pwc-archive/papers-with-abstracts huggingface dataset:
Automatic microscopic cell counting by use of deeply-supervised density regression model
Authors: Shenghua He, Kyaw Thu Minn, Lilianna Solnica-Krezel, Mark Anastasio, Hua Li
Date: 2019-03-04 00:00:00
Tasks: Automatic Cell Counting, regression

Accurately counting cells in microscopic images is important for medical
diagnoses and biological studies, but manual cell counting is very tedious,
time-consuming, and prone to subjective errors, and automatic counting can be
less accurate than desired. To improve the accuracy of automatic cell counting,
we propose here a novel method that employs deeply-supervised density
regression. A fully convolutional neural network (FCNN) serves as the primary
FCNN for density map regression. Innovatively, a set of auxiliary FCNNs are
employed to provide additional supervision for learning the intermediate layers
of the primary CNN to improve network performance. In addition, the primary CNN
is designed as a concatenating framework to integrate multi-scale features
through shortcut connections in the network, which improves the granularity of
the features extracted from the intermediate CNN layers and further supports
the final density map estimation.
Stochastic Online Learning with Probabilistic Graph Feedback
Authors: Shuai Li, Wei Chen, Zheng Wen, Kwong-Sak Leung
Date: 2019-03-04 00:00:00

We consider a problem of stochastic online learning with general probabilistic graph feedback, where each directed edge in the feedback graph has probability $p_{ij}$. Two cases are covered. (a) The one-step case, where after playing arm $i$ the learner observes a sample reward feedback of arm $j$ with independent probability $p_{ij}$. (b) The cascade case where after playing arm $i$ the learner observes feedback of all arms $j$ in a probabilistic cascade starting from $i$ -- for each $(i,j)$ with probability $p_{ij}$, if arm $i$ is played or observed, then a reward sample of arm $j$ would be observed with independent probability $p_{ij}$. Previous works mainly focus on deterministic graphs which corresponds to one-step case with $p_{ij} \in \{0,1\}$, an adversarial sequence of graphs with certain topology guarantees, or a specific type of random graphs. We analyze the asymptotic lower bounds and design algorithms in both cases. The regret upper bounds of the algorithms match the lower bounds with high probability.
From Knowledge Map to Mind Map: Artificial Imagination
Authors: Ruixue Liu, Baoyang Chen, XIAOYU GUO, Yan Dai, Meng Chen, Zhijie Qiu, Xiaodong He
Date: 2019-03-04 00:00:00

Imagination is one of the most important factors which makes an artistic
painting unique and impressive. With the rapid development of Artificial
Intelligence, more and more researchers try to create painting with AI
technology automatically. However, lacking of imagination is still a main
problem for AI painting. In this paper, we propose a novel approach to inject
rich imagination into a special painting art Mind Map creation. We firstly
consider lexical and phonological similarities of seed word, then learn and
inherit original painting style of the author, and finally apply Dadaism and
impossibility of improvisation principles into painting process. We also design
several metrics for imagination evaluation. Experimental results show that our
proposed method can increase imagination of painting and also improve its
overall quality.
Unsupervised Cross-spectral Stereo Matching by Learning to Synthesize
Authors: Mingyang Liang, Xiaoyang Guo, Hongsheng Li, Xiaogang Wang, You Song
Date: 2019-03-04 00:00:00
Tasks: Image-to-Image Translation, Stereo Matching, Stereo Matching Hand, Translation

Unsupervised cross-spectral stereo matching aims at recovering disparity
given cross-spectral image pairs without any supervision in the form of ground
truth disparity or depth. The estimated depth provides additional information
complementary to individual semantic features, which can be helpful for other
vision tasks such as tracking, recognition and detection. However, there are
large appearance variations between images from different spectral bands, which
is a challenge for cross-spectral stereo matching. Existing deep unsupervised
stereo matching methods are sensitive to the appearance variations and do not
perform well on cross-spectral data. We propose a novel unsupervised
cross-spectral stereo matching framework based on image-to-image translation.
First, a style adaptation network transforms images across different spectral
bands by cycle consistency and adversarial learning, during which appearance
variations are minimized. Then, a stereo matching network is trained with image
pairs from the same spectra using view reconstruction loss. At last, the
estimated disparity is utilized to supervise the spectral-translation network
in an end-to-end way. Moreover, a novel style adaptation network F-cycleGAN is
proposed to improve the robustness of spectral translation. Our method can
tackle appearance variations and enhance the robustness of unsupervised
cross-spectral stereo matching. Experimental results show that our method
achieves good performance without using depth supervision or explicit semantic
information.
COMIC: Towards A Compact Image Captioning Model with Attention
Authors: Jia Huei Tan, Chee Seng Chan, Joon Huang Chuah
Date: 2019-03-04 00:00:00
Tasks: Decoder, Image Captioning

Recent works in image captioning have shown very promising raw performance. However, we realize that most of these encoder-decoder style networks with attention do not scale naturally to large vocabulary size, making them difficult to be deployed on embedded system with limited hardware resources. This is because the size of word and output embedding matrices grow proportionally with the size of vocabulary, adversely affecting the compactness of these networks. To address this limitation, this paper introduces a brand new idea in the domain of image captioning. That is, we tackle the problem of compactness of image captioning models which is hitherto unexplored. We showed that, our proposed model, named COMIC for COMpact Image Captioning, achieves comparable results in five common evaluation metrics with state-of-the-art approaches on both MS-COCO and InstaPIC-1.1M datasets despite having an embedding vocabulary size that is 39x - 99x smaller. The source code and models are available at: https://github.com/jiahuei/COMIC-Compact-Image-Captioning-with-Attention
Neural Networks Trained on Natural Scenes Exhibit Gestalt Closure
Authors: Been Kim, Emily Reif, Martin Wattenberg, Samy Bengio, Michael C. Mozer
Date: 2019-03-04 00:00:00
Tasks: Image Classification

The Gestalt laws of perceptual organization, which describe how visual elements in an image are grouped and interpreted, have traditionally been thought of as innate despite their ecological validity. We use deep-learning methods to investigate whether natural scene statistics might be sufficient to derive the Gestalt laws. We examine the law of closure, which asserts that human visual perception tends to "close the gap" by assembling elements that can jointly be interpreted as a complete figure or object. We demonstrate that a state-of-the-art convolutional neural network, trained to classify natural images, exhibits closure on synthetic displays of edge fragments, as assessed by similarity of internal representations. This finding provides support for the hypothesis that the human perceptual system is even more elegant than the Gestaltists imagined: a single law---adaptation to the statistical structure of the environment---might suffice as fundamental.
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
Authors: Antoni Rosinol, Torsten Sattler, Marc Pollefeys, Luca Carlone
Date: 2019-03-04 00:00:00
Tasks: 3D Reconstruction, Simultaneous Localization and Mapping, State Estimation

Visual-Inertial Odometry (VIO) algorithms typically rely on a point cloud representation of the scene that does not model the topology of the environment. A 3D mesh instead offers a richer, yet lightweight, model. Nevertheless, building a 3D mesh out of the sparse and noisy 3D landmarks triangulated by a VIO algorithm often results in a mesh that does not fit the real scene. In order to regularize the mesh, previous approaches decouple state estimation from the 3D mesh regularization step, and either limit the 3D mesh to the current frame or let the mesh grow indefinitely. We propose instead to tightly couple mesh regularization and state estimation by detecting and enforcing structural regularities in a novel factor-graph formulation. We also propose to incrementally build the mesh by restricting its extent to the time-horizon of the VIO optimization; the resulting 3D mesh covers a larger portion of the scene than a per-frame approach while its memory usage and computational complexity remain bounded. We show that our approach successfully regularizes the mesh, while improving localization accuracy, when structural regularities are present, and remains operational in scenes without regularities.
NoRML: No-Reward Meta Learning
Authors: Yuxiang Yang, Ken Caluwaerts, Atil Iscen, Jie Tan, Chelsea Finn
Date: 2019-03-04 00:00:00
Tasks: Meta-Learning, Reinforcement Learning, Reinforcement Learning (RL)

Efficiently adapting to new environments and changes in dynamics is critical
for agents to successfully operate in the real world. Reinforcement learning
(RL) based approaches typically rely on external reward feedback for
adaptation. However, in many scenarios this reward signal might not be readily
available for the target task, or the difference between the environments can
be implicit and only observable from the dynamics. To this end, we introduce a
method that allows for self-adaptation of learned policies: No-Reward Meta
Learning (NoRML). NoRML extends Model Agnostic Meta Learning (MAML) for RL and
uses observable dynamics of the environment instead of an explicit reward
function in MAML's finetune step. Our method has a more expressive update step
than MAML, while maintaining MAML's gradient based foundation. Additionally, in
order to allow more targeted exploration, we implement an extension to MAML
that effectively disconnects the meta-policy parameters from the fine-tuned
policies' parameters. We first study our method on a number of synthetic
control problems and then validate our method on common benchmark environments,
showing that NoRML outperforms MAML when the dynamics change between tasks.
Learning low-precision neural networks without Straight-Through Estimator(STE)
Authors: Zhi-Gang Liu, Matthew Mattina
Date: 2019-03-04 00:00:00
Tasks: Quantization

The Straight-Through Estimator (STE) is widely used for back-propagating gradients through the quantization function, but the STE technique lacks a complete theoretical understanding. We propose an alternative methodology called alpha-blending (AB), which quantizes neural networks to low-precision using stochastic gradient descent (SGD). Our method (AB) avoids STE approximation by replacing the quantized weight in the loss function by an affine combination of the quantized weight w_q and the corresponding full-precision weight w with non-trainable scalar coefficient $\alpha$ and $1-\alpha$. During training, $\alpha$ is gradually increased from 0 to 1; the gradient updates to the weights are through the full-precision term, $(1-\alpha)w$, of the affine combination; the model is converted from full-precision to low-precision progressively. To evaluate the method, a 1-bit BinaryNet on CIFAR10 dataset and 8-bits, 4-bits MobileNet v1, ResNet_50 v1/2 on ImageNet dataset are trained using the alpha-blending approach, and the evaluation indicates that AB improves top-1 accuracy by 0.9%, 0.82% and 2.93% respectively compared to the results of STE based quantization.
Ultra-Scalable Spectral Clustering and Ensemble Clustering
Authors: Dong Huang, Chang-Dong Wang, Jian-Sheng Wu, Jian-Huang Lai, Chee-Keong Kwoh
Date: 2019-03-04 00:00:00
Tasks: Clustering, Image/Document Clustering

This paper focuses on scalability and robustness of spectral clustering for
extremely large-scale datasets with limited resources. Two novel algorithms are
proposed, namely, ultra-scalable spectral clustering (U-SPEC) and
ultra-scalable ensemble clustering (U-SENC). In U-SPEC, a hybrid representative
selection strategy and a fast approximation method for K-nearest
representatives are proposed for the construction of a sparse affinity
sub-matrix. By interpreting the sparse sub-matrix as a bipartite graph, the
transfer cut is then utilized to efficiently partition the graph and obtain the
clustering result. In U-SENC, multiple U-SPEC clusterers are further integrated
into an ensemble clustering framework to enhance the robustness of U-SPEC while
maintaining high efficiency. Based on the ensemble generation via multiple
U-SEPC's, a new bipartite graph is constructed between objects and base
clusters and then efficiently partitioned to achieve the consensus clustering
result. It is noteworthy that both U-SPEC and U-SENC have nearly linear time
and space complexity, and are capable of robustly and efficiently partitioning
ten-million-level nonlinearly-separable datasets on a PC with 64GB memory.
Experiments on various large-scale datasets have demonstrated the scalability
and robustness of our algorithms. The MATLAB code and experimental data are
available at https://www.researchgate.net/publication/330760669.
Robust commuter movement inference from connected mobile devices
Authors: Baoyang Song, Hasan Poonawala, Laura Wynter, Sebastien Blandin
Date: 2019-03-04 00:00:00
Tasks: Clustering

The preponderance of connected devices provides unprecedented opportunities
for fine-grained monitoring of the public infrastructure. However while
classical models expect high quality application-specific data streams, the
promise of the Internet of Things (IoT) is that of an abundance of disparate
and noisy datasets from connected devices. In this context, we consider the
problem of estimation of the level of service of a city-wide public transport
network. We first propose a robust unsupervised model for train movement
inference from wifi traces, via the application of robust clustering methods to
a one dimensional spatio-temporal setting. We then explore the extent to which
the demand-supply gap can be estimated from connected devices. We propose a
classification model of real-time commuter patterns, including both a batch
training phase and an online learning component. We describe our deployment
architecture and assess our system accuracy on a large-scale anonymized dataset
comprising more than 10 billion records.
CodeNet: Training Large Scale Neural Networks in Presence of Soft-Errors
Authors: Sanghamitra Dutta, Ziqian Bai, Tze Meng Low, Pulkit Grover
Date: 2019-03-04 00:00:00

This work proposes the first strategy to make distributed training of neural
networks resilient to computing errors, a problem that has remained unsolved
despite being first posed in 1956 by von Neumann. He also speculated that the
efficiency and reliability of the human brain is obtained by allowing for low
power but error-prone components with redundancy for error-resilience. It is
surprising that this problem remains open, even as massive artificial neural
networks are being trained on increasingly low-cost and unreliable processing
units. Our coding-theory-inspired strategy, "CodeNet," solves this problem by
addressing three challenges in the science of reliable computing: (i) Providing
the first strategy for error-resilient neural network training by encoding each
layer separately; (ii) Keeping the overheads of coding
(encoding/error-detection/decoding) low by obviating the need to re-encode the
updated parameter matrices after each iteration from scratch. (iii) Providing a
completely decentralized implementation with no central node (which is a single
point of failure), allowing all primary computational steps to be error-prone.
We theoretically demonstrate that CodeNet has higher error tolerance than
replication, which we leverage to speed up computation time. Simultaneously,
CodeNet requires lower redundancy than replication, and equal computational and
communication costs in scaling sense. We first demonstrate the benefits of
CodeNet in reducing expected computation time over replication when accounting
for checkpointing. Our experiments show that CodeNet achieves the best
accuracy-runtime tradeoff compared to both replication and uncoded strategies.
CodeNet is a significant step towards biologically plausible neural network
training, that could hold the key to orders of magnitude efficiency
improvements.
SECNLP: A Survey of Embeddings in Clinical Natural Language Processing
Authors: Kalyan KS, S. Sangeetha
Date: 2019-03-04 00:00:00
Tasks: Survey

Traditional representations like Bag of words are high dimensional, sparse and ignore the order as well as syntactic and semantic information. Distributed vector representations or embeddings map variable length text to dense fixed length vectors as well as capture the prior knowledge which can transferred to downstream tasks. Even though embedding has become de facto standard for representations in deep learning based NLP tasks in both general and clinical domains, there is no survey paper which presents a detailed review of embeddings in Clinical Natural Language Processing. In this survey paper, we discuss various medical corpora and their characteristics, medical codes and present a brief overview as well as comparison of popular embeddings models. We classify clinical embeddings into nine types and discuss each embedding type in detail. We discuss various evaluation methods followed by possible solutions to various challenges in clinical embeddings. Finally, we conclude with some of the future directions which will advance the research in clinical embeddings.
Spatiotemporal Pyramid Network for Video Action Recognition
Authors: Yunbo Wang, Mingsheng Long, Jian-Min Wang, Philip S. Yu
Date: 2019-03-04 00:00:00
Proceeding: CVPR 2017 7
Tasks: Action Recognition, Temporal Action Localization

Two-stream convolutional networks have shown strong performance in video
action recognition tasks. The key idea is to learn spatiotemporal features by
fusing convolutional networks spatially and temporally. However, it remains
unclear how to model the correlations between the spatial and temporal
structures at multiple abstraction levels. First, the spatial stream tends to
fail if two videos share similar backgrounds. Second, the temporal stream may
be fooled if two actions resemble in short snippets, though appear to be
distinct in the long term. We propose a novel spatiotemporal pyramid network to
fuse the spatial and temporal features in a pyramid structure such that they
can reinforce each other. From the architecture perspective, our network
constitutes hierarchical fusion strategies which can be trained as a whole
using a unified spatiotemporal loss. A series of ablation experiments support
the importance of each fusion strategy. From the technical perspective, we
introduce the spatiotemporal compact bilinear operator into video analysis
tasks. This operator enables efficient training of bilinear fusion operations
which can capture full interactions between the spatial and temporal features.
Our final network achieves state-of-the-art results on standard video datasets.
A Fundamental Performance Limitation for Adversarial Classification
Authors: Abed AlRahman Al Makdah, Vaibhav Katewa, Fabio Pasqualetti
Date: 2019-03-04 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Classification, General Classification

Despite the widespread use of machine learning algorithms to solve problems
of technological, economic, and social relevance, provable guarantees on the
performance of these data-driven algorithms are critically lacking, especially
when the data originates from unreliable sources and is transmitted over
unprotected and easily accessible channels. In this paper we take an important
step to bridge this gap and formally show that, in a quest to optimize their
accuracy, binary classification algorithms -- including those based on
machine-learning techniques -- inevitably become more sensitive to adversarial
manipulation of the data. Further, for a given class of algorithms with the
same complexity (i.e., number of classification boundaries), the fundamental
tradeoff curve between accuracy and sensitivity depends solely on the
statistics of the data, and cannot be improved by tuning the algorithm.
Active Authentication using an Autoencoder regularized CNN-based One-Class Classifier
Authors: Poojan Oza, Vishal M. Patel
Date: 2019-03-04 00:00:00
Tasks: Classification, General Classification, One-Class Classification, One-class classifier

Active authentication refers to the process in which users are unobtrusively
monitored and authenticated continuously throughout their interactions with
mobile devices. Generally, an active authentication problem is modelled as a
one class classification problem due to the unavailability of data from the
impostor users. Normally, the enrolled user is considered as the target class
(genuine) and the unauthorized users are considered as unknown classes
(impostor). We propose a convolutional neural network (CNN) based approach for
one class classification in which a zero centered Gaussian noise and an
autoencoder are used to model the pseudo-negative class and to regularize the
network to learn meaningful feature representations for one class data,
respectively. The overall network is trained using a combination of the
cross-entropy and the reconstruction error losses. A key feature of the
proposed approach is that any pre-trained CNN can be used as the base network
for one class classification. Effectiveness of the proposed framework is
demonstrated using three publically available face-based active authentication
datasets and it is shown that the proposed method achieves superior performance
compared to the traditional one class classification methods. The source code
is available at: github.com/otkupjnoz/oc-acnn.
Learning Modular Safe Policies in the Bandit Setting with Application to Adaptive Clinical Trials
Authors: Hossein Aboutalebi, Doina Precup, Tibor Schuster
Date: 2019-03-04 00:00:00

The stochastic multi-armed bandit problem is a well-known model for studying the exploration-exploitation trade-off. It has significant possible applications in adaptive clinical trials, which allow for dynamic changes in the treatment allocation probabilities of patients. However, most bandit learning algorithms are designed with the goal of minimizing the expected regret. While this approach is useful in many areas, in clinical trials, it can be sensitive to outlier data, especially when the sample size is small. In this paper, we define and study a new robustness criterion for bandit problems. Specifically, we consider optimizing a function of the distribution of returns as a regret measure. This provides practitioners more flexibility to define an appropriate regret measure. The learning algorithm we propose to solve this type of problem is a modification of the BESA algorithm [Baransi et al., 2014], which considers a more general version of regret. We present a regret bound for our approach and evaluate it empirically both on synthetic problems as well as on a dataset from the clinical trial literature. Our approach compares favorably to a suite of standard bandit algorithms.