pwc-archive/papers-with-abstracts huggingface dataset:
Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop
Authors: Francesco Restuccia, Tommaso Melodia
Date: 2019-03-12 00:00:00
Tasks: CPU

The explosion of 5G networks and the Internet of Things will result in an
exceptionally crowded RF environment, where techniques such as spectrum sharing
and dynamic spectrum access will become essential components of the wireless
communication process. In this vision, wireless devices must be able to (i)
learn to autonomously extract knowledge from the spectrum on-the-fly; and (ii)
react in real time to the inferred spectrum knowledge by appropriately changing
communication parameters, including frequency band, symbol modulation, coding
rate, among others. Traditional CPU-based machine learning suffers from high
latency, and requires application-specific and computationally-intensive
feature extraction/selection algorithms. In this paper, we present RFLearn, the
first system enabling spectrum knowledge extraction from unprocessed I/Q
samples by deep learning directly in the RF loop. RFLearn provides (i) a
complete hardware/software architecture where the CPU, radio transceiver and
learning/actuation circuits are tightly connected for maximum performance; and
(ii) a learning circuit design framework where the latency vs. hardware
resource consumption trade-off can explored. We implement and evaluate the
performance of RFLearn on custom software-defined radio built on a
system-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers and
VERT2450 antennas. We showcase the capabilities of RFLearn by applying it to
solving the fundamental problems of modulation and OFDM parameter recognition.
Experimental results reveal that RFLearn decreases latency and power by about
17x and 15x with respect to a software-based solution, with a comparatively low
hardware resource consumption.
Variational Bayesian Optimal Experimental Design
Authors: Adam Foster, Martin Jankowiak, Eli Bingham, Paul Horsfall, Yee Whye Teh, Tom Rainforth, Noah Goodman
Date: 2019-03-13 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Experimental Design, Variational Inference

Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information gain (EIG) of an experiment. To address this, we introduce several classes of fast EIG estimators by building on ideas from amortized variational inference. We show theoretically and empirically that these estimators can provide significant gains in speed and accuracy over previous approaches. We further demonstrate the practicality of our approach on a number of end-to-end experiments.
Hardness-Aware Deep Metric Learning
Authors: Wenzhao Zheng, Zhaodong Chen, Jiwen Lu, Jie zhou
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Retrieval, Metric Learning

This paper presents a hardness-aware deep metric learning (HDML) framework. Most previous deep metric learning methods employ the hard negative mining strategy to alleviate the lack of informative samples for training. However, this mining strategy only utilizes a subset of training data, which may not be enough to characterize the global geometry of the embedding space comprehensively. To address this problem, we perform linear interpolation on embeddings to adaptively manipulate their hard levels and generate corresponding label-preserving synthetics for recycled training, so that information buried in all samples can be fully exploited and the metric is always challenged with proper difficulty. Our method achieves very competitive performance on the widely used CUB-200-2011, Cars196, and Stanford Online Products datasets.
Predicting class-imbalanced business risk using resampling, regularization, and model ensembling algorithms
Authors: Yan Wang, Xuelei Sherry Ni
Date: 2019-03-13 00:00:00

We aim at developing and improving the imbalanced business risk modeling via
jointly using proper evaluation criteria, resampling, cross-validation,
classifier regularization, and ensembling techniques. Area Under the Receiver
Operating Characteristic Curve (AUC of ROC) is used for model comparison based
on 10-fold cross validation. Two undersampling strategies including random
undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two
oversampling methods including random oversampling (ROS) and Synthetic Minority
Oversampling Technique (SMOTE), are applied. Three highly interpretable
classifiers, including logistic regression without regularization (LR),
L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two
ensembling techniques, including Bagging and Boosting, are applied on the DT
classifier for further model improvement. The results show that, Boosting on DT
by using the oversampled data containing 50% positives via SMOTE is the optimal
model and it can achieve AUC, recall, and F1 score valued 0.8633, 0.9260, and
0.8907, respectively.
Learning Multi-agent Communication under Limited-bandwidth Restriction for Internet Packet Routing
Authors: Hangyu Mao, Zhibo Gong, Zhengchao Zhang, Zhen Xiao, Yan Ni
Date: 2019-02-26 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning

Communication is an important factor for the big multi-agent world to stay
organized and productive. Recently, the AI community has applied the Deep
Reinforcement Learning (DRL) to learn the communication strategy and the
control policy for multiple agents. However, when implementing the
communication for real-world multi-agent applications, there is a more
practical limited-bandwidth restriction, which has been largely ignored by the
existing DRL-based methods. Specifically, agents trained by most previous
methods keep sending messages incessantly in every control cycle; due to
emitting too many messages, these methods are unsuitable to be applied to the
real-world systems that have a limited bandwidth to transmit the messages. To
handle this problem, we propose a gating mechanism to adaptively prune
unprofitable messages. Results show that the gating mechanism can prune more
than 80% messages with little damage to the performance. Moreover, our method
outperforms several state-of-the-art DRL-based and rule-based methods by a
large margin in both the real-world packet routing tasks and four benchmark
tasks.
Benchmarking Natural Language Understanding Services for building Conversational Agents
Authors: Xingkun Liu, Arash Eshghi, Pawel Swietojanski, Verena Rieser
Date: 2019-03-13 00:00:00
Tasks: Benchmarking, General Classification, intent-classification, Intent Classification, Natural Language Understanding

We have recently seen the emergence of several publicly available Natural
Language Understanding (NLU) toolkits, which map user utterances to structured,
but more abstract, Dialogue Act (DA) or Intent specifications, while making
this process accessible to the lay developer. In this paper, we present the
first wide coverage evaluation and comparison of some of the most popular NLU
services, on a large, multi-domain (21 domains) dataset of 25K user utterances
that we have collected and annotated with Intent and Entity Type specifications
and which will be released as part of this submission. The results show that on
Intent classification Watson significantly outperforms the other platforms,
namely, Dialogflow, LUIS and Rasa; though these also perform well.
Interestingly, on Entity Type recognition, Watson performs significantly worse
due to its low Precision. Again, Dialogflow, LUIS and Rasa perform well on this
task.
GASC: Genre-Aware Semantic Change for Ancient Greek
Authors: Valerio Perrone, Marco Palma, Simon Hengchen, Alessandro Vatri, Jim Q. Smith, Barbara McGillivray
Date: 2019-03-13 00:00:00
Proceeding: WS 2019 8
Tasks: Information Retrieval, Retrieval

Word meaning changes over time, depending on linguistic and extra-linguistic factors. Associating a word's correct meaning in its historical context is a central challenge in diachronic research, and is relevant to a range of NLP tasks, including information retrieval and semantic search in historical texts. Bayesian models for semantic change have emerged as a powerful tool to address this challenge, providing explicit and interpretable representations of semantic change phenomena. However, while corpora typically come with rich metadata, existing models are limited by their inability to exploit contextual information (such as text genre) beyond the document time-stamp. This is particularly critical in the case of ancient languages, where lack of data and long diachronic span make it harder to draw a clear distinction between polysemy (the fact that a word has several senses) and semantic change (the process of acquiring, losing, or changing senses), and current systems perform poorly on these languages. We develop GASC, a dynamic semantic change model that leverages categorical metadata about the texts' genre to boost inference and uncover the evolution of meanings in Ancient Greek corpora. In a new evaluation framework, our model achieves improved predictive performance compared to the state of the art.
Gaussian Process Optimization with Adaptive Sketching: Scalable and No Regret
Authors: Daniele Calandriello, Luigi Carratino, Alessandro Lazaric, Michal Valko, Lorenzo Rosasco
Date: 2019-03-13 00:00:00
Tasks: Gaussian Processes

Gaussian processes (GP) are a well studied Bayesian approach for the optimization of black-box functions. Despite their effectiveness in simple problems, GP-based algorithms hardly scale to high-dimensional functions, as their per-iteration time and space cost is at least quadratic in the number of dimensions $d$ and iterations $t$. Given a set of $A$ alternatives to choose from, the overall runtime $O(t^3A)$ is prohibitive. In this paper we introduce BKB (budgeted kernelized bandit), a new approximate GP algorithm for optimization under bandit feedback that achieves near-optimal regret (and hence near-optimal convergence rate) with near-constant per-iteration complexity and remarkably no assumption on the input space or covariance of the GP. We combine a kernelized linear bandit algorithm (GP-UCB) with randomized matrix sketching based on leverage score sampling, and we prove that randomly sampling inducing points based on their posterior variance gives an accurate low-rank approximation of the GP, preserving variance estimates and confidence intervals. As a consequence, BKB does not suffer from variance starvation, an important problem faced by many previous sparse GP approximations. Moreover, we show that our procedure selects at most $\tilde{O}(d_{eff})$ points, where $d_{eff}$ is the effective dimension of the explored space, which is typically much smaller than both $d$ and $t$. This greatly reduces the dimensionality of the problem, thus leading to a $O(TAd_{eff}^2)$ runtime and $O(A d_{eff})$ space complexity.
Computing Approximate Equilibria in Sequential Adversarial Games by Exploitability Descent
Authors: Edward Lockhart, Marc Lanctot, Julien Pérolat, Jean-Baptiste Lespiau, Dustin Morrill, Finbarr Timbers, Karl Tuyls
Date: 2019-03-13 00:00:00
Tasks: counterfactual

In this paper, we present exploitability descent, a new algorithm to compute approximate equilibria in two-player zero-sum extensive-form games with imperfect information, by direct policy optimization against worst-case opponents. We prove that when following this optimization, the exploitability of a player's strategy converges asymptotically to zero, and hence when both players employ this optimization, the joint policies converge to a Nash equilibrium. Unlike fictitious play (XFP) and counterfactual regret minimization (CFR), our convergence result pertains to the policies being optimized rather than the average policies. Our experiments demonstrate convergence rates comparable to XFP and CFR in four benchmark games in the tabular case. Using function approximation, we find that our algorithm outperforms the tabular version in two of the games, which, to the best of our knowledge, is the first such result in imperfect information games among this class of algorithms.
Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach
Authors: Jaeseok Kim, Nino Cauli, Pedro Vicente, Bruno Damas, Alexandre Bernardino, José Santos-Victor, Filippo Cavallo
Date: 2019-03-13 00:00:00
Tasks: Data Augmentation, Transfer Learning

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy.
Universally Slimmable Networks and Improved Training Techniques
Authors: Jiahui Yu, Thomas Huang
Date: 2019-03-12 00:00:00
Proceeding: ICCV 2019 10
Tasks: Deep Reinforcement Learning, Image Super-Resolution, Reinforcement Learning, Super-Resolution

Slimmable networks are a family of neural networks that can instantly adjust the runtime width. The width can be chosen from a predefined widths set to adaptively optimize accuracy-efficiency trade-offs at runtime. In this work, we propose a systematic approach to train universally slimmable networks (US-Nets), extending slimmable networks to execute at arbitrary width, and generalizing to networks both with and without batch normalization layers. We further propose two improved training techniques for US-Nets, named the sandwich rule and inplace distillation, to enhance training process and boost testing accuracy. We show improved performance of universally slimmable MobileNet v1 and MobileNet v2 on ImageNet classification task, compared with individually trained ones and 4-switch slimmable network baselines. We also evaluate the proposed US-Nets and improved training techniques on tasks of image super-resolution and deep reinforcement learning. Extensive ablation experiments on these representative tasks demonstrate the effectiveness of our proposed methods. Our discovery opens up the possibility to directly evaluate FLOPs-Accuracy spectrum of network architectures. Code and models are available at: https://github.com/JiahuiYu/slimmable_networks
Learning Feature Aggregation in Temporal Domain for Re-Identification
Authors: Jakub Špaňhel, Jakub Sochor, Roman Juránek, Petr Dobeš, Vojtěch Bartl, Adam Herout
Date: 2019-03-12 00:00:00
Tasks: Person Re-Identification, Vehicle Re-Identification

Person re-identification is a standard and established problem in the
computer vision community. In recent years, vehicle re-identification is also
getting more attention. In this paper, we focus on both these tasks and propose
a method for aggregation of features in temporal domain as it is common to have
multiple observations of the same object. The aggregation is based on weighting
different elements of the feature vectors by different weights and it is
trained in an end-to-end manner by a Siamese network. The experimental results
show that our method outperforms other existing methods for feature aggregation
in temporal domain on both vehicle and person re-identification tasks.
Furthermore, to push research in vehicle re-identification further, we
introduce a novel dataset CarsReId74k. The dataset is not limited to
frontal/rear viewpoints. It contains 17,681 unique vehicles, 73,976 observed
tracks, and 277,236 positive pairs. The dataset was captured by 66 cameras from
various angles.
Towards Unsupervised Cancer Subtyping: Predicting Prognosis Using A Histologic Visual Dictionary
Authors: Hassan Muhammad, Carlie S. Sigel, Gabriele Campanella, Thomas Boerner, Linda M. Pak, Stefan Büttner, Jan N. M. IJzermans, Bas Groot Koerkamp, Michael Doukas, William R. Jarnagin, Amber Simpson, Thomas J. Fuchs
Date: 2019-03-12 00:00:00
Tasks: Clustering, Prognosis

Unlike common cancers, such as those of the prostate and breast, tumor
grading in rare cancers is difficult and largely undefined because of small
sample sizes, the sheer volume of time needed to undertake on such a task, and
the inherent difficulty of extracting human-observed patterns. One of the most
challenging examples is intrahepatic cholangiocarcinoma (ICC), a primary liver
cancer arising from the biliary system, for which there is well-recognized
tumor heterogeneity and no grading paradigm or prognostic biomarkers. In this
paper, we propose a new unsupervised deep convolutional autoencoder-based
clustering model that groups together cellular and structural morphologies of
tumor in 246 ICC digitized whole slides, based on visual similarity. From this
visual dictionary of histologic patterns, we use the clusters as covariates to
train Cox-proportional hazard survival models. In univariate analysis, three
clusters were significantly associated with recurrence-free survival.
Combinations of these clusters were significant in multivariate analysis. In a
multivariate analysis of all clusters, five showed significance to
recurrence-free survival, however the overall model was not measured to be
significant. Finally, a pathologist assigned clinical terminology to the
significant clusters in the visual dictionary and found evidence supporting the
hypothesis that collagen-enriched fibrosis plays a role in disease severity.
These results offer insight into the future of cancer subtyping and show that
computational pathology can contribute to disease prognostication, especially
in rare cancers.
All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
Authors: Wei-Jie Chen, Di Xie, Yuan Zhang, ShiLiang Pu
Date: 2019-03-13 00:00:00
Proceeding: CVPR 2019 6
Tasks: All, General Classification, image-classification, Image Classification, Quantization

Shift operation is an efficient alternative over depthwise separable
convolution. However, it is still bottlenecked by its implementation manner,
namely memory movement. To put this direction forward, a new and novel basic
component named Sparse Shift Layer (SSL) is introduced in this paper to
construct efficient convolutional neural networks. In this family of
architectures, the basic block is only composed by 1x1 convolutional layers
with only a few shift operations applied to the intermediate feature maps. To
make this idea feasible, we introduce shift operation penalty during
optimization and further propose a quantization-aware shift learning method to
impose the learned displacement more friendly for inference. Extensive ablation
studies indicate that only a few shift operations are sufficient to provide
spatial information communication. Furthermore, to maximize the role of SSL, we
redesign an improved network architecture to Fully Exploit the limited capacity
of neural Network (FE-Net). Equipped with SSL, this network can achieve 75.0%
top-1 accuracy on ImageNet with only 563M M-Adds. It surpasses other
counterparts constructed by depthwise separable convolution and the networks
searched by NAS in terms of accuracy and practical speed.
CIA-Net: Robust Nuclei Instance Segmentation with Contour-aware Information Aggregation
Authors: Yanning Zhou, Omer Fahri Onder, Qi Dou, Efstratios Tsougenis, Hao Chen, Pheng-Ann Heng
Date: 2019-03-13 00:00:00
Tasks: Instance Segmentation, Multi-tissue Nucleus Segmentation, Segmentation, Semantic Segmentation

Accurate segmenting nuclei instances is a crucial step in computer-aided
image analysis to extract rich features for cellular estimation and following
diagnosis as well as treatment. While it still remains challenging because the
wide existence of nuclei clusters, along with the large morphological variances
among different organs make nuclei instance segmentation susceptible to
over-/under-segmentation. Additionally, the inevitably subjective annotating
and mislabeling prevent the network learning from reliable samples and
eventually reduce the generalization capability for robustly segmenting unseen
organ nuclei. To address these issues, we propose a novel deep neural network,
namely Contour-aware Informative Aggregation Network (CIA-Net) with multi-level
information aggregation module between two task-specific decoders. Rather than
independent decoders, it leverages the merit of spatial and texture
dependencies between nuclei and contour by bi-directionally aggregating
task-specific features. Furthermore, we proposed a novel smooth truncated loss
that modulates losses to reduce the perturbation from outliers. Consequently,
the network can focus on learning from reliable and informative samples, which
inherently improves the generalization capability. Experiments on the 2018
MICCAI challenge of Multi-Organ-Nuclei-Segmentation validated the effectiveness
of our proposed method, surpassing all the other 35 competitive teams by a
significant margin.
Face Liveness Detection Based on Client Identity Using Siamese Network
Authors: Huiling Hao, Mingtao Pei
Date: 2019-03-13 00:00:00
Tasks: Face Recognition

Face liveness detection is an essential prerequisite for face recognition
applications. Previous face liveness detection methods usually train a binary
classifier to differentiate between a fake face and a real face before face
recognition. The client identity information is not utilized in previous face
liveness detection methods. However, in practical face recognition
applications, face spoofing attacks are always aimed at a specific client, and
the client identity information can provide useful clues for face liveness
detection. In this paper, we propose a face liveness detection method based on
the client identity using Siamese network. We detect face liveness after face
recognition instead of before face recognition, that is, we detect face
liveness with the client identity information. We train a Siamese network with
image pairs. Each image pair consists of two real face images or one real and
one fake face images. The face images in each pair come from a same client.
Given a test face image, the face image is firstly recognized by face
recognition system, then the real face image of the identified client is
retrieved to help the face liveness detection. Experiment results demonstrate
the effectiveness of our method.