pwc-archive/papers-with-abstracts huggingface dataset:
SL$^2$MF: Predicting Synthetic Lethality in Human Cancers via Logistic Matrix Factorization
Authors: Yong Liu, Min Wu, Chenghao Liu, Xiao-Li Li, Jie Zheng
Date: 2018-10-20 00:00:00

Synthetic lethality (SL) is a promising concept for novel discovery of
anti-cancer drug targets. However, wet-lab experiments for detecting SLs are
faced with various challenges, such as high cost, low consistency across
platforms or cell lines. Therefore, computational prediction methods are needed
to address these issues. This paper proposes a novel SL prediction method,
named SL2MF, which employs logistic matrix factorization to learn latent
representations of genes from the observed SL data. The probability that two
genes are likely to form SL is modeled by the linear combination of gene latent
vectors. As known SL pairs are more trustworthy than unknown pairs, we design
importance weighting schemes to assign higher importance weights for known SL
pairs and lower importance weights for unknown pairs in SL2MF. Moreover, we
also incorporate biological knowledge about genes from protein-protein
interaction (PPI) data and Gene Ontology (GO). In particular, we calculate the
similarity between genes based on their GO annotations and topological
properties in the PPI network. Extensive experiments on the SL interaction data
from SynLethDB database have been conducted to demonstrate the effectiveness of
SL2MF.
The Ocean Tensor Package
Authors: Ewout van den Berg
Date: 2018-10-20 00:00:00
Tasks: CPU, GPU

Matrix and tensor operations form the basis of a wide range of fields and
applications, and in many cases constitute a substantial part of the overall
computational complexity. The ability of general-purpose GPUs to speed up many
of these operations and enable others has resulted in a widespread adaptation
of these devices. In order for tensor operations to take full advantage of the
computational power, specialized software is required, and currently there
exist several packages (predominantly in the area of deep learning) that
incorporate tensor operations on both CPU and GPU. Nevertheless, a stand-alone
framework that supports general tensor operations is still missing. In this
paper we fill this gap and propose the Ocean Tensor Library: a modular
tensor-support package that is designed to serve as a foundational layer for
applications that require dense tensor operations on a variety of device types.
The API is carefully designed to be powerful, extensible, and at the same time
easy to use. The package is available as open source.
Learning Personas from Dialogue with Attentive Memory Networks
Authors: Eric Chu, Prashanth Vijayaraghavan, Deb Roy
Date: 2018-10-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Dialogue Generation, General Classification

The ability to infer persona from dialogue can have applications in areas
ranging from computational narrative analysis to personalized dialogue
generation. We introduce neural models to learn persona embeddings in a
supervised character trope classification task. The models encode dialogue
snippets from IMDB into representations that can capture the various categories
of film characters. The best-performing models use a multi-level attention
mechanism over a set of utterances. We also utilize prior knowledge in the form
of textual descriptions of the different tropes. We apply the learned
embeddings to find similar characters across different movies, and cluster
movies according to the distribution of the embeddings. The use of short
conversational text as input, and the ability to learn from prior knowledge
using memory, suggests these methods could be applied to other domains.
Mobile Sound Recognition for the Deaf and Hard of Hearing
Authors: Leonardo A. Fanzeres, Adriana S. Vivacqua, Luiz W. P. Biscainho
Date: 2018-10-19 00:00:00
Tasks: General Classification

Human perception of surrounding events is strongly dependent on audio cues.
Thus, acoustic insulation can seriously impact situational awareness. We
present an exploratory study in the domain of assistive computing, eliciting
requirements and presenting solutions to problems found in the development of
an environmental sound recognition system, which aims to assist deaf and hard
of hearing people in the perception of sounds. To take advantage of smartphones
computational ubiquity, we propose a system that executes all processing on the
device itself, from audio features extraction to recognition and visual
presentation of results. Our application also presents the confidence level of
the classification to the user. A test of the system conducted with deaf users
provided important and inspiring feedback from participants.
Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing
Authors: Magnus Wrenninge, Jonas Unger
Date: 2018-10-19 00:00:00
Tasks: Scene Parsing, Street Scene Parsing

We introduce Synscapes -- a synthetic dataset for street scene parsing
created using photorealistic rendering techniques, and show state-of-the-art
results for training and validation as well as new types of analysis. We study
the behavior of networks trained on real data when performing inference on
synthetic data: a key factor in determining the equivalence of simulation
environments. We also compare the behavior of networks trained on synthetic
data and evaluated on real-world data. Additionally, by analyzing pre-trained,
existing segmentation and detection models, we illustrate how uncorrelated
images along with a detailed set of annotations open up new avenues for
analysis of computer vision systems, providing fine-grain information about how
a model's performance changes according to factors such as distance, occlusion
and relative object orientation.
Safe Reinforcement Learning with Model Uncertainty Estimates
Authors: Björn Lütjens, Michael Everett, Jonathan P. How
Date: 2018-10-19 00:00:00
Tasks: Collision Avoidance, model, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Many current autonomous systems are being designed with a strong reliance on
black box predictions from deep neural networks (DNNs). However, DNNs tend to
be overconfident in predictions on unseen data and can give unpredictable
results for far-from-distribution test data. The importance of predictions that
are robust to this distributional shift is evident for safety-critical
applications, such as collision avoidance around pedestrians. Measures of model
uncertainty can be used to identify unseen data, but the state-of-the-art
extraction methods such as Bayesian neural networks are mostly intractable to
compute. This paper uses MC-Dropout and Bootstrapping to give computationally
tractable and parallelizable uncertainty estimates. The methods are embedded in
a Safe Reinforcement Learning framework to form uncertainty-aware navigation
around pedestrians. The result is a collision avoidance policy that knows what
it does not know and cautiously avoids pedestrians that exhibit unseen
behavior. The policy is demonstrated in simulation to be more robust to novel
observations and take safer actions than an uncertainty-unaware baseline.
pioNER: Datasets and Baselines for Armenian Named Entity Recognition
Authors: Tsolak Ghukasyan, Garnik Davtyan, Karen Avetisyan, Ivan Andrianov
Date: 2018-10-19 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Word Embeddings

In this work, we tackle the problem of Armenian named entity recognition,
providing silver- and gold-standard datasets as well as establishing baseline
results on popular models. We present a 163000-token named entity corpus
automatically generated and annotated from Wikipedia, and another 53400-token
corpus of news sentences with manual annotation of people, organization and
location named entities. The corpora were used to train and evaluate several
popular named entity recognition models. Alongside the datasets, we release
50-, 100-, 200-, 300-dimensional GloVe word embeddings trained on a collection
of Armenian texts from Wikipedia, news, blogs, and encyclopedia.
Understanding Deep Convolutional Networks through Gestalt Theory
Authors: Angelos Amanatiadis, Vasileios Kaburlasos, Elias Kosmatopoulos
Date: 2018-10-19 00:00:00
Tasks: General Classification

The superior performance of deep convolutional networks over high-dimensional
problems have made them very popular for several applications. Despite their
wide adoption, their underlying mechanisms still remain unclear with their
improvement procedures still relying mainly on a trial and error process. We
introduce a novel sensitivity analysis based on the Gestalt theory for giving
insights into the classifier function and intermediate layers. Since Gestalt
psychology stipulates that perception can be a product of complex interactions
among several elements, we perform an ablation study based on this concept to
discover which principles and image context significantly contribute in the
network classification. Our results reveal that ConvNets follow most of the
visual cortical perceptual mechanisms defined by the Gestalt principles at
several levels. The proposed framework stimulates specific feature maps in
classification problems and reveal important network attributes that can
produce more explainable network models.
Hows and Whys of Artificial Intelligence for Public Sector Decisions: Explanation and Evaluation
Authors: Alun Preece, Rob Ashelford, Harry Armstrong, Dave Braines
Date: 2018-09-28 00:00:00

Evaluation has always been a key challenge in the development of artificial
intelligence (AI) based software, due to the technical complexity of the
software artifact and, often, its embedding in complex sociotechnical
processes. Recent advances in machine learning (ML) enabled by deep neural
networks has exacerbated the challenge of evaluating such software due to the
opaque nature of these ML-based artifacts. A key related issue is the
(in)ability of such systems to generate useful explanations of their outputs,
and we argue that the explanation and evaluation problems are closely linked.
The paper models the elements of a ML-based AI system in the context of public
sector decision (PSD) applications involving both artificial and human
intelligence, and maps these elements against issues in both evaluation and
explanation, showing how the two are related. We consider a number of common
PSD application patterns in the light of our model, and identify a set of key
issues connected to explanation and evaluation in each case. Finally, we
propose multiple strategies to promote wider adoption of AI/ML technologies in
PSD, where each is distinguished by a focus on different elements of our model,
allowing PSD policy makers to adopt an approach that best fits their context
and concerns.
A Machine Learning Approach to Persian Text Readability Assessment Using a Crowdsourced Dataset
Authors: Hamid Mohammadi, Seyed Hossein Khasteh
Date: 2018-10-07 00:00:00
Tasks: BIG-bench Machine Learning

An automated approach to text readability assessment is essential to a language and can be a powerful tool for improving the understandability of texts written and published in that language. However, the Persian language, which is spoken by over 110 million speakers, lacks such a system. Unlike other languages such as English, French, and Chinese, very limited research studies have been carried out to build an accurate and reliable text readability assessment system for the Persian language. In the present research, the first Persian dataset for text readability assessment was gathered and the first model for Persian text readability assessment using machine learning was introduced. The experiments showed that this model was accurate and could assess the readability of Persian texts with a high degree of confidence. The results of this study can be used in a number of applications such as medical and educational text readability evaluation and have the potential to be the cornerstone of future studies in Persian text readability assessment.
Audio-Based Activities of Daily Living (ADL) Recognition with Large-Scale Acoustic Embeddings from Online Videos
Authors: Dawei Liang, Edison Thomaz
Date: 2018-10-19 00:00:00
Tasks: Activity Recognition

Over the years, activity sensing and recognition has been shown to play a key
enabling role in a wide range of applications, from sustainability and
human-computer interaction to health care. While many recognition tasks have
traditionally employed inertial sensors, acoustic-based methods offer the
benefit of capturing rich contextual information, which can be useful when
discriminating complex activities. Given the emergence of deep learning
techniques and leveraging new, large-scaled multi-media datasets, this paper
revisits the opportunity of training audio-based classifiers without the
onerous and time-consuming task of annotating audio data. We propose a
framework for audio-based activity recognition that makes use of millions of
embedding features from public online video sound clips. Based on the
combination of oversampling and deep learning approaches, our framework does
not require further feature processing or outliers filtering as in prior work.
We evaluated our approach in the context of Activities of Daily Living (ADL) by
recognizing 15 everyday activities with 14 participants in their own homes,
achieving 64.2% and 83.6% averaged within-subject accuracy in terms of top-1
and top-3 classification respectively. Individual class performance was also
examined in the paper to further study the co-occurrence characteristics of the
activities and the robustness of the framework.
Taking Advantage of Multitask Learning for Fair Classification
Authors: Luca Oneto, Michele Donini, Amon Elders, Massimiliano Pontil
Date: 2018-10-19 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification

A central goal of algorithmic fairness is to reduce bias in automated decision making. An unavoidable tension exists between accuracy gains obtained by using sensitive information (e.g., gender or ethnic group) as part of a statistical model, and any commitment to protect these characteristics. Often, due to biases present in the data, using the sensitive information in the functional form of a classifier improves classification accuracy. In this paper we show how it is possible to get the best of both worlds: optimize model accuracy and fairness without explicitly using the sensitive feature in the functional form of the model, thereby treating different individuals equally. Our method is based on two key ideas. On the one hand, we propose to use Multitask Learning (MTL), enhanced with fairness constraints, to jointly learn group specific classifiers that leverage information between sensitive groups. On the other hand, since learning group specific models might not be permitted, we propose to first predict the sensitive features by any learning method and then to use the predicted sensitive feature to train MTL with fairness constraints. This enables us to tackle fairness with a three-pronged approach, that is, by increasing accuracy on each group, enforcing measures of fairness during training, and protecting sensitive information during testing. Experimental results on two real datasets support our proposal, showing substantial improvements in both accuracy and fairness.
Lightweight Convolutional Approaches to Reading Comprehension on SQuAD
Authors: Tobin Bell, Benjamin Penchas
Date: 2018-10-19 00:00:00
Tasks: Question Answering, Reading Comprehension

Current state-of-the-art reading comprehension models rely heavily on
recurrent neural networks. We explored an entirely different approach to
question answering: a convolutional model. By their nature, these convolutional
models are fast to train and capture local dependencies well, though they can
struggle with longer-range dependencies and thus require augmentation to
achieve comparable performance to RNN-based models. We conducted over two dozen
controlled experiments with convolutional models and various
kernel/attention/regularization schemes to determine the precise performance
gains of each strategy, while maintaining a focus on speed. We ultimately
ensembled three models: crossconv (0.5398 dev F1), attnconv (0.5665), and
maybeconv (0.5285). The ensembled model was able to achieve a 0.6238 F1 score
using the official SQuAD evaluation script. Our individual convolutional model
crossconv was able to exceed the performance of the RNN-plus-attention baseline
by 25% while training 6 times faster.
Optimization of Molecules via Deep Reinforcement Learning
Authors: Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare, Patrick Riley
Date: 2018-10-19 00:00:00
Tasks: Deep Reinforcement Learning, Molecular Graph Generation, Multi-Objective Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN),
for molecule optimization by combining domain knowledge of chemistry and
state-of-the-art reinforcement learning techniques (double $Q$-learning and
randomized value functions). We directly define modifications on molecules,
thereby ensuring 100\% chemical validity. Further, we operate without
pre-training on any dataset to avoid possible bias from the choice of that set.
Inspired by problems faced during medicinal chemistry lead optimization, we
extend our model with multi-objective reinforcement learning, which maximizes
drug-likeness while maintaining similarity to the original molecule. We further
show the path through chemical space to achieve optimization for a molecule to
understand how the model works.
Subset Scanning Over Neural Network Activations
Authors: Skyler Speakman, Srihari Sridharan, Sekou Remy, Komminist Weldemariam, Edward McFowland
Date: 2018-10-19 00:00:00

This work views neural networks as data generating systems and applies
anomalous pattern detection techniques on that data in order to detect when a
network is processing an anomalous input. Detecting anomalies is a critical
component for multiple machine learning problems including detecting
adversarial noise. More broadly, this work is a step towards giving neural
networks the ability to recognize an out-of-distribution sample. This is the
first work to introduce "Subset Scanning" methods from the anomalous pattern
detection domain to the task of detecting anomalous input of neural networks.
Subset scanning treats the detection problem as a search for the most anomalous
subset of node activations (i.e., highest scoring subset according to
non-parametric scan statistics). Mathematical properties of these scoring
functions allow the search to be completed in log-linear rather than
exponential time while still guaranteeing the most anomalous subset of nodes in
the network is identified for a given input. Quantitative results for detecting
and characterizing adversarial noise are provided for CIFAR-10 images on a
simple convolutional neural network. We observe an "interference" pattern where
anomalous activations in shallow layers suppress the activation structure of
the original image in deeper layers.
A neural network to classify metaphorical violence on cable news
Authors: Matthew A. Turner
Date: 2018-10-19 00:00:00
Tasks: Transfer Learning

I present here an experimental system for identifying and annotating metaphor
in corpora. It is designed to plug in to Metacorps, an experimental web app for
annotating metaphor. As Metacorps users annotate metaphors, the system will use
user annotations as training data. When the system is confident, it will
suggest an identification and an annotation. Once approved by the user, this
becomes more training data. This naturally allows for transfer learning, where
the system can, with some known degree of reliability, classify one class of
metaphor after only being trained on another class of metaphor. For example, in
our metaphorical violence project, metaphors may be classified by the network
they were observed on, the grammatical subject or object of the violence
metaphor, or the violent word used (hit, attack, beat, etc.).
Using Machine Learning to reduce the energy wasted in Volunteer Computing Environments
Authors: A. Stephen McGough, Matthew Forshaw, John Brennan, Noura Al Moubayed, Stephen Bonner
Date: 2018-10-19 00:00:00
Tasks: BIG-bench Machine Learning

High Throughput Computing (HTC) provides a convenient mechanism for running
thousands of tasks. Many HTC systems exploit computers which are provisioned
for other purposes by utilising their idle time - volunteer computing. This has
great advantages as it gives access to vast quantities of computational power
for little or no cost. The downside is that running tasks are sacrificed if the
computer is needed for its primary use. Normally terminating the task which
must be restarted on a different computer - leading to wasted energy and an
increase in task completion time. We demonstrate, through the use of
simulation, how we can reduce this wasted energy by targeting tasks at
computers less likely to be needed for primary use, predicting this idle time
through machine learning. By combining two machine learning approaches, namely
Random Forest and MultiLayer Perceptron, we save 51.4% of the energy without
significantly affecting the time to complete tasks.
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
Authors: Natasha Jaques, Angeliki Lazaridou, Edward Hughes, Caglar Gulcehre, Pedro A. Ortega, DJ Strouse, Joel Z. Leibo, Nando de Freitas
Date: 2018-10-19 00:00:00
Proceeding: ICLR 2019 5
Tasks: counterfactual, Counterfactual Reasoning, Deep Reinforcement Learning, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a unified mechanism for achieving coordination and communication in Multi-Agent Reinforcement Learning (MARL), through rewarding agents for having causal influence over other agents' actions. Causal influence is assessed using counterfactual reasoning. At each timestep, an agent simulates alternate actions that it could have taken, and computes their effect on the behavior of other agents. Actions that lead to bigger changes in other agents' behavior are considered influential and are rewarded. We show that this is equivalent to rewarding agents for having high mutual information between their actions. Empirical results demonstrate that influence leads to enhanced coordination and communication in challenging social dilemma environments, dramatically increasing the learning curves of the deep RL agents, and leading to more meaningful learned communication protocols. The influence rewards for all agents can be computed in a decentralized way by enabling agents to learn a model of other agents using deep neural networks. In contrast, key previous works on emergent communication in the MARL setting were unable to learn diverse policies in a decentralized manner and had to resort to centralized training. Consequently, the influence reward opens up a window of new opportunities for research in this area.