pwc-archive/papers-with-abstracts huggingface dataset:
Adapting Neural Text Classification for Improved Software Categorization
Authors: Alexander LeClair, Zachary Eberhart, Collin McMillan
Date: 2018-06-05 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

Software Categorization is the task of organizing software into groups that
broadly describe the behavior of the software, such as "editors" or "science."
Categorization plays an important role in several maintenance tasks, such as
repository navigation and feature elicitation. Current approaches attempt to
cast the problem as text classification, to make use of the rich body of
literature from the NLP domain. However, as we will show in this paper, text
classification algorithms are generally not applicable off-the-shelf to source
code; we found that they work well when high-level project descriptions are
available, but suffer very large performance penalties when classifying source
code and comments only. We propose a set of adaptations to a state-of-the-art
neural classification algorithm and perform two evaluations: one with reference
data from Debian end-user programs, and one with a set of C/C++ libraries that
we hired professional programmers to annotate. We show that our proposed
approach achieves performance exceeding that of previous software
classification techniques as well as a state-of-the-art neural text
classification technique.
A Dataset for Building Code-Mixed Goal Oriented Conversation Systems
Authors: Suman Banerjee, Nikita Moghe, Siddhartha Arora, Mitesh M. Khapra
Date: 2018-06-15 00:00:00
Proceeding: COLING 2018 8
Tasks: Goal-Oriented Dialog

There is an increasing demand for goal-oriented conversation systems which
can assist users in various day-to-day activities such as booking tickets,
restaurant reservations, shopping, etc. Most of the existing datasets for
building such conversation systems focus on monolingual conversations and there
is hardly any work on multilingual and/or code-mixed conversations. Such
datasets and systems thus do not cater to the multilingual regions of the
world, such as India, where it is very common for people to speak more than one
language and seamlessly switch between them resulting in code-mixed
conversations. For example, a Hindi speaking user looking to book a restaurant
would typically ask, "Kya tum is restaurant mein ek table book karne mein meri
help karoge?" ("Can you help me in booking a table at this restaurant?"). To
facilitate the development of such code-mixed conversation models, we build a
goal-oriented dialog dataset containing code-mixed conversations. Specifically,
we take the text from the DSTC2 restaurant reservation dataset and create
code-mixed versions of it in Hindi-English, Bengali-English, Gujarati-English
and Tamil-English. We also establish initial baselines on this dataset using
existing state of the art models. This dataset along with our baseline
implementations is made publicly available for research purposes.
Ego-Lane Analysis System (ELAS): Dataset and Algorithms
Authors: Rodrigo F. Berriel, Edilson de Aguiar, Alberto F. de Souza, Thiago Oliveira-Santos
Date: 2018-06-15 00:00:00
Tasks: Change Detection, General Classification, Self-Driving Cars

Decreasing costs of vision sensors and advances in embedded hardware boosted
lane related research detection, estimation, and tracking in the past two
decades. The interest in this topic has increased even more with the demand for
advanced driver assistance systems (ADAS) and self-driving cars. Although
extensively studied independently, there is still need for studies that propose
a combined solution for the multiple problems related to the ego-lane, such as
lane departure warning (LDW), lane change detection, lane marking type (LMT)
classification, road markings detection and classification, and detection of
adjacent lanes (i.e., immediate left and right lanes) presence. In this paper,
we propose a real-time Ego-Lane Analysis System (ELAS) capable of estimating
ego-lane position, classifying LMTs and road markings, performing LDW and
detecting lane change events. The proposed vision-based system works on a
temporal sequence of images. Lane marking features are extracted in perspective
and Inverse Perspective Mapping (IPM) images that are combined to increase
robustness. The final estimated lane is modeled as a spline using a combination
of methods (Hough lines with Kalman filter and spline with particle filter).
Based on the estimated lane, all other events are detected. To validate ELAS
and cover the lack of lane datasets in the literature, a new dataset with more
than 20 different scenes (in more than 15,000 frames) and considering a variety
of scenarios (urban road, highways, traffic, shadows, etc.) was created. The
dataset was manually annotated and made publicly available to enable evaluation
of several events that are of interest for the research community (i.e., lane
estimation, change, and centering; road markings; intersections; LMTs;
crosswalks and adjacent lanes). ELAS achieved high detection rates in all
real-world events and proved to be ready for real-time applications.
Uncertainty Estimations by Softplus normalization in Bayesian Convolutional Neural Networks with Variational Inference
Authors: Kumar Shridhar, Felix Laumann, Marcus Liwicki
Date: 2018-06-15 00:00:00
Tasks: Bayesian Inference, General Classification, Variational Inference

We introduce a novel uncertainty estimation for classification tasks for Bayesian convolutional neural networks with variational inference. By normalizing the output of a Softplus function in the final layer, we estimate aleatoric and epistemic uncertainty in a coherent manner. The intractable posterior probability distributions over weights are inferred by Bayes by Backprop. Firstly, we demonstrate how this reliable variational inference method can serve as a fundamental construct for various network architectures. On multiple datasets in supervised learning settings (MNIST, CIFAR-10, CIFAR-100), this variational inference method achieves performances equivalent to frequentist inference in identical architectures, while the two desiderata, a measure for uncertainty and regularization are incorporated naturally. Secondly, we examine how our proposed measure for aleatoric and epistemic uncertainties is derived and validate it on the aforementioned datasets.
From probabilistic graphical models to generalized tensor networks for supervised learning
Authors: Ivan Glasser, Nicola Pancotti, J. Ignacio Cirac
Date: 2018-06-15 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, Tensor Networks

Tensor networks have found a wide use in a variety of applications in physics and computer science, recently leading to both theoretical insights as well as practical algorithms in machine learning. In this work we explore the connection between tensor networks and probabilistic graphical models, and show that it motivates the definition of generalized tensor networks where information from a tensor can be copied and reused in other parts of the network. We discuss the relationship between generalized tensor network architectures used in quantum physics, such as string-bond states, and architectures commonly used in machine learning. We provide an algorithm to train these networks in a supervised-learning context and show that they overcome the limitations of regular tensor networks in higher dimensions, while keeping the computation efficient. A method to combine neural networks and tensor networks as part of a common deep learning architecture is also introduced. We benchmark our algorithm for several generalized tensor network architectures on the task of classifying images and sounds, and show that they outperform previously introduced tensor-network algorithms. The models we consider also have a natural implementation on a quantum computer and may guide the development of near-term quantum machine learning architectures.
Techniques for visualizing LSTMs applied to electrocardiograms
Authors: Jos van der Westhuizen, Joan Lasenby
Date: 2017-05-23 00:00:00
Tasks: Time Series, Time Series Analysis

This paper explores four different visualization techniques for long
short-term memory (LSTM) networks applied to continuous-valued time series. On
the datasets analysed, we find that the best visualization technique is to
learn an input deletion mask that optimally reduces the true class score. With
a specific focus on single-lead electrocardiograms from the MIT-BIH arrhythmia
dataset, we show that salient input features for the LSTM classifier align well
with medical theory.
Deep-Temporal LSTM for Daily Living Action Recognition
Authors: Srijan Das, Michal Koperski, Francois Bremond, Gianpiero Francesca
Date: 2018-02-01 00:00:00
Tasks: Action Recognition, General Classification, Optical Flow Estimation, Temporal Action Localization, Video Classification

In this paper, we propose to improve the traditional use of RNNs by employing
a many to many model for video classification. We analyze the importance of
modeling spatial layout and temporal encoding for daily living action
recognition. Many RGB methods focus only on short term temporal information
obtained from optical flow. Skeleton based methods on the other hand show that
modeling long term skeleton evolution improves action recognition accuracy. In
this work, we propose a deep-temporal LSTM architecture which extends standard
LSTM and allows better encoding of temporal information. In addition, we
propose to fuse 3D skeleton geometry with deep static appearance. We validate
our approach on public available CAD60, MSRDailyActivity3D and NTU-RGB+D,
achieving competitive performance as compared to the state-of-the art.
Controllable Semantic Image Inpainting
Authors: Jin Xu, Yee Whye Teh
Date: 2018-06-15 00:00:00
Tasks: Image Inpainting

We develop a method for user-controllable semantic image inpainting: Given an
arbitrary set of observed pixels, the unobserved pixels can be imputed in a
user-controllable range of possibilities, each of which is semantically
coherent and locally consistent with the observed pixels. We achieve this using
a deep generative model bringing together: an encoder which can encode an
arbitrary set of observed pixels, latent variables which are trained to
represent disentangled factors of variations, and a bidirectional PixelCNN
model. We experimentally demonstrate that our method can generate plausible
inpainting results matching the user-specified semantics, but is still coherent
with observed pixels. We justify our choices of architecture and training
regime through more experiments.
Amortized Context Vector Inference for Sequence-to-Sequence Networks
Authors: Kyriacos Tolias, Ioannis Kourouklides, Sotirios Chatzis
Date: 2018-05-23 00:00:00
Tasks: Document Summarization, Variational Inference, Video Captioning

Neural attention (NA) has become a key component of sequence-to-sequence
models that yield state-of-the-art performance in as hard tasks as abstractive
document summarization (ADS) and video captioning (VC). NA mechanisms perform
inference of context vectors; these constitute weighted sums of deterministic
input sequence encodings, adaptively sourced over long temporal horizons.
Inspired from recent work in the field of amortized variational inference
(AVI), in this work we consider treating the context vectors generated by
soft-attention (SA) models as latent variables, with approximate finite mixture
model posteriors inferred via AVI. We posit that this formulation may yield
stronger generalization capacity, in line with the outcomes of existing
applications of AVI to deep networks. To illustrate our method, we implement it
and experimentally evaluate it considering challenging ADS, VC, and MT
benchmarks. This way, we exhibit its improved effectiveness over
state-of-the-art alternatives.
A Challenge Set for French --> English Machine Translation
Authors: Pierre Isabelle, Roland Kuhn
Date: 2018-06-07 00:00:00
Tasks: Machine Translation, Translation

We present a challenge set for French --> English machine translation based
on the approach introduced in Isabelle, Cherry and Foster (EMNLP 2017). Such
challenge sets are made up of sentences that are expected to be relatively
difficult for machines to translate correctly because their most
straightforward translations tend to be linguistically divergent. We present
here a set of 506 manually constructed French sentences, 307 of which are
targeted to the same kinds of structural divergences as in the paper mentioned
above. The remaining 199 sentences are designed to test the ability of the
systems to correctly translate difficult grammatical words such as
prepositions. We report on the results of using this challenge set for testing
two different systems, namely Google Translate and DEEPL, each on two different
dates (October 2017 and January 2018). All the resulting data are made publicly
available.
Discovering User Groups for Natural Language Generation
Authors: Nikos Engonopoulos, Christoph Teichmann, Alexander Koller
Date: 2018-06-15 00:00:00
Proceeding: WS 2018 7
Tasks: Referring Expression, Text Generation

We present a model which predicts how individual users of a dialog system
understand and produce utterances based on user groups. In contrast to previous
work, these user groups are not specified beforehand, but learned in training.
We evaluate on two referring expression (RE) generation tasks; our experiments
show that our model can identify user groups and learn how to most effectively
talk to them, and can dynamically assign unseen users to the correct groups as
they interact with the system.
Efficient Nearest Neighbors Search for Large-Scale Landmark Recognition
Authors: Federico Magliani, Tomaso Fontanini, Andrea Prati
Date: 2018-06-15 00:00:00
Tasks: Landmark Recognition, Retrieval

The problem of landmark recognition has achieved excellent results in
small-scale datasets. When dealing with large-scale retrieval, issues that were
irrelevant with small amount of data, quickly become fundamental for an
efficient retrieval phase. In particular, computational time needs to be kept
as low as possible, whilst the retrieval accuracy has to be preserved as much
as possible. In this paper we propose a novel multi-index hashing method called
Bag of Indexes (BoI) for Approximate Nearest Neighbors (ANN) search. It allows
to drastically reduce the query time and outperforms the accuracy results
compared to the state-of-the-art methods for large-scale landmark recognition.
It has been demonstrated that this family of algorithms can be applied on
different embedding techniques like VLAD and R-MAC obtaining excellent results
in very short times on different public datasets: Holidays+Flickr1M, Oxford105k
and Paris106k.
RepMet: Representative-based metric learning for classification and one-shot object detection
Authors: Leonid Karlinsky, Joseph Shtok, Sivan Harary, Eli Schwartz, Amit Aides, Rogerio Feris, Raja Giryes, Alex M. Bronstein
Date: 2018-06-12 00:00:00
Tasks: Classification, Few-Shot Object Detection, General Classification, Metric Learning, Object, object-detection, Object Detection, One-Shot Object Detection

Distance metric learning (DML) has been successfully applied to object
classification, both in the standard regime of rich training data and in the
few-shot scenario, where each category is represented by only a few examples.
In this work, we propose a new method for DML that simultaneously learns the
backbone network parameters, the embedding space, and the multi-modal
distribution of each of the training categories in that space, in a single
end-to-end training process. Our approach outperforms state-of-the-art methods
for DML-based object classification on a variety of standard fine-grained
datasets. Furthermore, we demonstrate the effectiveness of our approach on the
problem of few-shot object detection, by incorporating the proposed DML
architecture as a classification head into a standard object detection model.
We achieve the best results on the ImageNet-LOC dataset compared to strong
baselines, when only a few training examples are available. We also offer the
community a new episodic benchmark based on the ImageNet dataset for the
few-shot object detection task.
Query K-means Clustering and the Double Dixie Cup Problem
Authors: I Chien, Chao Pan, Olgica Milenkovic
Date: 2018-06-15 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Clustering

We consider the problem of approximate $K$-means clustering with outliers and
side information provided by same-cluster queries and possibly noisy answers.
Our solution shows that, under some mild assumptions on the smallest cluster
size, one can obtain an $(1+\epsilon)$-approximation for the optimal potential
with probability at least $1-\delta$, where $\epsilon>0$ and $\delta\in(0,1)$,
using an expected number of $O(\frac{K^3}{\epsilon \delta})$ noiseless
same-cluster queries and comparison-based clustering of complexity $O(ndK +
\frac{K^3}{\epsilon \delta})$, here, $n$ denotes the number of points and $d$
the dimension of space. Compared to a handful of other known approaches that
perform importance sampling to account for small cluster sizes, the proposed
query technique reduces the number of queries by a factor of roughly
$O(\frac{K^6}{\epsilon^3})$, at the cost of possibly missing very small
clusters. We extend this settings to the case where some queries to the oracle
produce erroneous information, and where certain points, termed outliers, do
not belong to any clusters. Our proof techniques differ from previous methods
used for $K$-means clustering analysis, as they rely on estimating the sizes of
the clusters and the number of points needed for accurate centroid estimation
and subsequent nontrivial generalizations of the double Dixie cup problem. We
illustrate the performance of the proposed algorithm both on synthetic and real
datasets, including MNIST and CIFAR $10$.
Dynamic Weight Alignment for Temporal Convolutional Neural Networks
Authors: Brian Kenji Iwana, Seiichi Uchida
Date: 2017-12-18 00:00:00
Tasks: Dynamic Time Warping, Time Series Analysis

In this paper, we propose a method of improving temporal Convolutional Neural
Networks (CNN) by determining the optimal alignment of weights and inputs using
dynamic programming. Conventional CNN convolutions linearly match the shared
weights to a window of the input. However, it is possible that there exists a
more optimal alignment of weights. Thus, we propose the use of Dynamic Time
Warping (DTW) to dynamically align the weights to the input of the
convolutional layer. Specifically, the dynamic alignment overcomes issues such
as temporal distortion by finding the minimal distance matching of the weights
and the inputs under constraints. We demonstrate the effectiveness of the
proposed architecture on the Unipen online handwritten digit and character
datasets, the UCI Spoken Arabic Digit dataset, and the UCI Activities of Daily
Life dataset.
Learning Semantic Sentence Embeddings using Sequential Pair-wise Discriminator
Authors: Badri N. Patro, Vinod K. Kurmi, Sandeep Kumar, Vinay P. Namboodiri
Date: 2018-06-03 00:00:00
Proceeding: COLING 2018 8
Tasks: Decoder, Paraphrase Generation, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Sentiment Analysis

In this paper, we propose a method for obtaining sentence-level embeddings.
While the problem of securing word-level embeddings is very well studied, we
propose a novel method for obtaining sentence-level embeddings. This is
obtained by a simple method in the context of solving the paraphrase generation
task. If we use a sequential encoder-decoder model for generating paraphrase,
we would like the generated paraphrase to be semantically close to the original
sentence. One way to ensure this is by adding constraints for true paraphrase
embeddings to be close and unrelated paraphrase candidate sentence embeddings
to be far. This is ensured by using a sequential pair-wise discriminator that
shares weights with the encoder that is trained with a suitable loss function.
Our loss function penalizes paraphrase sentence embedding distances from being
too large. This loss is used in combination with a sequential encoder-decoder
network. We also validated our method by evaluating the obtained embeddings for
a sentiment analysis task. The proposed method results in semantic embeddings
and outperforms the state-of-the-art on the paraphrase generation and sentiment
analysis task on standard datasets. These results are also shown to be
statistically significant.
Bayesian Best-Arm Identification for Selecting Influenza Mitigation Strategies
Authors: Pieter Libin, Timothy Verstraeten, Diederik M. Roijers, Jelena Grujic, Kristof Theys, Philippe Lemey, Ann Nowé
Date: 2017-11-16 00:00:00
Tasks: Decision Making, Thompson Sampling

Pandemic influenza has the epidemic potential to kill millions of people.
While various preventive measures exist (i.a., vaccination and school
closures), deciding on strategies that lead to their most effective and
efficient use remains challenging. To this end, individual-based
epidemiological models are essential to assist decision makers in determining
the best strategy to curb epidemic spread. However, individual-based models are
computationally intensive and it is therefore pivotal to identify the optimal
strategy using a minimal amount of model evaluations. Additionally, as
epidemiological modeling experiments need to be planned, a computational budget
needs to be specified a priori. Consequently, we present a new sampling
technique to optimize the evaluation of preventive strategies using fixed
budget best-arm identification algorithms. We use epidemiological modeling
theory to derive knowledge about the reward distribution which we exploit using
Bayesian best-arm identification algorithms (i.e., Top-two Thompson sampling
and BayesGap). We evaluate these algorithms in a realistic experimental setting
and demonstrate that it is possible to identify the optimal strategy using only
a limited number of model evaluations, i.e., 2-to-3 times faster compared to
the uniform sampling method, the predominant technique used for epidemiological
decision making in the literature. Finally, we contribute and evaluate a
statistic for Top-two Thompson sampling to inform the decision makers about the
confidence of an arm recommendation.
Robust Bayesian Model Selection for Variable Clustering with the Gaussian Graphical Model
Authors: Daniel Andrade, Akiko Takeda, Kenji Fukumizu
Date: 2018-06-15 00:00:00
Tasks: Clustering, model, Model Selection

Variable clustering is important for explanatory analysis. However, only few
dedicated methods for variable clustering with the Gaussian graphical model
have been proposed. Even more severe, small insignificant partial correlations
due to noise can dramatically change the clustering result when evaluating for
example with the Bayesian Information Criteria (BIC). In this work, we try to
address this issue by proposing a Bayesian model that accounts for negligible
small, but not necessarily zero, partial correlations. Based on our model, we
propose to evaluate a variable clustering result using the marginal likelihood.
To address the intractable calculation of the marginal likelihood, we propose
two solutions: one based on a variational approximation, and another based on
MCMC. Experiments on simulated data shows that the proposed method is similarly
accurate as BIC in the no noise setting, but considerably more accurate when
there are noisy partial correlations. Furthermore, on real data the proposed
method provides clustering results that are intuitively sensible, which is not
always the case when using BIC or its extensions.