pwc-archive/papers-with-abstracts huggingface dataset:
Spectral Methods from Tensor Networks
Authors: Ankur Moitra, Alexander S. Wein
Date: 2018-11-02 00:00:00
Tasks: Tensor Decomposition, Tensor Networks

A tensor network is a diagram that specifies a way to "multiply" a collection
of tensors together to produce another tensor (or matrix). Many existing
algorithms for tensor problems (such as tensor decomposition and tensor PCA),
although they are not presented this way, can be viewed as spectral methods on
matrices built from simple tensor networks. In this work we leverage the full
power of this abstraction to design new algorithms for certain continuous
tensor decomposition problems.
  An important and challenging family of tensor problems comes from orbit
recovery, a class of inference problems involving group actions (inspired by
applications such as cryo-electron microscopy). Orbit recovery problems over
finite groups can often be solved via standard tensor methods. However, for
infinite groups, no general algorithms are known. We give a new spectral
algorithm based on tensor networks for one such problem: continuous
multi-reference alignment over the infinite group SO(2). Our algorithm extends
to the more general heterogeneous case.
Progress and Tradeoffs in Neural Language Models
Authors: Raphael Tang, Jimmy Lin
Date: 2018-11-02 00:00:00
Tasks: Language Modeling, Language Modelling

In recent years, we have witnessed a dramatic shift towards techniques driven
by neural networks for a variety of NLP tasks. Undoubtedly, neural language
models (NLMs) have reduced perplexity by impressive amounts. This progress,
however, comes at a substantial cost in performance, in terms of inference
latency and energy consumption, which is particularly of concern in deployments
on mobile devices. This paper, which examines the quality-performance tradeoff
of various language modeling techniques, represents to our knowledge the first
to make this observation. We compare state-of-the-art NLMs with "classic"
Kneser-Ney (KN) LMs in terms of energy usage, latency, perplexity, and
prediction accuracy using two standard benchmarks. On a Raspberry Pi, we find
that orders of increase in latency and energy usage correspond to less change
in perplexity, while the difference is much less pronounced on a desktop.
CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge
Authors: Alon Talmor, Jonathan Herzig, Nicholas Lourie, Jonathan Berant
Date: 2018-11-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Common Sense Reasoning, Multiple-choice, Question Answering, World Knowledge

When answering a question, people often draw upon their rich world knowledge
in addition to the particular context. Recent work has focused primarily on
answering questions given some relevant document or context, and required very
little general background. To investigate question answering with prior
knowledge, we present CommonsenseQA: a challenging new dataset for commonsense
question answering. To capture common sense beyond associations, we extract
from ConceptNet (Speer et al., 2017) multiple target concepts that have the
same semantic relation to a single source concept. Crowd-workers are asked to
author multiple-choice questions that mention the source concept and
discriminate in turn between each of the target concepts. This encourages
workers to create questions with complex semantics that often require prior
knowledge. We create 12,247 questions through this procedure and demonstrate
the difficulty of our task with a large number of strong baselines. Our best
baseline is based on BERT-large (Devlin et al., 2018) and obtains 56% accuracy,
well below human performance, which is 89%.
Foundations of Comparison-Based Hierarchical Clustering
Authors: Debarghya Ghoshdastidar, Michaël Perrot, Ulrike Von Luxburg
Date: 2018-11-02 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Clustering

We address the classical problem of hierarchical clustering, but in a framework where one does not have access to a representation of the objects or their pairwise similarities. Instead, we assume that only a set of comparisons between objects is available, that is, statements of the form "objects $i$ and $j$ are more similar than objects $k$ and $l$." Such a scenario is commonly encountered in crowdsourcing applications. The focus of this work is to develop comparison-based hierarchical clustering algorithms that do not rely on the principles of ordinal embedding. We show that single and complete linkage are inherently comparison-based and we develop variants of average linkage. We provide statistical guarantees for the different methods under a planted hierarchical partition model. We also empirically demonstrate the performance of the proposed approaches on several datasets.
Convolutional Neural Networks for Epileptic Seizure Prediction
Authors: Matthias Eberlein, Raphael Hildebrand, Ronald Tetzlaff, Nico Hoffmann, Levin Kuhlmann, Benjamin Brinkmann, Jens Müller
Date: 2018-11-02 00:00:00
Tasks: Binary Classification, General Classification, Prediction, Seizure prediction

Epilepsy is the most common neurological disorder and an accurate forecast of seizures would help to overcome the patient's uncertainty and helplessness. In this contribution, we present and discuss a novel methodology for the classification of intracranial electroencephalography (iEEG) for seizure prediction. Contrary to previous approaches, we categorically refrain from an extraction of hand-crafted features and use a convolutional neural network (CNN) topology instead for both the determination of suitable signal characteristics and the binary classification of preictal and interictal segments. Three different models have been evaluated on public datasets with long-term recordings from four dogs and three patients. Overall, our findings demonstrate the general applicability. In this work we discuss the strengths and limitations of our methodology.
Single-Model Uncertainties for Deep Learning
Authors: Natasa Tagasovska, David Lopez-Paz
Date: 2018-11-02 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Deep Learning, model, Prediction Intervals, quantile regression, regression

We provide single-model estimates of aleatoric and epistemic uncertainty for deep neural networks. To estimate aleatoric uncertainty, we propose Simultaneous Quantile Regression (SQR), a loss function to learn all the conditional quantiles of a given target variable. These quantiles can be used to compute well-calibrated prediction intervals. To estimate epistemic uncertainty, we propose Orthonormal Certificates (OCs), a collection of diverse non-constant functions that map all training samples to zero. These certificates map out-of-distribution examples to non-zero values, signaling epistemic uncertainty. Our uncertainty estimators are computationally attractive, as they do not require ensembling or retraining deep models, and achieve competitive performance.
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling
Authors: Ilia Kulikov, Alexander H. Miller, Kyunghyun Cho, Jason Weston
Date: 2018-11-02 00:00:00
Proceeding: WS 2019 10
Tasks: Dialogue Generation, Diversity, Open-Ended Question Answering

We investigate the impact of search strategies in neural dialogue modeling. We first compare two standard search algorithms, greedy and beam search, as well as our newly proposed iterative beam search which produces a more diverse set of candidate responses. We evaluate these strategies in realistic full conversations with humans and propose a model-based Bayesian calibration to address annotator bias. These conversations are analyzed using two automatic metrics: log-probabilities assigned by the model and utterance diversity. Our experiments reveal that better search algorithms lead to higher rated conversations. However, finding the optimal selection mechanism to choose from a more diverse set of candidates is still an open question.
Probabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Authors: Maria I. Gorinova, Andrew D. Gordon, Charles Sutton
Date: 2018-11-02 00:00:00
Tasks: Probabilistic Programming

Stan is a probabilistic programming language that has been increasingly used
for real-world scalable projects. However, to make practical inference
possible, the language sacrifices some of its usability by adopting a block
syntax, which lacks compositionality and flexible user-defined functions.
Moreover, the semantics of the language has been mainly given in terms of
intuition about implementation, and has not been formalised.
  This paper provides a formal treatment of the Stan language, and introduces
the probabilistic programming language SlicStan --- a compositional,
self-optimising version of Stan. Our main contributions are: (1) the
formalisation of a core subset of Stan through an operational density-based
semantics; (2) the design and semantics of the Stan-like language SlicStan,
which facilities better code reuse and abstraction through its compositional
syntax, more flexible functions, and information-flow type system; and (3) a
formal, semantic-preserving procedure for translating SlicStan to Stan.
Classification of Findings with Localized Lesions in Fundoscopic Images using a Regionally Guided CNN
Authors: Jaemin Son, Woong Bae, Sangkeun Kim, Sang Jun Park, Kyu-Hwan Jung
Date: 2018-11-02 00:00:00
Tasks: General Classification

Fundoscopic images are often investigated by ophthalmologists to spot
abnormal lesions to make diagnoses. Recent successes of convolutional neural
networks are confined to diagnoses of few diseases without proper localization
of lesion. In this paper, we propose an efficient annotation method for
localizing lesions and a CNN architecture that can classify an individual
finding and localize the lesions at the same time. Also, we introduce a new
loss function to guide the network to learn meaningful patterns with the
guidance of the regional annotations. In experiments, we demonstrate that our
network performed better than the widely used network and the guidance loss
helps achieve higher AUROC up to 4.1% and superior localization capability.
Comparison of Classification Algorithms Used Medical Documents Categorization
Authors: Durmus Ozkan Sahin, Erdal Kilic
Date: 2018-11-02 00:00:00
Tasks: Classification, feature selection, General Classification, text-classification, Text Classification

Volume of text based documents have been increasing day by day. Medical
documents are located within this growing text documents. In this study, the
techniques used for text classification applied on medical documents and
evaluated classification performance. Used data sets are multi class and multi
labelled. Chi Square (CHI) technique was used for feature selection also SMO,
NB, C4.5, RF and KNN algorithms was used for classification. The aim of this
study, success of various classifiers is evaluated on multi class and multi
label data sets consisting of medical documents. The first 400 features, while
the most successful in the KNN classifier, feature number 400 and after the SMO
has become the most successful classifier.
Algorithms for screening of Cervical Cancer: A chronological review
Authors: Yasha Singh, Dhruv Srivastava, P. S. Chandranand, Dr. Surinder Singh
Date: 2018-11-02 00:00:00
Tasks: Cell Segmentation, Clustering, General Classification, regression

There are various algorithms and methodologies used for automated screening
of cervical cancer by segmenting and classifying cervical cancer cells into
different categories. This study presents a critical review of different
research papers published that integrated AI methods in screening cervical
cancer via different approaches analyzed in terms of typical metrics like
dataset size, drawbacks, accuracy etc. An attempt has been made to furnish the
reader with an insight of Machine Learning algorithms like SVM (Support Vector
Machines), GLCM (Gray Level Co-occurrence Matrix), k-NN (k-Nearest Neighbours),
MARS (Multivariate Adaptive Regression Splines), CNNs (Convolutional Neural
Networks), spatial fuzzy clustering algorithms, PNNs (Probabilistic Neural
Networks), Genetic Algorithm, RFT (Random Forest Trees), C5.0, CART
(Classification and Regression Trees) and Hierarchical clustering algorithm for
feature extraction, cell segmentation and classification. This paper also
covers the publicly available datasets related to cervical cancer. It presents
a holistic review on the computational methods that have evolved over the
period of time, in chronological order in detection of malignant cells.
Heterogeneity Aware Deep Embedding for Mobile Periocular Recognition
Authors: Rishabh Garg, Yashasvi Baweja, Soumyadeep Ghosh, Mayank Vatsa, Richa Singh, Nalini Ratha
Date: 2018-11-02 00:00:00
Tasks: Mobile Periocular Recognition

Mobile biometric approaches provide the convenience of secure authentication
with an omnipresent technology. However, this brings an additional challenge of
recognizing biometric patterns in unconstrained environment including
variations in mobile camera sensors, illumination conditions, and capture
distance. To address the heterogeneous challenge, this research presents a
novel heterogeneity aware loss function within a deep learning framework. The
effectiveness of the proposed loss function is evaluated for periocular
biometrics using the CSIP, IMP and VISOB mobile periocular databases. The
results show that the proposed algorithm yields state-of-the-art results in a
heterogeneous environment and improves generalizability for cross-database
experiments.
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
Authors: Angrosh Mandya, Danushka Bollegala, Frans Coenen, Katie Atkinson
Date: 2018-11-02 00:00:00
Proceeding: AKBC 2019
Tasks: Relation, Relation Extraction, Sentence, Word Embeddings

We propose in this paper a combined model of Long Short Term Memory and
Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and
positional embeddings for cross-sentence n-ary relation extraction. The
proposed model brings together the properties of both LSTMs and CNNs, to
simultaneously exploit long-range sequential information and capture most
informative features, essential for cross-sentence n-ary relation extraction.
The LSTM-CNN model is evaluated on standard dataset on cross-sentence n-ary
relation extraction, where it significantly outperforms baselines such as CNNs,
LSTMs and also a combined CNN-LSTM model. The paper also shows that the
LSTM-CNN model outperforms the current state-of-the-art methods on
cross-sentence n-ary relation extraction.
ATP: Directed Graph Embedding with Asymmetric Transitivity Preservation
Authors: Jiankai Sun, Bortik Bandyopadhyay, Armin Bashizade, Jiongqian Liang, P. Sadayappan, Srinivasan Parthasarathy
Date: 2018-11-02 00:00:00
Tasks: Community Question Answering, Graph Embedding, Link Prediction, Question Answering

Directed graphs have been widely used in Community Question Answering
services (CQAs) to model asymmetric relationships among different types of
nodes in CQA graphs, e.g., question, answer, user. Asymmetric transitivity is
an essential property of directed graphs, since it can play an important role
in downstream graph inference and analysis. Question difficulty and user
expertise follow the characteristic of asymmetric transitivity. Maintaining
such properties, while reducing the graph to a lower dimensional vector
embedding space, has been the focus of much recent research. In this paper, we
tackle the challenge of directed graph embedding with asymmetric transitivity
preservation and then leverage the proposed embedding method to solve a
fundamental task in CQAs: how to appropriately route and assign newly posted
questions to users with the suitable expertise and interest in CQAs. The
technique incorporates graph hierarchy and reachability information naturally
by relying on a non-linear transformation that operates on the core
reachability and implicit hierarchy within such graphs. Subsequently, the
methodology levers a factorization-based approach to generate two embedding
vectors for each node within the graph, to capture the asymmetric transitivity.
Extensive experiments show that our framework consistently and significantly
outperforms the state-of-the-art baselines on two diverse real-world tasks:
link prediction, and question difficulty estimation and expert finding in
online forums like Stack Exchange. Particularly, our framework can support
inductive embedding learning for newly posted questions (unseen nodes during
training), and therefore can properly route and assign these kinds of questions
to experts in CQAs.
Multi-Kernel Regression with Sparsity Constraint
Authors: Shayan Aziznejad, Michael Unser
Date: 2018-11-02 00:00:00
Tasks: regression

In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regularization. We show that the solution admits a multi-kernel expansion with adaptive positions. In this representation, the number of active kernels is upper-bounded by the number of data points while the gTV regularization imposes an $\ell_1$ penalty on the kernel coefficients. Finally, we illustrate numerically the outcome of our theory.
OrthoNet: Multilayer Network Data Clustering
Authors: Mireille El Gheche, Giovanni Chierchia, Pascal Frossard
Date: 2018-11-02 00:00:00
Tasks: Clustering, Graph Clustering, Node Clustering

Network data appears in very diverse applications, like biological, social, or sensor networks. Clustering of network nodes into categories or communities has thus become a very common task in machine learning and data mining. Network data comes with some information about the network edges. In some cases, this network information can even be given with multiple views or multiple layers, each one representing a different type of relationship between the network nodes. Increasingly often, network nodes also carry a feature vector. We propose in this paper to extend the node clustering problem, that commonly considers only the network information, to a problem where both the network information and the node features are considered together for learning a clustering-friendly representation of the feature space. Specifically, we design a generic two-step algorithm for multilayer network data clustering. The first step aggregates the different layers of network information into a graph representation given by the geometric mean of the network Laplacian matrices. The second step uses a neural net to learn a feature embedding that is consistent with the structure given by the network layers. We propose a novel algorithm for efficiently training the neural net via stochastic gradient descent, which encourages the neural net outputs to span the leading eigenvectors of the aggregated Laplacian matrix, in order to capture the pairwise interactions on the network, and provide a clustering-friendly representation of the feature space. We demonstrate with an extensive set of experiments on synthetic and real datasets that our method leads to a significant improvement w.r.t. state-of-the-art multilayer graph clustering algorithms, as it judiciously combines nodes features and network information in the node embedding algorithms.
eLIAN: Enhanced Algorithm for Angle-constrained Path Finding
Authors: Anton Andreychuk, Natalia Soboleva, Konstantin Yakovlev
Date: 2018-11-02 00:00:00
Tasks: Sensitivity

Problem of finding 2D paths of special shape, e.g. paths comprised of line
segments having the property that the angle between any two consecutive
segments does not exceed the predefined threshold, is considered in the paper.
This problem is harder to solve than the one when shortest paths of any shape
are sought, since the planer's search space is substantially bigger as multiple
search nodes corresponding to the same location need to be considered. One way
to reduce the search effort is to fix the length of the path's segment and to
prune the nodes that violate the imposed constraint. This leads to
incompleteness and to the sensitivity of the 's performance to chosen parameter
value. In this work we introduce a novel technique that reduces this
sensitivity by automatically adjusting the length of the path's segment
on-the-fly, e.g. during the search. Embedding this technique into the known
grid-based angle-constrained path finding algorithm - LIAN, leads to notable
increase of the planner's effectiveness, e.g. success rate, while keeping
efficiency, e.g. runtime, overhead at reasonable level. Experimental evaluation
shows that LIAN with the suggested enhancements, dubbed eLIAN, solves up to
20\% of tasks more compared to the predecessor. Meanwhile, the solution quality
of eLIAN is nearly the same as the one of LIAN.
Automated Theorem Proving in Intuitionistic Propositional Logic by Deep Reinforcement Learning
Authors: Mitsuru Kusumoto, Keisuke Yahata, Masahiro Sakai
Date: 2018-11-02 00:00:00
Tasks: Automated Theorem Proving, Data Augmentation, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The problem-solving in automated theorem proving (ATP) can be interpreted as
a search problem where the prover constructs a proof tree step by step. In this
paper, we propose a deep reinforcement learning algorithm for proof search in
intuitionistic propositional logic. The most significant challenge in the
application of deep learning to the ATP is the absence of large, public theorem
database. We, however, overcame this issue by applying a novel data
augmentation procedure at each iteration of the reinforcement learning. We also
improve the efficiency of the algorithm by representing the syntactic structure
of formulas by a novel compact graph representation. Using the large volume of
augmented data, we train highly accurate graph neural networks that approximate
the value function for the set of the syntactic structures of formulas. Our
method is also cost-efficient in terms of computational time. We will show that
our prover outperforms Coq's $\texttt{tauto}$ tactic, a prover based on
human-engineered heuristics. Within the specified time limit, our prover solved
84% of the theorems in a benchmark library, while $\texttt{tauto}$ was able to
solve only 52%.
Dealing with Ambiguity in Robotic Grasping via Multiple Predictions
Authors: Ghazal Ghazaei, Iro Laina, Christian Rupprecht, Federico Tombari, Nassir Navab, Kianoush Nazarpour
Date: 2018-11-02 00:00:00
Tasks: Robotic Grasping

Humans excel in grasping and manipulating objects because of their life-long
experience and knowledge about the 3D shape and weight distribution of objects.
However, the lack of such intuition in robots makes robotic grasping an
exceptionally challenging task. There are often several equally viable options
of grasping an object. However, this ambiguity is not modeled in conventional
systems that estimate a single, optimal grasp position. We propose to tackle
this problem by simultaneously estimating multiple grasp poses from a single
RGB image of the target object. Further, we reformulate the problem of robotic
grasping by replacing conventional grasp rectangles with grasp belief maps,
which hold more precise location information than a rectangle and account for
the uncertainty inherent to the task. We augment a fully convolutional neural
network with a multiple hypothesis prediction model that predicts a set of
grasp hypotheses in under 60ms, which is critical for real-time robotic
applications. The grasp detection accuracy reaches over 90% for unseen objects,
outperforming the current state of the art on this task.