pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Aspect Term Extraction with B-LSTM & CRF using Automatically Labelled Datasets
Authors: Athanasios Giannakopoulos, Claudiu Musat, Andreea Hossmann, Michael Baeriswyl
Date: 2017-09-15 00:00:00
Proceeding: WS 2017 9
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Term Extraction

Aspect Term Extraction (ATE) identifies opinionated aspect terms in texts and
is one of the tasks in the SemEval Aspect Based Sentiment Analysis (ABSA)
contest. The small amount of available datasets for supervised ATE and the
costly human annotation for aspect term labelling give rise to the need for
unsupervised ATE. In this paper, we introduce an architecture that achieves
top-ranking performance for supervised ATE. Moreover, it can be used
efficiently as feature extractor and classifier for unsupervised ATE. Our
second contribution is a method to automatically construct datasets for ATE. We
train a classifier on our automatically labelled datasets and evaluate it on
the human annotated SemEval ABSA test sets. Compared to a strong rule-based
baseline, we obtain a dramatically higher F-score and attain precision values
above 80%. Our unsupervised method beats the supervised ABSA baseline from
SemEval, while preserving high precision scores.
Robust Kernelized Multi-View Self-Representations for Clustering by Tensor Multi-Rank Minimization
Authors: Yanyun Qu, Jinyan Liu, Yuan Xie, Wensheng Zhang
Date: 2017-09-15 00:00:00
Tasks: Clustering, Face Clustering

Most recently, tensor-SVD is implemented on multi-view self-representation
clustering and has achieved the promising results in many real-world
applications such as face clustering, scene clustering and generic object
clustering. However, tensor-SVD based multi-view self-representation clustering
is proposed originally to solve the clustering problem in the multiple linear
subspaces, leading to unsatisfactory results when dealing with the case of
non-linear subspaces. To handle data clustering from the non-linear subspaces,
a kernelization method is designed by mapping the data from the original input
space to a new feature space in which the transformed data can be clustered by
a multiple linear clustering method. In this paper, we make an optimization
model for the kernelized multi-view self-representation clustering problem. We
also develop a new efficient algorithm based on the alternation direction
method and infer a closed-form solution. Since all the subproblems can be
solved exactly, the proposed optimization algorithm is guaranteed to obtain the
optimal solution. In particular, the original tensor-based multi-view
self-representation clustering problem is a special case of our approach and
can be solved by our algorithm. Experimental results on several popular
real-world clustering datasets demonstrate that our approach achieves the
state-of-the-art performance.
Convolutional Networks for Spherical Signals
Authors: Taco Cohen, Mario Geiger, Jonas Köhler, Max Welling
Date: 2017-09-14 00:00:00
Tasks: General Classification, Translation

The success of convolutional networks in learning problems involving planar
signals such as images is due to their ability to exploit the translation
symmetry of the data distribution through weight sharing. Many areas of science
and egineering deal with signals with other symmetries, such as rotation
invariant data on the sphere. Examples include climate and weather science,
astrophysics, and chemistry. In this paper we present spherical convolutional
networks. These networks use convolutions on the sphere and rotation group,
which results in rotational weight sharing and rotation equivariance. Using a
synthetic spherical MNIST dataset, we show that spherical convolutional
networks are very effective at dealing with rotationally invariant
classification problems.
A Deep Generative Framework for Paraphrase Generation
Authors: Ankush Gupta, Arvind Agarwal, Prawaan Singh, Piyush Rai
Date: 2017-09-15 00:00:00
Tasks: Decoder, Information Retrieval, Paraphrase Generation, Question Answering, Retrieval, Sentence

Paraphrase generation is an important problem in NLP, especially in question
answering, information retrieval, information extraction, conversation systems,
to name a few. In this paper, we address the problem of generating paraphrases
automatically. Our proposed method is based on a combination of deep generative
models (VAE) with sequence-to-sequence models (LSTM) to generate paraphrases,
given an input sentence. Traditional VAEs when combined with recurrent neural
networks can generate free text but they are not suitable for paraphrase
generation for a given sentence. We address this problem by conditioning the
both, encoder and decoder sides of VAE, on the original sentence, so that it
can generate the given sentence's paraphrases. Unlike most existing models, our
model is simple, modular and can generate multiple paraphrases, for a given
sentence. Quantitative evaluation of the proposed method on a benchmark
paraphrase dataset demonstrates its efficacy, and its performance improvement
over the state-of-the-art methods by a significant margin, whereas qualitative
human evaluation indicate that the generated paraphrases are well-formed,
grammatically correct, and are relevant to the input sentence. Furthermore, we
evaluate our method on a newly released question paraphrase dataset, and
establish a new baseline for future research.
Joint Hierarchical Category Structure Learning and Large-Scale Image Classification
Authors: Yanyun Qu, Li Lin, Fumin Shen, Chang Lu, Yang Wu, Yuan Xie, DaCheng Tao
Date: 2017-09-15 00:00:00
Tasks: Classification, Clustering, General Classification, image-classification, Image Classification, Multi-class Classification

We investigate the scalable image classification problem with a large number
of categories. Hierarchical visual data structures are helpful for improving
the efficiency and performance of large-scale multi-class classification. We
propose a novel image classification method based on learning hierarchical
inter-class structures. Specifically, we first design a fast algorithm to
compute the similarity metric between categories, based on which a visual tree
is constructed by hierarchical spectral clustering. Using the learned visual
tree, a test sample label is efficiently predicted by searching for the best
path over the entire tree. The proposed method is extensively evaluated on the
ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that
our method obtains significantly better category hierarchies than other
state-of-the-art visual tree-based methods and, therefore, much more accurate
classification.
Shapechanger: Environments for Transfer Learning
Authors: Sébastien M. R. Arnold, Tsam Kiu Pun, Théo-Tim J. Denisart, Francisco J. Valero-Cuevas
Date: 2017-09-15 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning, Transfer Reinforcement Learning

We present Shapechanger, a library for transfer reinforcement learning
specifically designed for robotic tasks. We consider three types of knowledge
transfer---from simulation to simulation, from simulation to real, and from
real to real---and a wide range of tasks with continuous states and actions.
Shapechanger is under active development and open-sourced at:
https://github.com/seba-1511/shapechanger/.
Accelerating SGD for Distributed Deep-Learning Using Approximated Hessian Matrix
Authors: Sébastien M. R. Arnold, Chunming Wang
Date: 2017-09-15 00:00:00
Tasks: Deep Learning, Second-order methods, Stochastic Optimization

We introduce a novel method to compute a rank $m$ approximation of the
inverse of the Hessian matrix in the distributed regime. By leveraging the
differences in gradients and parameters of multiple Workers, we are able to
efficiently implement a distributed approximation of the Newton-Raphson method.
We also present preliminary results which underline advantages and challenges
of second-order methods for large stochastic optimization problems. In
particular, our work suggests that novel strategies for combining gradients
provide further information on the loss surface.
Deep Reinforcement Learning for Conversational AI
Authors: Mahipal Jadeja, Neelanshi Varia, Agam Shah
Date: 2017-09-15 00:00:00
Tasks: Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning is revolutionizing the artificial intelligence
field. Currently, it serves as a good starting point for constructing
intelligent autonomous systems which offer a better knowledge of the visual
world. It is possible to scale deep reinforcement learning with the use of deep
learning and do amazing tasks such as use of pixels in playing video games. In
this paper, key concepts of deep reinforcement learning including reward
function, differences between reinforcement learning and supervised learning
and models for implementation of reinforcement are discussed. Key challenges
related to the implementation of reinforcement learning in conversational AI
domain are identified as well as discussed in detail. Various conversational
models which are based on deep reinforcement learning (as well as deep
learning) are also discussed. In summary, this paper discusses key aspects of
deep reinforcement learning which are crucial for designing an efficient
conversational AI.
Asian Stamps Identification and Classification System
Authors: Behzad Mahaseni, Nabhan D. Salih
Date: 2017-09-15 00:00:00
Tasks: Classification, General Classification

In this paper, we address the problem of stamp recognition. The goal is to
classify a given stamp to a certain country and also identify the year it is
published. We propose a new approach for stamp recognition based on describing
a given stamp image using color information and texture information. For color
information we use color histogram for the entire image and for texture we use
two features. SIFT which is based on local feature descriptors and HOG which is
a dens texture descriptor. As a result on total we have three different types
of features. Our initial evaluation shows that give these information we are
able to classify the images with a reasonable accuracy.
Scene Graph Generation from Objects, Phrases and Region Captions
Authors: Yikang Li, Wanli Ouyang, Bolei Zhou, Kun Wang, Xiaogang Wang
Date: 2017-07-31 00:00:00
Proceeding: ICCV 2017 10
Tasks: Graph Generation, object-detection, Object Detection, Scene Graph Generation, Scene Understanding

Object detection, scene graph generation and region captioning, which are
three scene understanding tasks at different semantic levels, are tied
together: scene graphs are generated on top of objects detected in an image
with their pairwise relationship predicted, while region captioning gives a
language description of the objects, their attributes, relations, and other
context information. In this work, to leverage the mutual connections across
semantic levels, we propose a novel neural network model, termed as Multi-level
Scene Description Network (denoted as MSDN), to solve the three vision tasks
jointly in an end-to-end manner. Objects, phrases, and caption regions are
first aligned with a dynamic graph based on their spatial and semantic
connections. Then a feature refining structure is used to pass messages across
the three levels of semantic tasks through the graph. We benchmark the learned
model on three tasks, and show the joint learning across three tasks with our
proposed method can bring mutual improvements over previous models.
Particularly, on the scene graph generation task, our proposed method
outperforms the state-of-art method with more than 3% margin.
Learning Compact Geometric Features
Authors: Marc Khoury, Qian-Yi Zhou, Vladlen Koltun
Date: 2017-09-15 00:00:00
Proceeding: ICCV 2017 10
Tasks: Point Cloud Registration

We present an approach to learning features that represent the local geometry
around a point in an unstructured point cloud. Such features play a central
role in geometric registration, which supports diverse applications in robotics
and 3D vision. Current state-of-the-art local features for unstructured point
clouds have been manually crafted and none combines the desirable properties of
precision, compactness, and robustness. We show that features with these
properties can be learned from data, by optimizing deep networks that map
high-dimensional histograms into low-dimensional Euclidean spaces. The
presented approach yields a family of features, parameterized by dimension,
that are both more compact and more accurate than existing descriptors.
Disentangled Variational Auto-Encoder for Semi-supervised Learning
Authors: Yang Li, Quan Pan, Suhang Wang, Haiyun Peng, Tao Yang, Erik Cambria
Date: 2017-09-15 00:00:00
Tasks: Decoder, Reinforcement Learning

Semi-supervised learning is attracting increasing attention due to the fact
that datasets of many domains lack enough labeled data. Variational
Auto-Encoder (VAE), in particular, has demonstrated the benefits of
semi-supervised learning. The majority of existing semi-supervised VAEs utilize
a classifier to exploit label information, where the parameters of the
classifier are introduced to the VAE. Given the limited labeled data, learning
the parameters for the classifiers may not be an optimal solution for
exploiting label information. Therefore, in this paper, we develop a novel
approach for semi-supervised VAE without classifier. Specifically, we propose a
new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the
input data into disentangled representation and non-interpretable
representation, then the category information is directly utilized to
regularize the disentangled representation via the equality constraint. To
further enhance the feature learning ability of the proposed VAE, we
incorporate reinforcement learning to relieve the lack of data. The dynamic
framework is capable of dealing with both image and text data with its
corresponding encoder and decoder networks. Extensive experiments on image and
text datasets demonstrate the effectiveness of the proposed framework.
Self-Guiding Multimodal LSTM - when we do not have a perfect training dataset for image captioning
Authors: Yang Xian, YingLi Tian
Date: 2017-09-15 00:00:00
Tasks: Image Captioning, Sentence

In this paper, a self-guiding multimodal LSTM (sg-LSTM) image captioning
model is proposed to handle uncontrolled imbalanced real-world image-sentence
dataset. We collect FlickrNYC dataset from Flickr as our testbed with 306,165
images and the original text descriptions uploaded by the users are utilized as
the ground truth for training. Descriptions in FlickrNYC dataset vary
dramatically ranging from short term-descriptions to long
paragraph-descriptions and can describe any visual aspects, or even refer to
objects that are not depicted. To deal with the imbalanced and noisy situation
and to fully explore the dataset itself, we propose a novel guiding textual
feature extracted utilizing a multimodal LSTM (m-LSTM) model. Training of
m-LSTM is based on the portion of data in which the image content and the
corresponding descriptions are strongly bonded. Afterwards, during the training
of sg-LSTM on the rest training data, this guiding information serves as
additional input to the network along with the image representations and the
ground-truth descriptions. By integrating these input components into a
multimodal block, we aim to form a training scheme with the textual information
tightly coupled with the image content. The experimental results demonstrate
that the proposed sg-LSTM model outperforms the traditional state-of-the-art
multimodal RNN captioning framework in successfully describing the key
components of the input images.
Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector
Authors: Huidong Dai, Weiji He, Guohua Gu, Ling Ye, Tianyi Mao, Qian Chen
Date: 2017-09-15 00:00:00

Photon counting 3D imaging allows to obtain 3D images with single-photon
sensitivity and sub-ns temporal resolution. However, it is challenging to scale
to high spatial resolution. In this work, we demonstrate a photon counting 3D
imaging technique with short-pulsed structured illumination and a single-pixel
photon counting detector. The proposed multi-resolution photon counting 3D
imaging technique acquires a high-resolution 3D image from a coarse image and
edges at successfully finer resolution sampled by Hadamard multiplexing along
the wavelet trees. The detected power is significantly increased thanks to the
Hadamard multiplexing. Both the required measurements and the reconstruction
time can be significantly reduced by performing wavelet-tree-based regions of
edges predication and Hadamard demultiplexing, which makes the proposed
technique suitable for scenes with high spatial resolution. The experimental
results indicate that a 3D image at resolution up to 512*512 pixels can be
acquired and retrieved with practical time as low as 17 seconds.
Learning task structure via sparsity grouped multitask learning
Authors: Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano
Date: 2017-05-13 00:00:00
Tasks: feature selection, Sparse Learning

Sparse mapping has been a key methodology in many high-dimensional scientific
problems. When multiple tasks share the set of relevant features, learning them
jointly in a group drastically improves the quality of relevant feature
selection. However, in practice this technique is used limitedly since such
grouping information is usually hidden. In this paper, our goal is to recover
the group structure on the sparsity patterns and leverage that information in
the sparse learning. Toward this, we formulate a joint optimization problem in
the task parameter and the group membership, by constructing an appropriate
regularizer to encourage sparse learning as well as correct recovery of task
groups. We further demonstrate that our proposed method recovers groups and the
sparsity patterns in the task parameters accurately by extensive experiments.
Dynamic Capacity Estimation in Hopfield Networks
Authors: Saarthak Sarup, Mingoo Seok
Date: 2017-09-15 00:00:00
Tasks: Capacity Estimation

Understanding the memory capacity of neural networks remains a challenging
problem in implementing artificial intelligence systems. In this paper, we
address the notion of capacity with respect to Hopfield networks and propose a
dynamic approach to monitoring a network's capacity. We define our
understanding of capacity as the maximum number of stored patterns which can be
retrieved when probed by the stored patterns. Prior work in this area has
presented static expressions dependent on neuron count $N$, forcing network
designers to assume worst-case input characteristics for bias and correlation
when setting the capacity of the network. Instead, our model operates
simultaneously with the learning Hopfield network and concludes on a capacity
estimate based on the patterns which were stored. By continuously updating the
crosstalk associated with the stored patterns, our model guards the network
from overwriting its memory traces and exceeding its capacity. We simulate our
model using artificially generated random patterns, which can be set to a
desired bias and correlation, and observe capacity estimates between 93% and
97% accurate. As a result, our model doubles the memory efficiency of Hopfield
networks in comparison to the static and worst-case capacity estimate while
minimizing the risk of lost patterns.
ClickBAIT: Click-based Accelerated Incremental Training of Convolutional Neural Networks
Authors: Ervin Teng, João Diogo Falcão, Bob Iannucci
Date: 2017-09-15 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection, Object Tracking, Optical Flow Estimation, TAG

Today's general-purpose deep convolutional neural networks (CNN) for image
classification and object detection are trained offline on large static
datasets. Some applications, however, will require training in real-time on
live video streams with a human-in-the-loop. We refer to this class of problem
as Time-ordered Online Training (ToOT) - these problems will require a
consideration of not only the quantity of incoming training data, but the human
effort required to tag and use it. In this paper, we define training benefit as
a metric to measure the effectiveness of a sequence in using each user
interaction. We demonstrate and evaluate a system tailored to performing ToOT
in the field, capable of training an image classifier on a live video stream
through minimal input from a human operator. We show that by exploiting the
time-ordered nature of the video stream through optical flow-based object
tracking, we can increase the effectiveness of human actions by about 8 times.
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
Authors: Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath
Date: 2017-09-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Face Generation

We propose an adversarial training procedure for learning a causal implicit
generative model for a given causal graph. We show that adversarial training
can be used to learn a generative model with true observational and
interventional distributions if the generator architecture is consistent with
the given causal graph. We consider the application of generating faces based
on given binary labels where the dependency structure between the labels is
preserved with a causal graph. This problem can be seen as learning a causal
implicit generative model for the image and labels. We devise a two-stage
procedure for this problem. First we train a causal implicit generative model
over binary labels using a neural network consistent with a causal graph as the
generator. We empirically show that WassersteinGAN can be used to output
discrete labels. Later, we propose two new conditional GAN architectures, which
we call CausalGAN and CausalBEGAN. We show that the optimal generator of the
CausalGAN, given the labels, samples from the image distributions conditioned
on these labels. The conditional GAN combined with a trained causal implicit
generative model for the labels is then a causal implicit generative model over
the labels and the generated image. We show that the proposed architectures can
be used to sample from observational and interventional image distributions,
even for interventions which do not naturally occur in the dataset.