pwc-archive/papers-with-abstracts huggingface dataset:
Optimized Preprocessing and Machine Learning for Quantitative Raman Spectroscopy in Biology
Authors: Emily E Storey, Amr S. Helmy
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning

Raman spectroscopy's capability to provide meaningful composition predictions
is heavily reliant on a pre-processing step to remove insignificant spectral
variation. This is crucial in biofluid analysis. Widespread adoption of
diagnostics using Raman requires a robust model which can withstand routine
spectra discrepancies due to unavoidable variations such as age, diet, and
medical background. A wealth of pre-processing methods are available, and it is
often up to trial-and-error or user experience to select the method which gives
the best results. This process can be incredibly time consuming and
inconsistent for multiple operators.
  In this study we detail a method to analyze the statistical variability
within a set of training spectra and determine suitability to form a robust
model. This allows us to selectively qualify or exclude a pre-processing
method, predetermine robustness, and simultaneously identify the number of
components which will form the best predictive model. We demonstrate the
ability of this technique to improve predictive models of two artificial
biological fluids.
  Raman spectroscopy is ideal for noninvasive, nondestructive analysis. Routine
health monitoring which maximizes comfort is increasingly crucial, particularly
in epidemic-level diabetes diagnoses. High variability in spectra of biological
samples can hinder Raman's adoption for these methods. Our technique allows the
decision of optimal pre-treatment method to be determined for the operator;
model performance is no longer a function of user experience. We foresee this
statistical technique being an instrumental element to widening the adoption of
Raman as a monitoring tool in a field of biofluid analysis.
Unpaired Thermal to Visible Spectrum Transfer using Adversarial Training
Authors: Adam Nyberg, Abdelrahman Eldesokey, David Bergström, David Gustafsson
Date: 2019-04-03 00:00:00
Tasks: Generative Adversarial Network

Thermal Infrared (TIR) cameras are gaining popularity in many computer vision
applications due to their ability to operate under low-light conditions. Images
produced by TIR cameras are usually difficult for humans to perceive visually,
which limits their usability. Several methods in the literature were proposed
to address this problem by transforming TIR images into realistic visible
spectrum (VIS) images. However, existing TIR-VIS datasets suffer from imperfect
alignment between TIR-VIS image pairs which degrades the performance of
supervised methods. We tackle this problem by learning this transformation
using an unsupervised Generative Adversarial Network (GAN) which trains on
unpaired TIR and VIS images. When trained and evaluated on KAIST-MS dataset,
our proposed methods was shown to produce significantly more realistic and
sharp VIS images than the existing state-of-the-art supervised methods. In
addition, our proposed method was shown to generalize very well when evaluated
on a new dataset of new environments.
Hyperbolic Image Embeddings
Authors: Valentin Khrulkov, Leyla Mirvakhabova, Evgeniya Ustinova, Ivan Oseledets, Victor Lempitsky
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2020 6
Tasks: Few-Shot Learning, General Classification, image-classification, Image Classification, Image Retrieval, Retrieval

Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity). In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.
Robust Multi-agent Counterfactual Prediction
Authors: Alexander Peysakhovich, Christian Kroer, Adam Lerer
Date: 2019-04-03 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: counterfactual, Prediction, Reinforcement Learning

We consider the problem of using logged data to make predictions about what
would happen if we changed the `rules of the game' in a multi-agent system.
This task is difficult because in many cases we observe actions individuals
take but not their private information or their full reward functions. In
addition, agents are strategic, so when the rules change, they will also change
their actions. Existing methods (e.g. structural estimation, inverse
reinforcement learning) make counterfactual predictions by constructing a model
of the game, adding the assumption that agents' behavior comes from optimizing
given some goals, and then inverting observed actions to learn agent's
underlying utility function (a.k.a. type). Once the agent types are known,
making counterfactual predictions amounts to solving for the equilibrium of the
counterfactual environment. This approach imposes heavy assumptions such as
rationality of the agents being observed, correctness of the analyst's model of
the environment/parametric form of the agents' utility functions, and various
other conditions to make point identification possible. We propose a method for
analyzing the sensitivity of counterfactual conclusions to violations of these
assumptions. We refer to this method as robust multi-agent counterfactual
prediction (RMAC). We apply our technique to investigating the robustness of
counterfactual claims for classic environments in market design: auctions,
school choice, and social choice. Importantly, we show RMAC can be used in
regimes where point identification is impossible (e.g. those which have
multiple equilibria or non-injective maps from type distributions to outcomes).
BERT Post-Training for Review Reading Comprehension and Aspect-based Sentiment Analysis
Authors: Hu Xu, Bing Liu, Lei Shu, Philip S. Yu
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Aspect Extraction, Reading Comprehension, Sentiment Classification

Question-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the recent success of machine reading comprehension (MRC) on formal documents, this paper explores the potential of turning customer reviews into a large source of knowledge that can be exploited to answer user questions.~We call this problem Review Reading Comprehension (RRC). To the best of our knowledge, no existing work has been done on RRC. In this work, we first build an RRC dataset called ReviewRC based on a popular benchmark for aspect-based sentiment analysis. Since ReviewRC has limited training examples for RRC (and also for aspect-based sentiment analysis), we then explore a novel post-training approach on the popular language model BERT to enhance the performance of fine-tuning of BERT for RRC. To show the generality of the approach, the proposed post-training is also applied to some other review-based tasks such as aspect extraction and aspect sentiment classification in aspect-based sentiment analysis. Experimental results demonstrate that the proposed post-training is highly effective. The datasets and code are available at https://www.cs.uic.edu/~hxu/.
The Effect of Downstream Classification Tasks for Evaluating Sentence Embeddings
Authors: Peter Potash
Date: 2019-04-03 00:00:00
Tasks: Classification, General Classification, Sentence, Sentence Embeddings

One popular method for quantitatively evaluating the utility of sentence embeddings involves using them in downstream language processing tasks that require sentence representations as input. One simple such task is classification, where the sentence representations are used to train and test models on several classification datasets. We argue that by evaluating sentence representations in such a manner, the goal of the representations becomes learning a low-dimensional factorization of a sentence-task label matrix. We show how characteristics of this matrix can affect the ability for a low-dimensional factorization to perform as sentence representations in a suite of classification tasks. Primarily, sentences that have more labels across all possible classification tasks have a higher reconstruction loss, however the general nature of this effect is ultimately dependent on the overall distribution of labels across all possible sentences.
Revisiting Visual Grounding
Authors: Erik Conser, Kennedy Hahn, Chandler M. Watson, Melanie Mitchell
Date: 2019-04-03 00:00:00
Proceeding: WS 2019 6
Tasks: Image Retrieval, Retrieval, Visual Grounding

We revisit a particular visual grounding method: the "Image Retrieval Using
Scene Graphs" (IRSG) system of Johnson et al. (2015). Our experiments indicate
that the system does not effectively use its learned object-relationship
models. We also look closely at the IRSG dataset, as well as the widely used
Visual Relationship Dataset (VRD) that is adapted from it. We find that these
datasets exhibit biases that allow methods that ignore relationships to perform
relatively well. We also describe several other problems with the IRSG dataset,
and report on experiments using a subset of the dataset in which the biases and
other problems are removed. Our studies contribute to a more general effort:
that of better understanding what machine learning methods that combine
language and vision actually learn and what popular datasets actually test.
Decomposing Temperature Time Series with Non-Negative Matrix Factorization
Authors: Peter Weiderer, Ana Maria Tomé, Elmar Wolfgang Lang
Date: 2019-04-03 00:00:00
Tasks: Time Series, Time Series Analysis

During the fabrication of casting parts sensor data is typically
automatically recorded and accumulated for process monitoring and defect
diagnosis. As casting is a thermal process with many interacting process
parameters, root cause analysis tends to be tedious and ineffective. We show
how a decomposition based on non-negative matrix factorization (NMF), which is
guided by a knowledge-based initialization strategy, is able to extract
physical meaningful sources from temperature time series collected during a
thermal manufacturing process. The approach assumes the time series to be
generated by a superposition of several simultaneously acting component
processes. NMF is able to reverse the superposition and to identify the hidden
component processes. The latter can be linked to ongoing physical phenomena and
process variables, which cannot be monitored directly. Our approach provides
new insights into the underlying physics and offers a tool, which can assist in
diagnosing defect causes. We demonstrate our method by applying it to real
world data, collected in a foundry during the series production of casting
parts for the automobile industry.
DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation
Authors: Hanchao Li, Pengfei Xiong, Haoqiang Fan, Jian Sun
Date: 2019-04-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Real-Time Semantic Segmentation, Segmentation, Semantic Segmentation, SMAC+

This paper introduces an extremely efficient CNN architecture named DFANet
for semantic segmentation under resource constraints. Our proposed network
starts from a single lightweight backbone and aggregates discriminative
features through sub-network and sub-stage cascade respectively. Based on the
multi-scale feature propagation, DFANet substantially reduces the number of
parameters, but still obtains sufficient receptive field and enhances the model
learning ability, which strikes a balance between the speed and segmentation
performance. Experiments on Cityscapes and CamVid datasets demonstrate the
superior performance of DFANet with 8$\times$ less FLOPs and 2$\times$ faster
than the existing state-of-the-art real-time semantic segmentation methods
while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU
on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on
one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on
a higher resolution image.
The Born Supremacy: Quantum Advantage and Training of an Ising Born Machine
Authors: Brian Coyle, Daniel Mills, Vincent Danos, Elham Kashefi
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning

The search for an application of near-term quantum devices is widespread. Quantum Machine Learning is touted as a potential utilisation of such devices, particularly those which are out of the reach of the simulation capabilities of classical computers. In this work, we propose a generative Quantum Machine Learning Model, called the Ising Born Machine (IBM), which we show cannot, in the worst case, and up to suitable notions of error, be simulated efficiently by a classical device. We also show this holds for all the circuit families encountered during training. In particular, we explore quantum circuit learning using non-universal circuits derived from Ising Model Hamiltonians, which are implementable on near term quantum devices. We propose two novel training methods for the IBM by utilising the Stein Discrepancy and the Sinkhorn Divergence cost functions. We show numerically, both using a simulator within Rigetti's Forest platform and on the Aspen-1 16Q chip, that the cost functions we suggest outperform the more commonly used Maximum Mean Discrepancy (MMD) for differentiable training. We also propose an improvement to the MMD by proposing a novel utilisation of quantum kernels which we demonstrate provides improvements over its classical counterpart. We discuss the potential of these methods to learn `hard' quantum distributions, a feat which would demonstrate the advantage of quantum over classical computers, and provide the first formal definitions for what we call `Quantum Learning Supremacy'. Finally, we propose a novel view on the area of quantum circuit compilation by using the IBM to `mimic' target quantum circuits using classical output data only.
Massively Multilingual Adversarial Speech Recognition
Authors: Oliver Adams, Matthew Wiesner, Shinji Watanabe, David Yarowsky
Date: 2019-04-03 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, speech-recognition, Speech Recognition

We report on adaptation of multilingual end-to-end speech recognition models
trained on as many as 100 languages. Our findings shed light on the relative
importance of similarity between the target and pretraining languages along the
dimensions of phonetics, phonology, language family, geographical location, and
orthography. In this context, experiments demonstrate the effectiveness of two
additional pretraining objectives in encouraging language-independent encoder
representations: a context-independent phoneme objective paired with a
language-adversarial classification objective.
Jointly Pre-training with Supervised, Autoencoder, and Value Losses for Deep Reinforcement Learning
Authors: Gabriel V. de la Cruz Jr., Yunshu Du, Matthew E. Taylor
Date: 2019-04-03 00:00:00
Tasks: Atari Games, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep Reinforcement Learning (DRL) algorithms are known to be data
inefficient. One reason is that a DRL agent learns both the feature and the
policy tabula rasa. Integrating prior knowledge into DRL algorithms is one way
to improve learning efficiency since it helps to build helpful representations.
In this work, we consider incorporating human knowledge to accelerate the
asynchronous advantage actor-critic (A3C) algorithm by pre-training a small
amount of non-expert human demonstrations. We leverage the supervised
autoencoder framework and propose a novel pre-training strategy that jointly
trains a weighted supervised classification loss, an unsupervised
reconstruction loss, and an expected return loss. The resulting pre-trained
model learns more useful features compared to independently training in
supervised or unsupervised fashion. Our pre-training method drastically
improved the learning performance of the A3C agent in Atari games of Pong and
MsPacman, exceeding the performance of the state-of-the-art algorithms at a
much smaller number of game interactions. Our method is light-weight and easy
to implement in a single machine. For reproducibility, our code is available at
github.com/gabrieledcjr/DeepRL/tree/A3C-ALA2019
Progressive Stochastic Binarization of Deep Networks
Authors: David Hartmann, Michael Wand
Date: 2019-04-03 00:00:00
Tasks: Binarization, Network Pruning, Quantization

A plethora of recent research has focused on improving the memory footprint
and inference speed of deep networks by reducing the complexity of (i)
numerical representations (for example, by deterministic or stochastic
quantization) and (ii) arithmetic operations (for example, by binarization of
weights).
  We propose a stochastic binarization scheme for deep networks that allows for
efficient inference on hardware by restricting itself to additions of small
integers and fixed shifts. Unlike previous approaches, the underlying
randomized approximation is progressive, thus permitting an adaptive control of
the accuracy of each operation at run-time. In a low-precision setting, we
match the accuracy of previous binarized approaches. Our representation is
unbiased - it approaches continuous computation with increasing sample size. In
a high-precision regime, the computational costs are competitive with previous
quantization schemes. Progressive stochastic binarization also permits
localized, dynamic accuracy control within a single network, thereby providing
a new tool for adaptively focusing computational attention.
  We evaluate our method on networks of various architectures, already
pretrained on ImageNet. With representational costs comparable to previous
schemes, we obtain accuracies close to the original floating point
implementation. This includes pruned networks, except the known special case of
certain types of separated convolutions. By focusing computational attention
using progressive sampling, we reduce inference costs on ImageNet further by a
factor of up to 33% (before network pruning).
Linearly Converging Quasi Branch and Bound Algorithms for Global Rigid Registration
Authors: Nadav Dym, Shahar Ziv Kovalsky
Date: 2019-04-03 00:00:00
Proceeding: ICCV 2019 10

In recent years, several branch-and-bound (BnB) algorithms have been proposed
to globally optimize rigid registration problems. In this paper, we suggest a
general framework to improve upon the BnB approach, which we name Quasi BnB.
Quasi BnB replaces the linear lower bounds used in BnB algorithms with
quadratic quasi-lower bounds which are based on the quadratic behavior of the
energy in the vicinity of the global minimum. While quasi-lower bounds are not
truly lower bounds, the Quasi-BnB algorithm is globally optimal. In fact we
prove that it exhibits linear convergence -- it achieves $\epsilon$-accuracy in
$~O(\log(1/\epsilon)) $ time while the time complexity of other rigid
registration BnB algorithms is polynomial in $1/\epsilon $. Our experiments
verify that Quasi-BnB is significantly more efficient than state-of-the-art BnB
algorithms, especially for problems where high accuracy is desired.
Semantics-Aware Image to Image Translation and Domain Transfer
Authors: Pravakar Roy, Nicolai Häni, Jun-Jee Chao, Volkan Isler
Date: 2019-04-03 00:00:00
Tasks: Decoder, Domain Adaptation, Image-to-Image Translation, Object, Translation, Unsupervised Image-To-Image Translation

Image to image translation is the problem of transferring an image from a source domain to a different (but related) target domain. We present a new unsupervised image to image translation technique that leverages the underlying semantic information for object transfiguration and domain transfer tasks. Specifically, we present a generative adversarial learning approach that jointly translates images and labels from a source domain to a target domain. Our main technical contribution is an encoder-decoder based network architecture that jointly encodes the image and its underlying semantics and translates both individually to the target domain. Additionally, we propose object transfiguration and cross-domain semantic consistency losses that preserve semantic labels. Through extensive experimental evaluation, we demonstrate the effectiveness of our approach as compared to the state-of-the-art methods on unsupervised image-to-image translation, domain adaptation, and object transfiguration.
PaintBot: A Reinforcement Learning Approach for Natural Media Painting
Authors: Biao Jia, Chen Fang, Jonathan Brandt, Byungmoon Kim, Dinesh Manocha
Date: 2019-04-03 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a new automated digital painting framework, based on a painting
agent trained through reinforcement learning. To synthesize an image, the agent
selects a sequence of continuous-valued actions representing primitive painting
strokes, which are accumulated on a digital canvas. Action selection is guided
by a given reference image, which the agent attempts to replicate subject to
the limitations of the action space and the agent's learned policy. The
painting agent policy is determined using a variant of proximal policy
optimization reinforcement learning. During training, our agent is presented
with patches sampled from an ensemble of reference images. To accelerate
training convergence, we adopt a curriculum learning strategy, whereby
reference patches are sampled according to how challenging they are using the
current policy. We experiment with differing loss functions, including
pixel-wise and perceptual loss, which have consequent differing effects on the
learned policy. We demonstrate that our painting agent can learn an effective
policy with a high dimensional continuous action space comprising pen pressure,
width, tilt, and color, for a variety of painting styles. Through a
coarse-to-fine refinement process our agent can paint arbitrarily complex
images in the desired style.
Differentially Private Model Publishing for Deep Learning
Authors: Lei Yu, Ling Liu, Calton Pu, Mehmet Emre Gursoy, Stacey Truex
Date: 2019-04-03 00:00:00
Tasks: Deep Learning, model

Deep learning techniques based on neural networks have shown significant success in a wide range of AI tasks. Large-scale training datasets are one of the critical factors for their success. However, when the training datasets are crowdsourced from individuals and contain sensitive information, the model parameters may encode private information and bear the risks of privacy leakage. The recent growing trend of the sharing and publishing of pre-trained models further aggravates such privacy risks. To tackle this problem, we propose a differentially private approach for training neural networks. Our approach includes several new techniques for optimizing both privacy loss and model accuracy. We employ a generalization of differential privacy called concentrated differential privacy(CDP), with both a formal and refined privacy loss analysis on two different data batching methods. We implement a dynamic privacy budget allocator over the course of training to improve model accuracy. Extensive experiments demonstrate that our approach effectively improves privacy loss accounting, training efficiency and model quality under a given privacy budget.