pwc-archive/papers-with-abstracts huggingface dataset:
To Compress, or Not to Compress: Characterizing Deep Learning Model Compression for Embedded Inference
Authors: Qing Qin, Jie Ren, Jialong Yu, Ling Gao, Hai Wang, Jie Zheng, Yansong Feng, Jianbin Fang, Zheng Wang
Date: 2018-10-21 00:00:00
Tasks: Deep Learning, image-classification, Image Classification, Model Compression, Quantization

The recent advances in deep neural networks (DNNs) make them attractive for
embedded systems. However, it can take a long time for DNNs to make an
inference on resource-constrained computing devices. Model compression
techniques can address the computation issue of deep inference on embedded
devices. This technique is highly attractive, as it does not rely on
specialized hardware, or computation-offloading that is often infeasible due to
privacy concerns or high latency. However, it remains unclear how model
compression techniques perform across a wide range of DNNs. To design efficient
embedded deep learning solutions, we need to understand their behaviors. This
work develops a quantitative approach to characterize model compression
techniques on a representative embedded deep learning architecture, the NVIDIA
Jetson Tx2. We perform extensive experiments by considering 11 influential
neural network architectures from the image classification and the natural
language processing domains. We experimentally show that how two mainstream
compression techniques, data quantization and pruning, perform on these network
architectures and the implications of compression techniques to the model
storage size, inference time, energy consumption and performance metrics. We
demonstrate that there are opportunities to achieve fast deep inference on
embedded systems, but one must carefully choose the compression settings. Our
results provide insights on when and how to apply model compression techniques
and guidelines for designing efficient embedded deep learning systems.
Electricity consumption forecasting method based on MPSO-BP neural network model
Authors: Youshan Zhang, Liangdong Guo, Qi Li, Junhui Li
Date: 2018-10-21 00:00:00

This paper deals with the problem of the electricity consumption forecasting
method. An MPSO-BP (modified particle swarm optimization-back propagation)
neural network model is constructed based on the history data of a mineral
company of Anshan in China. The simulation showed that the convergence of the
algorithm and forecasting accuracy using the obtained model are better than
those of other traditional ones, such as BP, PSO, fuzzy neural network and so
on. Then we predict the electricity consumption of each month in 2017 based on
the MPSO-BP neural network model.
Automated identification of hookahs (waterpipes) on Instagram: an application in feature extraction using Convolutional Neural Network and Support Vector Machine classification
Authors: Youshan Zhang, Jon-Patrick Allem, Jennifer B. Unger, Tess Boley Cruz
Date: 2018-10-21 00:00:00
Tasks: General Classification, image-classification, Image Classification

Background: Instagram, with millions of posts per day, can be used to inform
public health surveillance targets and policies. However, current research
relying on image-based data often relies on hand coding of images which is time
consuming and costly, ultimately limiting the scope of the study. Current best
practices in automated image classification (e.g., support vector machine
(SVM), Backpropagation (BP) neural network, and artificial neural network) are
limited in their capacity to accurately distinguish between objects within
images. Objective: This study demonstrates how convolutional neural network
(CNN) can be used to extract unique features within an image and how SVM can
then be used to classify the image. Methods: Images of waterpipes or hookah (an
emerging tobacco product possessing similar harms to that of cigarettes) were
collected from Instagram and used in analyses (n=840). CNN was used to extract
unique features from images identified to contain waterpipes. A SVM classifier
was built to distinguish between images with and without waterpipes. Methods
for image classification were then compared to show how a CNN + SVM classifier
could improve accuracy. Results: As the number of the validated training images
increased, the total number of extracted features increased. Additionally, as
the number of features learned by the SVM classifier increased, the average
level of accuracy increased. Overall, 99.5% of the 420 images classified were
correctly identified as either hookah or non-hookah images. This level of
accuracy was an improvement over earlier methods that used SVM, CNN or Bag of
Features (BOF) alone. Conclusions: CNN extracts more features of the images
allowing a SVM classifier to be better informed, resulting in higher accuracy
compared with methods that extract fewer features. Future research can use this
method to grow the scope of image-based studies.
A Regressive Convolution Neural network and Support Vector Regression Model for Electricity Consumption Forecasting
Authors: Youshan Zhang, Qi Li
Date: 2018-10-21 00:00:00
Tasks: Management, Prediction, regression

Electricity consumption forecasting has important implications for the
mineral companies on guiding quarterly work, normal power system operation, and
the management. However, electricity consumption prediction for the mineral
company is different from traditional electricity load prediction since mineral
company electricity consumption can be affected by various factors (e.g., ore
grade, processing quantity of the crude ore, ball milling fill rate). The
problem is non-trivial due to three major challenges for traditional methods:
insufficient training data, high computational cost and low prediction
accu-racy. To tackle these challenges, we firstly propose a Regressive
Convolution Neural Network (RCNN) to predict the electricity consumption. While
RCNN still suffers from high computation overhead, we utilize RCNN to extract
features from the history data and Regressive Support Vector Machine (SVR)
trained with the features to predict the electricity consumption. The
experimental results show that the proposed RCNN-SVR model achieves higher
accuracy than using the traditional RNN or SVM alone. The MSE, MAPE, and
CV-RMSE of RCNN-SVR model are 0.8564, 1.975%, and 0.0687% respectively, which
illustrates the low predicting error rate of the proposed model.
Sleep Arousal Detection from Polysomnography using the Scattering Transform and Recurrent Neural Networks
Authors: Philip Warrick, Masun Nabhan Homsi
Date: 2018-10-21 00:00:00
Tasks: Sleep Arousal Detection, STS

Sleep disorders are implicated in a growing number of health problems. In
this paper, we present a signal-processing/machine learning approach to
detecting arousals in the multi-channel polysomnographic recordings of the
Physionet/CinC Challenge2018 dataset.
  Methods: Our network architecture consists of two components. Inputs were
presented to a Scattering Transform (ST) representation layer which fed a
recurrent neural network for sequence learning using three layers of Long
Short-Term Memory (LSTM). The STs were calculated for each signal with
downsampling parameters chosen to give approximately 1 s time resolution,
resulting in an eighteen-fold data reduction. The LSTM layers then operated at
this downsampled rate.
  Results: The proposed approach detected arousal regions on the 10% random
sample of the hidden test set with an AUROC of 88.0% and an AUPRC of 42.1%.
Learning-based Application-Agnostic 3D NoC Design for Heterogeneous Manycore Systems
Authors: Biresh Kumar Joardar, Ryan Gary Kim, Janardhan Rao Doppa, Partha Pratim Pande, Diana Marculescu, Radu Marculescu
Date: 2018-10-20 00:00:00
Tasks: CPU, GPU

The rising use of deep learning and other big-data algorithms has led to an increasing demand for hardware platforms that are computationally powerful, yet energy-efficient. Due to the amount of data parallelism in these algorithms, high-performance 3D manycore platforms that incorporate both CPUs and GPUs present a promising direction. However, as systems use heterogeneity (e.g., a combination of CPUs, GPUs, and accelerators) to improve performance and efficiency, it becomes more pertinent to address the distinct and likely conflicting communication requirements (e.g., CPU memory access latency or GPU network throughput) that arise from such heterogeneity. Unfortunately, it is difficult to quickly explore the hardware design space and choose appropriate tradeoffs between these heterogeneous requirements. To address these challenges, we propose the design of a 3D Network-on-Chip (NoC) for heterogeneous manycore platforms that considers the appropriate design objectives for a 3D heterogeneous system and explores various tradeoffs using an efficient ML-based multi-objective optimization technique. The proposed design space exploration considers the various requirements of its heterogeneous components and generates a set of 3D NoC architectures that efficiently trades off these design objectives. Our findings show that by jointly considering these requirements (latency, throughput, temperature, and energy), we can achieve 9.6% better Energy-Delay Product on average at nearly iso-temperature conditions when compared to a thermally-optimized design for 3D heterogeneous NoCs. More importantly, our results suggest that our 3D NoCs optimized for a few applications can be generalized for unknown applications as well. Our results show that these generalized 3D NoCs only incur a 1.8% (36-tile system) and 1.1% (64-tile system) average performance loss compared to application-specific NoCs.
A Polynomial Time MCMC Method for Sampling from Continuous DPPs
Authors: Shayan Oveis Gharan, Alireza Rezaei
Date: 2018-10-20 00:00:00
Tasks: Point Processes

We study the Gibbs sampling algorithm for continuous determinantal point
processes. We show that, given a warm start, the Gibbs sampler generates a
random sample from a continuous $k$-DPP defined on a $d$-dimensional domain by
only taking $\text{poly}(k)$ number of steps. As an application, we design an
algorithm to generate random samples from $k$-DPPs defined by a spherical
Gaussian kernel on a unit sphere in $d$-dimensions, $\mathbb{S}^{d-1}$ in time
polynomial in $k,d$.
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
Authors: Mandar Joshi, Eunsol Choi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer
Date: 2018-10-20 00:00:00
Proceeding: NAACL 2019 6
Tasks: Common Sense Reasoning, Sentence, Word Embeddings

Reasoning about implied relationships (e.g., paraphrastic, common sense,
encyclopedic) between pairs of words is crucial for many cross-sentence
inference problems. This paper proposes new methods for learning and using
embeddings of word pairs that implicitly represent background knowledge about
such relationships. Our pairwise embeddings are computed as a compositional
function on word representations, which is learned by maximizing the pointwise
mutual information (PMI) with the contexts in which the two words co-occur. We
add these representations to the cross-sentence attention layer of existing
inference models (e.g. BiDAF for QA, ESIM for NLI), instead of extending or
replacing existing word embeddings. Experiments show a gain of 2.7% on the
recently released SQuAD2.0 and 1.3% on MultiNLI. Our representations also aid
in better generalization with gains of around 6-7% on adversarial SQuAD
datasets, and 8.8% on the adversarial entailment test set by Glockner et al.
(2018).
Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
Authors: Jing Li, Rafal K. Mantiuk, Junle Wang, Suiyi Ling, Patrick Le Callet
Date: 2018-10-20 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Bayesian Optimization, Computational Efficiency

In this paper we present a hybrid active sampling strategy for pairwise
preference aggregation, which aims at recovering the underlying rating of the
test candidates from sparse and noisy pairwise labelling. Our method employs
Bayesian optimization framework and Bradley-Terry model to construct the
utility function, then to obtain the Expected Information Gain (EIG) of each
pair. For computational efficiency, Gaussian-Hermite quadrature is used for
estimation of EIG. In this work, a hybrid active sampling strategy is proposed,
either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST)
sampling in each trial, which is determined by the test budget. The proposed
method has been validated on both simulated and real-world datasets, where it
shows higher preference aggregation ability than the state-of-the-art methods.
Corresponding Supine and Prone Colon Visualization Using Eigenfunction Analysis and Fold Modeling
Authors: Saad Nadeem, Joseph Marino, Xianfeng GU, Arie Kaufman
Date: 2018-10-20 00:00:00

We present a method for registration and visualization of corresponding
supine and prone virtual colonoscopy scans based on eigenfunction analysis and
fold modeling. In virtual colonoscopy, CT scans are acquired with the patient
in two positions, and their registration is desirable so that physicians can
corroborate findings between scans. Our algorithm performs this registration
efficiently through the use of Fiedler vector representation (the second
eigenfunction of the Laplace-Beltrami operator). This representation is
employed to first perform global registration of the two colon positions. The
registration is then locally refined using the haustral folds, which are
automatically segmented using the 3D level sets of the Fiedler vector. The use
of Fiedler vectors and the segmented folds presents a precise way of
visualizing corresponding regions across datasets and visual modalities. We
present multiple methods of visualizing the results, including 2D flattened
rendering and the corresponding 3D endoluminal views. The precise fold modeling
is used to automatically find a suitable cut for the 2D flattening, which
provides a less distorted visualization. Our approach is robust, and we
demonstrate its efficiency and efficacy by showing matched views on both the 2D
flattened colons and in the 3D endoluminal view. We analytically evaluate the
results by measuring the distance between features on the registered colons,
and we also assess our fold segmentation against 20 manually labeled datasets.
We have compared our results analytically to previous methods, and have found
our method to achieve superior results. We also prove the hot spots conjecture
for modeling cylindrical topology using Fiedler vector representation, which
allows our approach to be used for general cylindrical geometry modeling and
feature extraction.
Abstractive Summarization Using Attentive Neural Techniques
Authors: Jacob Krantz, Jugal Kalita
Date: 2018-10-20 00:00:00
Tasks: Abstractive Text Summarization, Decoder, Machine Translation, Sentence, Translation

In a world of proliferating data, the ability to rapidly summarize text is
growing in importance. Automatic summarization of text can be thought of as a
sequence to sequence problem. Another area of natural language processing that
solves a sequence to sequence problem is machine translation, which is rapidly
evolving due to the development of attention-based encoder-decoder networks.
This work applies these modern techniques to abstractive summarization. We
perform analysis on various attention mechanisms for summarization with the
goal of developing an approach and architecture aimed at improving the state of
the art. In particular, we modify and optimize a translation model with
self-attention for generating abstractive sentence summaries. The effectiveness
of this base model along with attention variants is compared and analyzed in
the context of standardized evaluation sets and test metrics. However, we show
that these metrics are limited in their ability to effectively score
abstractive summaries, and propose a new approach based on the intuition that
an abstractive model requires an abstractive evaluation.
Enriched Interpretation
Authors: Robert E. Kent
Date: 2018-10-20 00:00:00
Tasks: Management

The theory introduced, presented and developed in this paper, is concerned
with an enriched extension of the theory of Rough Sets pioneered by Zdzislaw
Pawlak. The enrichment discussed here is in the sense of valuated categories as
developed by F.W. Lawvere. This paper relates Rough Sets to an abstraction of
the theory of Fuzzy Sets pioneered by Lotfi Zadeh, and provides a natural
foundation for "soft computation". To paraphrase Lotfi Zadeh, the impetus for
the transition from a hard theory to a soft theory derives from the fact that
both the generality of a theory and its applicability to real-world problems
are substantially enhanced by replacing various hard concepts with their soft
counterparts. Here we discuss the corresponding enriched notions for
indiscernibility, subsets, upper/lower approximations, and rough sets.
Throughout, we indicate linkages with the theory of Formal Concept Analysis
pioneered by Rudolf Wille. We pay particular attention to the all-important
notion of a "linguistic variable" - developing its enriched extension,
comparing it with the notion of conceptual scale from Formal Concept Analysis,
and discussing the pragmatic issues of its creation and use in the
interpretation of data. These pragmatic issues are exemplified by the
discovery, conceptual analysis, interpretation, and categorization of networked
information resources in WAVE, the Web Analysis and Visualization Environment
currently being developed for the management and interpretation of the universe
of resource information distributed over the World-Wide Web.
Modeling Composite Labels for Neural Morphological Tagging
Authors: Alexander Tkachenko, Kairit Sirts
Date: 2018-10-20 00:00:00
Proceeding: CONLL 2018 10
Tasks: Morphological Tagging, POS, POS Tagging, TAG

Neural morphological tagging has been regarded as an extension to POS tagging
task, treating each morphological tag as a monolithic label and ignoring its
internal structure. We propose to view morphological tags as composite labels
and explicitly model their internal structure in a neural sequence tagger. For
this, we explore three different neural architectures and compare their
performance with both CRF and simple neural multiclass baselines. We evaluate
our models on 49 languages and show that the neural architecture that models
the morphological labels as sequences of morphological category values performs
significantly better than both baselines establishing state-of-the-art results
in morphological tagging for most languages.
Autonomous Self-Explanation of Behavior for Interactive Reinforcement Learning Agents
Authors: Yosuke Fukuchi, Masahiko Osawa, Hiroshi Yamakawa, Michita Imai
Date: 2018-10-20 00:00:00
Tasks: BIG-bench Machine Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In cooperation, the workers must know how co-workers behave. However, an
agent's policy, which is embedded in a statistical machine learning model, is
hard to understand, and requires much time and knowledge to comprehend.
Therefore, it is difficult for people to predict the behavior of machine
learning robots, which makes Human Robot Cooperation challenging. In this
paper, we propose Instruction-based Behavior Explanation (IBE), a method to
explain an autonomous agent's future behavior. In IBE, an agent can
autonomously acquire the expressions to explain its own behavior by reusing the
instructions given by a human expert to accelerate the learning of the agent's
policy. IBE also enables a developmental agent, whose policy may change during
the cooperation, to explain its own behavior with sufficient time granularity.
The Frontiers of Fairness in Machine Learning
Authors: Alexandra Chouldechova, Aaron Roth
Date: 2018-10-20 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

The last few years have seen an explosion of academic and popular interest in
algorithmic fairness. Despite this interest and the volume and velocity of work
that has been produced recently, the fundamental science of fairness in machine
learning is still in a nascent state. In March 2018, we convened a group of
experts as part of a CCC visioning workshop to assess the state of the field,
and distill the most promising research directions going forward. This report
summarizes the findings of that workshop. Along the way, it surveys recent
theoretical work in the field and points towards promising directions for
research.
Hierarchical Text Generation using an Outline
Authors: Mehdi Drissi, Olivia Watkins, Jugal Kalita
Date: 2018-10-20 00:00:00
Tasks: Dialogue Generation, speech-recognition, Speech Recognition, Text Generation, Translation

Many challenges in natural language processing require generating text,
including language translation, dialogue generation, and speech recognition.
For all of these problems, text generation becomes more difficult as the text
becomes longer. Current language models often struggle to keep track of
coherence for long pieces of text. Here, we attempt to have the model construct
and use an outline of the text it generates to keep it focused. We find that
the usage of an outline improves perplexity. We do not find that using the
outline improves human evaluation over a simpler baseline, revealing a
discrepancy in perplexity and human perception. Similarly, hierarchical
generation is not found to improve human evaluation scores.
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Authors: S. H. Shabbeer Basha, Soumen Ghosh, Kancharagunta Kishan Babu, Shiv Ram Dubey, Viswanath Pulabaigari, Snehasis Mukherjee
Date: 2018-09-30 00:00:00
Tasks: Classification, General Classification, Medical Image Analysis, Nuclei Classification

Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell nuclei is a challenging task due to the cellular heterogeneity. This paper proposes an efficient Convolutional Neural Network (CNN) based architecture for classification of histological routine colon cancer nuclei named as RCCNet. The main objective of this network is to keep the CNN model as simple as possible. The proposed RCCNet model consists of only 1,512,868 learnable parameters which are significantly less compared to the popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The experiments are conducted over publicly available routine colon cancer histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet model are compared with five state-of-the-art CNN models in terms of the accuracy, weighted average F1 score and training time. The proposed method has achieved a classification accuracy of 80.61% and 0.7887 weighted average F1 score. The proposed RCCNet is more efficient and generalized terms of the training time and data over-fitting, respectively.