 10x improvement in runtime, up to 6x improvement in RMSE, and more
conservative behavior under the null. For MN-SRM, our method grants a modest
improvement to out-of-sample reconstruction while relaxing an orthonormality
constraint of SRM. We also provide a software prototyping tool for MN models
that can flexibly reuse noise covariance assumptions and algorithms across
models.