pwc-archive/papers-with-abstracts huggingface dataset:
Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study
Authors: Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre
Date: 2018-06-18 00:00:00
Tasks: Attribute, General Classification, image-classification, Image Classification, Remote Sensing Image Classification

The motivation of this paper is to conduct a comparative study on remote
sensing image classification using the morphological attribute profiles (APs)
and feature profiles (FPs) generated from different types of tree structures.
Over the past few years, APs have been among the most effective methods to
model the image's spatial and contextual information. Recently, a novel
extension of APs called FPs has been proposed by replacing pixel gray-levels
with some statistical and geometrical features when forming the output
profiles. FPs have been proved to be more efficient than the standard APs when
generated from component trees (max-tree and min-tree). In this work, we
investigate their performance on the inclusion tree (tree of shapes) and
partition trees (alpha tree and omega tree). Experimental results from both
panchromatic and hyperspectral images again confirm the efficiency of FPs
compared to APs.
Repetition Estimation
Authors: Tom F. H. Runia, Cees G. M. Snoek, Arnold W. M. Smeulders
Date: 2018-06-18 00:00:00

Visual repetition is ubiquitous in our world. It appears in human activity
(sports, cooking), animal behavior (a bee's waggle dance), natural phenomena
(leaves in the wind) and in urban environments (flashing lights). Estimating
visual repetition from realistic video is challenging as periodic motion is
rarely perfectly static and stationary. To better deal with realistic video, we
elevate the static and stationary assumptions often made by existing work. Our
spatiotemporal filtering approach, established on the theory of periodic
motion, effectively handles a wide variety of appearances and requires no
learning. Starting from motion in 3D we derive three periodic motion types by
decomposition of the motion field into its fundamental components. In addition,
three temporal motion continuities emerge from the field's temporal dynamics.
For the 2D perception of 3D motion we consider the viewpoint relative to the
motion; what follows are 18 cases of recurrent motion perception. To estimate
repetition under all circumstances, our theory implies constructing a mixture
of differential motion maps: gradient, divergence and curl. We temporally
convolve the motion maps with wavelet filters to estimate repetitive dynamics.
Our method is able to spatially segment repetitive motion directly from the
temporal filter responses densely computed over the motion maps. For
experimental verification of our claims, we use our novel dataset for
repetition estimation, better-reflecting reality with non-static and
non-stationary repetitive motion. On the task of repetition counting, we obtain
favorable results compared to a deep learning alternative.
GroupReduce: Block-Wise Low-Rank Approximation for Neural Language Model Shrinking
Authors: Patrick H. Chen, Si Si, Yang Li, Ciprian Chelba, Cho-Jui Hsieh
Date: 2018-06-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Language Modeling, Language Modelling, Model Compression, Quantization

Model compression is essential for serving large deep neural nets on devices
with limited resources or applications that require real-time responses. As a
case study, a state-of-the-art neural language model usually consists of one or
more recurrent layers sandwiched between an embedding layer used for
representing input tokens and a softmax layer for generating output tokens. For
problems with a very large vocabulary size, the embedding and the softmax
matrices can account for more than half of the model size. For instance, the
bigLSTM model achieves state-of- the-art performance on the One-Billion-Word
(OBW) dataset with around 800k vocabulary, and its word embedding and softmax
matrices use more than 6GBytes space, and are responsible for over 90% of the
model parameters. In this paper, we propose GroupReduce, a novel compression
method for neural language models, based on vocabulary-partition (block) based
low-rank matrix approximation and the inherent frequency distribution of tokens
(the power-law distribution of words). The experimental results show our method
can significantly outperform traditional compression methods such as low-rank
approximation and pruning. On the OBW dataset, our method achieved 6.6 times
compression rate for the embedding and softmax matrices, and when combined with
quantization, our method can achieve 26 times compression rate, which
translates to a factor of 12.8 times compression for the entire model with very
little degradation in perplexity.
Using Mode Connectivity for Loss Landscape Analysis
Authors: Akhilesh Gotmare, Nitish Shirish Keskar, Caiming Xiong, Richard Socher
Date: 2018-06-18 00:00:00

Mode connectivity is a recently introduced frame- work that empirically
establishes the connected- ness of minima by finding a high accuracy curve
between two independently trained models. To investigate the limits of this
setup, we examine the efficacy of this technique in extreme cases where the
input models are trained or initialized differently. We find that the procedure
is resilient to such changes. Given this finding, we propose using the
framework for analyzing loss surfaces and training trajectories more generally,
and in this direction, study SGD with cosine annealing and restarts (SGDR). We
report that while SGDR moves over barriers in its trajectory, propositions
claiming that it converges to and escapes from multiple local minima are not
substantiated by our empirical results.
Towards Gene Expression Convolutions using Gene Interaction Graphs
Authors: Francis Dutil, Joseph Paul Cohen, Martin Weiss, Georgy Derevyanko, Yoshua Bengio
Date: 2018-06-18 00:00:00

We study the challenges of applying deep learning to gene expression data. We
find experimentally that there exists non-linear signal in the data, however is
it not discovered automatically given the noise and low numbers of samples used
in most research. We discuss how gene interaction graphs (same pathway,
protein-protein, co-expression, or research paper text association) can be used
to impose a bias on a deep model similar to the spatial bias imposed by
convolutions on an image. We explore the usage of Graph Convolutional Neural
Networks coupled with dropout and gene embeddings to utilize the graph
information. We find this approach provides an advantage for particular tasks
in a low data regime but is very dependent on the quality of the graph used. We
conclude that more work should be done in this direction. We design experiments
that show why existing methods fail to capture signal that is present in the
data when features are added which clearly isolates the problem that needs to
be addressed.
Comparative Analysis of Neural QA models on SQuAD
Authors: Soumya Wadhwa, Khyathi Raghavi Chandu, Eric Nyberg
Date: 2018-06-18 00:00:00
Proceeding: WS 2018 7
Tasks: Information Retrieval, Question Answering, Reading Comprehension, Retrieval

The task of Question Answering has gained prominence in the past few decades
for testing the ability of machines to understand natural language. Large
datasets for Machine Reading have led to the development of neural models that
cater to deeper language understanding compared to information retrieval tasks.
Different components in these neural architectures are intended to tackle
different challenges. As a first step towards achieving generalization across
multiple domains, we attempt to understand and compare the peculiarities of
existing end-to-end neural models on the Stanford Question Answering Dataset
(SQuAD) by performing quantitative as well as qualitative analysis of the
results attained by each of them. We observed that prediction errors reflect
certain model-specific biases, which we further discuss in this paper.
Deconvolving convolution neural network for cell detection
Authors: Shan E Ahmed Raza, Khalid AbdulJabbar, Mariam Jamal-Hanjani, Selvaraju Veeriah, John Le Quesne, Charles Swanton, Yinyin Yuan
Date: 2018-06-18 00:00:00
Tasks: Cell Detection, Deep Learning

Automatic cell detection in histology images is a challenging task due to
varying size, shape and features of cells and stain variations across a large
cohort. Conventional deep learning methods regress the probability of each
pixel belonging to the centre of a cell followed by detection of local maxima.
We present deconvolution as an alternate approach to local maxima detection.
The ground truth points are convolved with a mapping filter to generate
artifical labels. A convolutional neural network (CNN) is modified to convolve
it's output with the same mapping filter and is trained for the mapped labels.
Output of the trained CNN is then deconvolved to generate points as cell
detection. We compare our method with state-of-the-art deep learning approaches
where the results show that the proposed approach detects cells with
comparatively high precision and F1-score.
A Comparison of Transformer and Recurrent Neural Networks on Multilingual Neural Machine Translation
Authors: Surafel M. Lakew, Mauro Cettolo, Marcello Federico
Date: 2018-06-18 00:00:00
Proceeding: COLING 2018 8
Tasks: Machine Translation, NMT, Transfer Learning, Translation

Recently, neural machine translation (NMT) has been extended to
multilinguality, that is to handle more than one translation direction with a
single system. Multilingual NMT showed competitive performance against pure
bilingual systems. Notably, in low-resource settings, it proved to work
effectively and efficiently, thanks to shared representation space that is
forced across languages and induces a sort of transfer-learning. Furthermore,
multilingual NMT enables so-called zero-shot inference across language pairs
never seen at training time. Despite the increasing interest in this framework,
an in-depth analysis of what a multilingual NMT model is capable of and what it
is not is still missing. Motivated by this, our work (i) provides a
quantitative and comparative analysis of the translations produced by
bilingual, multilingual and zero-shot systems; (ii) investigates the
translation quality of two of the currently dominant neural architectures in
MT, which are the Recurrent and the Transformer ones; and (iii) quantitatively
explores how the closeness between languages influences the zero-shot
translation. Our analysis leverages multiple professional post-edits of
automatic translations by several different systems and focuses both on
automatic standard metrics (BLEU and TER) and on widely used error categories,
which are lexical, morphology, and word order errors.
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Authors: Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
Date: 2018-06-18 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: All, Clustering, Topic Models

People belong to multiple communities, words belong to multiple topics, and
books cover multiple genres; overlapping clusters are commonplace. Many
existing overlapping clustering methods model each person (or word, or book) as
a non-negative weighted combination of "exemplars" who belong solely to one
community, with some small noise. Geometrically, each person is a point on a
cone whose corners are these exemplars. This basic form encompasses the widely
used Mixed Membership Stochastic Blockmodel of networks (Airoldi et al., 2008)
and its degree-corrected variants (Jin et al., 2017), as well as topic models
such as LDA (Blei et al., 2003). We show that a simple one-class SVM yields
provably consistent parameter inference for all such models, and scales to
large datasets. Experimental results on several simulated and real datasets
show our algorithm (called SVM-cone) is both accurate and scalable.
Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization
Authors: Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Date: 2018-06-18 00:00:00
Tasks: Future prediction, Prediction, Segmentation, Semantic Segmentation

For autonomous agents to successfully operate in the real world, anticipation
of future events and states of their environment is a key competence. This
problem can be formalized as a sequence prediction problem, where a number of
observations are used to predict the sequence into the future. However,
real-world scenarios demand a model of uncertainty of such predictions, as
future states become increasingly uncertain and multi-modal -- in particular on
long time horizons. This makes modelling and learning challenging. We cast
state of the art semantic segmentation and future prediction models based on
deep learning into a Bayesian formulation that in turn allows for a full
Bayesian treatment of the prediction problem. We present a new sampling scheme
for this model that draws from the success of variational autoencoders by
incorporating a recognition network. In the experiments we show that our model
outperforms prior work in accuracy of the predicted segmentation and provides
calibrated probabilities that also better capture the multi-modal aspects of
possible future states of street scenes.
Designing Optimal Binary Rating Systems
Authors: Nikhil Garg, Ramesh Johari
Date: 2018-06-18 00:00:00

Modern online platforms rely on effective rating systems to learn about
items. We consider the optimal design of rating systems that collect binary
feedback after transactions. We make three contributions. First, we formalize
the performance of a rating system as the speed with which it recovers the true
underlying ranking on items (in a large deviations sense), accounting for both
items' underlying match rates and the platform's preferences. Second, we
provide an efficient algorithm to compute the binary feedback system that
yields the highest such performance. Finally, we show how this theoretical
perspective can be used to empirically design an implementable, approximately
optimal rating system, and validate our approach using real-world experimental
data collected on Amazon Mechanical Turk.
Learning Object Localization and 6D Pose Estimation from Simulation and Weakly Labeled Real Images
Authors: Jean-Philippe Mercier, Chaitanya Mitash, Philippe Giguère, Abdeslam Boularias
Date: 2018-06-18 00:00:00
Tasks: 6D Pose Estimation, 6D Pose Estimation using RGB, Domain Adaptation, Object, Object Localization, Pose Estimation, Robotic Grasping

This work proposes a process for efficiently training a point-wise object
detector that enables localizing objects and computing their 6D poses in
cluttered and occluded scenes. Accurate pose estimation is typically a
requirement for robust robotic grasping and manipulation of objects placed in
cluttered, tight environments, such as a shelf with multiple objects. To
minimize the human labor required for annotation, the proposed object detector
is first trained in simulation by using automatically annotated synthetic
images. We then show that the performance of the detector can be substantially
improved by using a small set of weakly annotated real images, where a human
provides only a list of objects present in each image without indicating the
location of the objects. To close the gap between real and synthetic images, we
adopt a domain adaptation approach through adversarial training. The detector
resulting from this training process can be used to localize objects by using
its per-object activation maps. In this work, we use the activation maps to
guide the search of 6D poses of objects. Our proposed approach is evaluated on
several publicly available datasets for pose estimation. We also evaluated our
model on classification and localization in unsupervised and semi-supervised
settings. The results clearly indicate that this approach could provide an
efficient way toward fully automating the training process of computer vision
models used in robotics.
Learning to Decode 7T-like MR Image Reconstruction from 3T MR Images
Authors: Aditya Sharma, Prabhjot Kaur, Aditya Nigam, Arnav Bhavsar
Date: 2018-06-18 00:00:00
Tasks: Decoder, Image Reconstruction, Medical Diagnosis

Increasing demand for high field magnetic resonance (MR) scanner indicates
the need for high-quality MR images for accurate medical diagnosis. However,
cost constraints, instead, motivate a need for algorithms to enhance images
from low field scanners. We propose an approach to process the given low field
(3T) MR image slices to reconstruct the corresponding high field (7T-like)
slices. Our framework involves a novel architecture of a merged convolutional
autoencoder with a single encoder and multiple decoders. Specifically, we
employ three decoders with random initializations, and the proposed training
approach involves selection of a particular decoder in each weight-update
iteration for back propagation. We demonstrate that the proposed algorithm
outperforms some related contemporary methods in terms of performance and
reconstruction time.
The Minimax Learning Rates of Normal and Ising Undirected Graphical Models
Authors: Luc Devroye, Abbas Mehrabian, Tommy Reddad
Date: 2018-06-18 00:00:00

Let $G$ be an undirected graph with $m$ edges and $d$ vertices. We show that $d$-dimensional Ising models on $G$ can be learned from $n$ i.i.d. samples within expected total variation distance some constant factor of $\min\{1, \sqrt{(m + d)/n}\}$, and that this rate is optimal. We show that the same rate holds for the class of $d$-dimensional multivariate normal undirected graphical models with respect to $G$. We also identify the optimal rate of $\min\{1, \sqrt{m/n}\}$ for Ising models with no external magnetic field.
A Survey of Inverse Reinforcement Learning: Challenges, Methods and Progress
Authors: Saurabh Arora, Prashant Doshi
Date: 2018-06-18 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Inverse reinforcement learning (IRL) is the problem of inferring the reward function of an agent, given its policy or observed behavior. Analogous to RL, IRL is perceived both as a problem and as a class of methods. By categorically surveying the current literature in IRL, this article serves as a reference for researchers and practitioners of machine learning and beyond to understand the challenges of IRL and select the approaches best suited for the problem on hand. The survey formally introduces the IRL problem along with its central challenges such as the difficulty in performing accurate inference and its generalizability, its sensitivity to prior knowledge, and the disproportionate growth in solution complexity with problem size. The article elaborates how the current methods mitigate these challenges. We further discuss the extensions to traditional IRL methods for handling: inaccurate and incomplete perception, an incomplete model, multiple reward functions, and nonlinear reward functions. This survey concludes the discussion with some broad advances in the research area and currently open research questions.
Diving Deep onto Discriminative Ensemble of Histological Hashing & Class-Specific Manifold Learning for Multi-class Breast Carcinoma Taxonomy
Authors: Sawon Pratiher, Subhankar Chattoraj
Date: 2018-06-18 00:00:00
Tasks: Classification, Diagnostic, General Classification

Histopathological images (HI) encrypt resolution dependent heterogeneous
textures & diverse color distribution variability, manifesting in
micro-structural surface tissue convolutions. Also, inherently high coherency
of cancerous cells poses significant challenges to breast cancer (BC)
multi-classification. As such, multi-class stratification is sparsely explored
& prior work mainly focus on benign & malignant tissue characterization only,
which forestalls further quantitative analysis of subordinate classes like
adenosis, mucinous carcinoma & fibroadenoma etc, for diagnostic competence. In
this work, a fully-automated, near-real-time & computationally inexpensive
robust multi-classification deep framework from HI is presented.
  The proposed scheme employs deep neural network (DNN) aided discriminative
ensemble of holistic class-specific manifold learning (CSML) for underlying HI
sub-space embedding & HI hashing based local shallow signatures. The model
achieves 95.8% accuracy pertinent to multi-classification & 2.8% overall
performance improvement & 38.2% enhancement for Lobular carcinoma (LC)
sub-class recognition rate as compared to the existing state-of-the-art on well
known BreakHis dataset is achieved. Also, 99.3% recognition rate at 200X & a
sensitivity of 100% for binary grading at all magnification validates its
suitability for clinical deployment in hand-held smart devices.
Learning Distributed Representations from Reviews for Collaborative Filtering
Authors: Amjad Almahairi, Kyle Kastner, Kyunghyun Cho, Aaron Courville
Date: 2018-06-18 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Recent work has shown that collaborative filter-based recommender systems can
be improved by incorporating side information, such as natural language
reviews, as a way of regularizing the derived product representations.
Motivated by the success of this approach, we introduce two different models of
reviews and study their effect on collaborative filtering performance. While
the previous state-of-the-art approach is based on a latent Dirichlet
allocation (LDA) model of reviews, the models we explore are neural network
based: a bag-of-words product-of-experts model and a recurrent neural network.
We demonstrate that the increased flexibility offered by the product-of-experts
model allowed it to achieve state-of-the-art performance on the Amazon review
dataset, outperforming the LDA-based approach. However, interestingly, the
greater modeling power offered by the recurrent neural network appears to
undermine the model's ability to act as a regularizer of the product
representations.
Combining Word Feature Vector Method with the Convolutional Neural Network for Slot Filling in Spoken Language Understanding
Authors: Ruixi Lin
Date: 2018-06-18 00:00:00
Tasks: Sentence, slot-filling, Slot Filling, Spoken Language Understanding

Slot filling is an important problem in Spoken Language Understanding (SLU)
and Natural Language Processing (NLP), which involves identifying a user's
intent and assigning a semantic concept to each word in a sentence. This paper
presents a word feature vector method and combines it into the convolutional
neural network (CNN). We consider 18 word features and each word feature is
constructed by merging similar word labels. By introducing the concept of
external library, we propose a feature set approach that is beneficial for
building the relationship between a word from the training dataset and the
feature. Computational results are reported using the ATIS dataset and
comparisons with traditional CNN as well as bi-directional sequential CNN are
also presented.