pwc-archive/papers-with-abstracts huggingface dataset:
Deep Policies for Width-Based Planning in Pixel Domains
Authors: Miquel Junyent, Anders Jonsson, Vicenç Gómez
Date: 2019-04-12 00:00:00
Tasks: Atari Games

Width-based planning has demonstrated great success in recent years due to its ability to scale independently of the size of the state space. For example, Bandres et al. (2018) introduced a rollout version of the Iterated Width algorithm whose performance compares well with humans and learning methods in the pixel setting of the Atari games suite. In this setting, planning is done on-line using the "screen" states and selecting actions by looking ahead into the future. However, this algorithm is purely exploratory and does not leverage past reward information. Furthermore, it requires the state to be factored into features that need to be pre-defined for the particular task, e.g., the B-PROST pixel features. In this work, we extend width-based planning by incorporating an explicit policy in the action selection mechanism. Our method, called $\pi$-IW, interleaves width-based planning and policy learning using the state-actions visited by the planner. The policy estimate takes the form of a neural network and is in turn used to guide the planning step, thus reinforcing promising paths. Surprisingly, we observe that the representation learned by the neural network can be used as a feature space for the width-based planner without degrading its performance, thus removing the requirement of pre-defined features for the planner. We compare $\pi$-IW with previous width-based methods and with AlphaZero, a method that also interleaves planning and learning, in simple environments, and show that $\pi$-IW has superior performance. We also show that $\pi$-IW algorithm outperforms previous width-based methods in the pixel setting of Atari games suite.
MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Authors: Juan Liu, Kevin M. Koch
Date: 2019-04-11 00:00:00

Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer
estimates of local tissue magnetism (magnetic susceptibility), which has been
shown useful to provide novel image contrast and as biomarkers of abnormal
tissue. QSM requires addressing a challenging post-processing problem:
filtering of image phase estimates and inversion of the phase to susceptibility
relationship. A wide variety of quantification errors, robustness limitations,
and artifacts plague QSM algorithms. To overcome these limitations, a robust
deep-learning-based single-step QSM reconstruction approach is proposed and
demonstrated. This neural network was trained using magnetostatic physics
simulations based on in-vivo data sources. Random perturbations were added to
the physics simulations to provide sufficient input-label pairs for the
training purposes. The network was quantitatively tested using gold-standard
in-silico labeled datasets against established QSM total field inversion
approaches. In addition, the algorithm was applied to susceptibility-weighted
imaging (SWI) data collected on a cohort of clinical subjects with brain
hemmhorage. When quantitatively compared against gold-standard in-silico
labels, the proposed algorithm outperformed the existing comparable approaches.
High quality QSM were consistently estimated from clinical
susceptibility-weighted data on 100 subjects without any noticeable inversion
failures. The proposed approach was able to robustly generate high quality QSM
with improved accuracy in in-silico gold-standard experiments. QSM produced by
the proposed method can be generated in real-time on existing MRI scanner
platforms and provide enhanced visualization and quantification of
magnetism-based tissue contrasts.
PerfVis: Pervasive Visualization in Immersive AugmentedReality for Performance Awareness
Authors: Leonel Merino, Mario Hess, Alexandre Bergel, Oscar Nierstrasz, Daniel Weiskopf
Date: 2019-04-05 00:00:00

Developers are usually unaware of the impact of code changes to the
performance of software systems. Although developers can analyze the
performance of a system by executing, for instance, a performance test to
compare the performance of two consecutive versions of the system, changing
from a programming task to a testing task would disrupt the development flow.
In this paper, we propose the use of a city visualization that dynamically
provides developers with a pervasive view of the continuous performance of a
system. We use an immersive augmented reality device (Microsoft HoloLens) to
display our visualization and extend the integrated development environment on
a computer screen to use the physical space. We report on technical details of
the design and implementation of our visualization tool, and discuss early
feedback that we collected of its usability. Our investigation explores a new
visual metaphor to support the exploration and analysis of possibly very large
and multidimensional performance data. Our initial result indicates that the
city metaphor can be adequate to analyze dynamic performance data on a large
and non-trivial software system.
Visualization of Labeled Mixed-featured Datasets
Authors: Yifan Zhu, Fan Dai, Ranjan Maitra
Date: 2019-04-06 00:00:00

We develop methodology for visualization of labeled mixed-featured datasets. We first investigate datasets with continuous features where our Max-Ratio Projection (MRP) method utilizes the group information in high dimensions to provide distinctive lower-dimensional projections that are then displayed using Radviz3D. Our methodology is extended to datasets with discrete and continuous features where a Gaussianized distributional transform is used in conjunction with copula models before applying MRP and visualizing the result using RadViz3D. A R package $radviz3d$ implementing our complete methodology is available.
Parallel Hardware for Faster Morphological Analysis
Authors: Issam Damaj, Mahmoud Imdoukh, Rached Zantout
Date: 2019-04-09 00:00:00
Tasks: Morphological Analysis

Morphological analysis in the Arabic language is computationally intensive,
has numerous forms and rules, and is intrinsically parallel. The investigation
presented in this paper confirms that the effective development of parallel
algorithms and the derivation of corresponding processors in hardware enable
implementations with appealing performance characteristics. The presented
developments of parallel hardware comprise the application of a variety of
algorithm modelling techniques, strategies for concurrent processing, and the
creation of pioneering hardware implementations that target modern programmable
devices. The investigation includes the creation of a linguistic-based stemmer
for Arabic verb root extraction with extended infix processing to attain
high-levels of accuracy. The implementations comprise three versions, namely,
software, non-pipelined processor, and pipelined processor with high
throughput. The targeted systems are high-performance multi-core processors for
software implementations and high-end Field Programmable Gate Array systems for
hardware implementations. The investigation includes a thorough evaluation of
the methodology, and performance and accuracy analyses of the developed
software and hardware implementations. The pipelined processor achieved a
significant speedup of 5571.4 over the software implementation. The developed
stemmer for verb root extraction with infix processing attained accuracies of
87% and 90.7% for analyzing the texts of the Holy Quran and its Chapter 29 -
Surat Al-Ankabut.
Beyond the Nucleus: Cytoplasmic Dominance in Follicular Thyroid Carcinoma Detection Using Single-Cell Raman Imaging Across Multiple Devices
Authors: Aurelien Pelissier, Kosuke Hashimoto, Kentaro Mochizuki, J. Nicholas Taylor, Jean-Emmanuel Clement, Yasuaki Kumamoto, Katsumasa Fujita, Yoshinori Harada, Tamiki Komatsuzaki
Date: 2019-04-11 00:00:00
Tasks: Diagnostic, Dimensionality Reduction, feature selection

Cytological diagnosis of follicular thyroid carcinoma (FTC) is one of major challenges in the field of endocrine oncology due to absence of evident morphological indicators. Morphological abnormalities in the nucleus are typically key indicators in cancer cytopathology and are attributed to a range of biochemical alterations in nuclear components. Consequently, Raman spectroscopy has been widely used to detect cancer in various cytological samples, often identifying biochemical changes prior to observable morphological alterations. However, in the case of FTC, cytoplasmic features such as carotenoids, cytochromes, and lipid droplets have shown greater diagnostic relevance compared to nuclear features. This study leverages single-cell Raman imaging to explore the spatial origin of diagnostic signals in FTC and normal thyroid (NT) cells, assessing the contributions of the nucleus and cytoplasm independently. Our results demonstrate that Raman spectra from the cytoplasmic region can distinguish between FTC and NT cells with an accuracy of 84% under co-culture conditions, maintaining robustness across multiple devices. In contrast, classification based on nuclear spectra achieved only 53% accuracy, suggesting that biochemical alterations in the cytoplasm play a more significant role in FTC detection than those in the nucleus. Our work elevates the promise of Raman-based cytopathology by providing complementary organelle-dependent information to traditional diagnostic methods and demonstrating transferability across different devices.
End-to-End Robotic Reinforcement Learning without Reward Engineering
Authors: Avi Singh, Larry Yang, Kristian Hartikainen, Chelsea Finn, Sergey Levine
Date: 2019-04-16 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The combination of deep neural network models and reinforcement learning algorithms can make it possible to learn policies for robotic behaviors that directly read in raw sensory inputs, such as camera images, effectively subsuming both estimation and control into one model. However, real-world applications of reinforcement learning must specify the goal of the task by means of a manually programmed reward function, which in practice requires either designing the very same perception pipeline that end-to-end reinforcement learning promises to avoid, or else instrumenting the environment with additional sensors to determine if the task has been performed successfully. In this paper, we propose an approach for removing the need for manual engineering of reward specifications by enabling a robot to learn from a modest number of examples of successful outcomes, followed by actively solicited queries, where the robot shows the user a state and asks for a label to determine whether that state represents successful completion of the task. While requesting labels for every single state would amount to asking the user to manually provide the reward signal, our method requires labels for only a tiny fraction of the states seen during training, making it an efficient and practical approach for learning skills without manually engineered rewards. We evaluate our method on real-world robotic manipulation tasks where the observations consist of images viewed by the robot's camera. In our experiments, our method effectively learns to arrange objects, place books, and drape cloth, directly from images and without any manually specified reward functions, and with only 1-4 hours of interaction with the real world.
Matrix and tensor decompositions for training binary neural networks
Authors: Adrian Bulat, Jean Kossaifi, Georgios Tzimiropoulos, Maja Pantic
Date: 2019-04-16 00:00:00
Tasks: Binarization, Classification with Binary Neural Network, Model Compression, Pose Estimation, Quantization, Tensor Decomposition

This paper is on improving the training of binary neural networks in which
both activations and weights are binary. While prior methods for neural network
binarization binarize each filter independently, we propose to instead
parametrize the weight tensor of each layer using matrix or tensor
decomposition. The binarization process is then performed using this latent
parametrization, via a quantization function (e.g. sign function) applied to
the reconstructed weights. A key feature of our method is that while the
reconstruction is binarized, the computation in the latent factorized space is
done in the real domain. This has several advantages: (i) the latent
factorization enforces a coupling of the filters before binarization, which
significantly improves the accuracy of the trained models. (ii) while at
training time, the binary weights of each convolutional layer are parametrized
using real-valued matrix or tensor decomposition, during inference we simply
use the reconstructed (binary) weights. As a result, our method does not
sacrifice any advantage of binary networks in terms of model compression and
speeding-up inference. As a further contribution, instead of computing the
binary weight scaling factors analytically, as in prior work, we propose to
learn them discriminatively via back-propagation. Finally, we show that our
approach significantly outperforms existing methods when tested on the
challenging tasks of (a) human pose estimation (more than 4% improvements) and
(b) ImageNet classification (up to 5% performance gains).
Objects as Points
Authors: Xingyi Zhou, Dequan Wang, Philipp Krähenbühl
Date: 2019-04-16 00:00:00
Tasks: 3D Object Detection, Keypoint Detection, Keypoint Estimation, Object, Object Detection, One-stage Anchor-free Oriented Object Detection, Real-Time Object Detection

Detection identifies objects as axis-aligned boxes in an image. Most
successful object detectors enumerate a nearly exhaustive list of potential
object locations and classify each. This is wasteful, inefficient, and requires
additional post-processing. In this paper, we take a different approach. We
model an object as a single point --- the center point of its bounding box. Our
detector uses keypoint estimation to find center points and regresses to all
other object properties, such as size, 3D location, orientation, and even pose.
Our center point based approach, CenterNet, is end-to-end differentiable,
simpler, faster, and more accurate than corresponding bounding box based
detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO
dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with
multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D
bounding box in the KITTI benchmark and human pose on the COCO keypoint
dataset. Our method performs competitively with sophisticated multi-stage
methods and runs in real-time.
Active Adversarial Domain Adaptation
Authors: Jong-Chyi Su, Yi-Hsuan Tsai, Kihyuk Sohn, Buyu Liu, Subhransu Maji, Manmohan Chandraker
Date: 2019-04-16 00:00:00
Tasks: Active Learning, Diversity, Domain Adaptation, object-detection, Object Detection, Transfer Learning

We propose an active learning approach for transferring representations across domains. Our approach, active adversarial domain adaptation (AADA), explores a duality between two related problems: adversarial domain alignment and importance sampling for adapting models across domains. The former uses a domain discriminative model to align domains, while the latter utilizes it to weigh samples to account for distribution shifts. Specifically, our importance weight promotes samples with large uncertainty in classification and diversity from labeled examples, thus serves as a sample selection scheme for active learning. We show that these two views can be unified in one framework for domain adaptation and transfer learning when the source domain has many labeled examples while the target domain does not. AADA provides significant improvements over fine-tuning based approaches and other sampling methods when the two domains are closely related. Results on challenging domain adaptation tasks, e.g., object detection, demonstrate that the advantage over baseline approaches is retained even after hundreds of examples being actively annotated.
Temporal Cycle-Consistency Learning
Authors: Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman
Date: 2019-04-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: Anomaly Detection, Representation Learning, Self-Supervised Learning, Video Alignment

We introduce a self-supervised representation learning method based on the
task of temporal alignment between videos. The method trains a network using
temporal cycle consistency (TCC), a differentiable cycle-consistency loss that
can be used to find correspondences across time in multiple videos. The
resulting per-frame embeddings can be used to align videos by simply matching
frames using the nearest-neighbors in the learned embedding space.
  To evaluate the power of the embeddings, we densely label the Pouring and
Penn Action video datasets for action phases. We show that (i) the learned
embeddings enable few-shot classification of these action phases, significantly
reducing the supervised training requirements; and (ii) TCC is complementary to
other methods of self-supervised learning in videos, such as Shuffle and Learn
and Time-Contrastive Networks. The embeddings are also used for a number of
applications based on alignment (dense temporal correspondence) between video
pairs, including transfer of metadata of synchronized modalities between videos
(sounds, temporal semantic labels), synchronized playback of multiple videos,
and anomaly detection. Project webpage:
https://sites.google.com/view/temporal-cycle-consistency .
Improved Speech Separation with Time-and-Frequency Cross-domain Joint Embedding and Clustering
Authors: Gene-Ping Yang, Chao-I Tuan, Hung-Yi Lee, Lin-shan Lee
Date: 2019-04-16 00:00:00
Tasks: Clustering, Speech Separation

Speech separation has been very successful with deep learning techniques.
Substantial effort has been reported based on approaches over spectrogram,
which is well known as the standard time-and-frequency cross-domain
representation for speech signals. It is highly correlated to the phonetic
structure of speech, or "how the speech sounds" when perceived by human, but
primarily frequency domain features carrying temporal behaviour. Very
impressive work achieving speech separation over time domain was reported
recently, probably because waveforms in time domain may describe the different
realizations of speech in a more precise way than spectrogram. In this paper,
we propose a framework properly integrating the above two directions, hoping to
achieve both purposes. We construct a time-and-frequency feature map by
concatenating the 1-dim convolution encoded feature map (for time domain) and
the spectrogram (for frequency domain), which was then processed by an
embedding network and clustering approaches very similar to those used in time
and frequency domain prior works. In this way, the information in the time and
frequency domains, as well as the interactions between them, can be jointly
considered during embedding and clustering. Very encouraging results
(state-of-the-art to our knowledge) were obtained with WSJ0-2mix dataset in
preliminary experiments.
UTFPR at SemEval-2019 Task 5: Hate Speech Identification with Recurrent Neural Networks
Authors: Gustavo Henrique Paetzold, Shervin Malmasi, Marcos Zampieri
Date: 2019-04-16 00:00:00
Proceeding: SEMEVAL 2019 6

In this paper we revisit the problem of automatically identifying hate speech
in posts from social media. We approach the task using a system based on
minimalistic compositional Recurrent Neural Networks (RNN). We tested our
approach on the SemEval-2019 Task 5: Multilingual Detection of Hate Speech
Against Immigrants and Women in Twitter (HatEval) shared task dataset. The
dataset made available by the HatEval organizers contained English and Spanish
posts retrieved from Twitter annotated with respect to the presence of hateful
content and its target. In this paper we present the results obtained by our
system in comparison to the other entries in the shared task. Our system
achieved competitive performance ranking 7th in sub-task A out of 62 systems in
the English track.
Double Transfer Learning for Breast Cancer Histopathologic Image Classification
Authors: Jonathan de Matos, Alceu de S. Britto Jr., Luiz E. S. Oliveira, Alessandro L. Koerich
Date: 2019-04-16 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Transfer Learning

This work proposes a classification approach for breast cancer
histopathologic images (HI) that uses transfer learning to extract features
from HI using an Inception-v3 CNN pre-trained with ImageNet dataset. We also
use transfer learning on training a support vector machine (SVM) classifier on
a tissue labeled colorectal cancer dataset aiming to filter the patches from a
breast cancer HI and remove the irrelevant ones. We show that removing
irrelevant patches before training a second SVM classifier, improves the
accuracy for classifying malign and benign tumors on breast cancer images. We
are able to improve the classification accuracy in 3.7% using the feature
extraction transfer learning and an additional 0.7% using the irrelevant patch
elimination. The proposed approach outperforms the state-of-the-art in three
out of the four magnification factors of the breast cancer dataset.
Scalable and Efficient Hypothesis Testing with Random Forests
Authors: Tim Coleman, Wei Peng, Lucas Mentch
Date: 2019-04-16 00:00:00
Tasks: Two-sample testing

Throughout the last decade, random forests have established themselves as among the most accurate and popular supervised learning methods. While their black-box nature has made their mathematical analysis difficult, recent work has established important statistical properties like consistency and asymptotic normality by considering subsampling in lieu of bootstrapping. Though such results open the door to traditional inference procedures, all formal methods suggested thus far place severe restrictions on the testing framework and their computational overhead precludes their practical scientific use. Here we propose a permutation-style testing approach to formally assess feature significance. We establish asymptotic validity of the test via exchangeability arguments and show that the test maintains high power with orders of magnitude fewer computations. As importantly, the procedure scales easily to big data settings where large training and testing sets may be employed without the need to construct additional models. Simulations and applications to ecological data where random forests have recently shown promise are provided.
Unsupervised Discovery of Multimodal Links in Multi-image, Multi-sentence Documents
Authors: Jack Hessel, Lillian Lee, David Mimno
Date: 2019-04-16 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Sentence

Images and text co-occur constantly on the web, but explicit links between images and sentences (or other intra-document textual units) are often not present. We present algorithms that discover image-sentence relationships without relying on explicit multimodal annotation in training. We experiment on seven datasets of varying difficulty, ranging from documents consisting of groups of images captioned post hoc by crowdworkers to naturally-occurring user-generated multimodal documents. We find that a structured training objective based on identifying whether collections of images and sentences co-occur in documents can suffice to predict links between specific sentences and specific images within the same document at test time.