pwc-archive/papers-with-abstracts huggingface dataset:
Unsupervised Rank-Preserving Hashing for Large-Scale Image Retrieval
Authors: Svebor Karaman, Xudong Lin, Xuefeng Hu, Shih-Fu Chang
Date: 2019-03-04 00:00:00
Tasks: Decoder, Image Retrieval, Re-Ranking, Retrieval

We propose an unsupervised hashing method which aims to produce binary codes
that preserve the ranking induced by a real-valued representation. Such compact
hash codes enable the complete elimination of real-valued feature storage and
allow for significant reduction of the computation complexity and storage cost
of large-scale image retrieval applications. Specifically, we learn a neural
network-based model, which transforms the input representation into a binary
representation. We formalize the training objective of the network in an
intuitive and effective way, considering each training sample as a query and
aiming to obtain the same retrieval results using the produced hash codes as
those obtained with the original features. This training formulation directly
optimizes the hashing model for the target usage of the hash codes it produces.
We further explore the addition of a decoder trained to obtain an approximated
reconstruction of the original features. At test time, we retrieved the most
promising database samples with an efficient graph-based search procedure using
only our hash codes and perform re-ranking using the reconstructed features,
thus without needing to access the original features at all. Experiments
conducted on multiple publicly available large-scale datasets show that our
method consistently outperforms all compared state-of-the-art unsupervised
hashing methods and that the reconstruction procedure can effectively boost the
search accuracy with a minimal constant additional cost.
A Stochastic Trust Region Method for Non-convex Minimization
Authors: Zebang Shen, Pan Zhou, Cong Fang, Alejandro Ribeiro
Date: 2019-03-04 00:00:00
Proceeding: ICLR 2020 1

We target the problem of finding a local minimum in non-convex finite-sum
minimization. Towards this goal, we first prove that the trust region method
with inexact gradient and Hessian estimation can achieve a convergence rate of
order $\mathcal{O}(1/{k^{2/3}})$ as long as those differential estimations are
sufficiently accurate. Combining such result with a novel Hessian estimator, we
propose the sample-efficient stochastic trust region (STR) algorithm which
finds an $(\epsilon, \sqrt{\epsilon})$-approximate local minimum within
$\mathcal{O}({\sqrt{n}}/{\epsilon^{1.5}})$ stochastic Hessian oracle queries.
This improves state-of-the-art result by $\mathcal{O}(n^{1/6})$. Experiments
verify theoretical conclusions and the efficiency of STR.
MGpi: A Computational Model of Multiagent Group Perception and Interaction
Authors: Navyata Sanghvi, Ryo Yonetani, Kris Kitani
Date: 2019-03-04 00:00:00
Tasks: Imitation Learning

Toward enabling next-generation robots capable of socially intelligent interaction with humans, we present a $\mathbf{computational\; model}$ of interactions in a social environment of multiple agents and multiple groups. The Multiagent Group Perception and Interaction (MGpi) network is a deep neural network that predicts the appropriate social action to execute in a group conversation (e.g., speak, listen, respond, leave), taking into account neighbors' observable features (e.g., location of people, gaze orientation, distraction, etc.). A central component of MGpi is the Kinesic-Proxemic-Message (KPM) gate, that performs social signal gating to extract important information from a group conversation. In particular, KPM gate filters incoming social cues from nearby agents by observing their body gestures (kinesics) and spatial behavior (proxemics). The MGpi network and its KPM gate are learned via imitation learning, using demonstrations from our designed $\mathbf{social\; interaction\; simulator}$. Further, we demonstrate the efficacy of the KPM gate as a social attention mechanism, achieving state-of-the-art performance on the task of $\mathbf{group\; identification}$ without using explicit group annotations, layout assumptions, or manually chosen parameters.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Authors: Changhao Chen, Stefano Rosa, Yishu Miao, Chris Xiaoxuan Lu, Wei Wu, Andrew Markham, Niki Trigoni
Date: 2019-03-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Autonomous Driving, Sensor Fusion

Deep learning approaches for Visual-Inertial Odometry (VIO) have proven
successful, but they rarely focus on incorporating robust fusion strategies for
dealing with imperfect input sensory data. We propose a novel end-to-end
selective sensor fusion framework for monocular VIO, which fuses monocular
images and inertial measurements in order to estimate the trajectory whilst
improving robustness to real-life issues, such as missing and corrupted data or
bad sensor synchronization. In particular, we propose two fusion modalities
based on different masking strategies: deterministic soft fusion and stochastic
hard fusion, and we compare with previously proposed direct fusion baselines.
During testing, the network is able to selectively process the features of the
available sensor modalities and produce a trajectory at scale. We present a
thorough investigation on the performances on three public autonomous driving,
Micro Aerial Vehicle (MAV) and hand-held VIO datasets. The results demonstrate
the effectiveness of the fusion strategies, which offer better performances
compared to direct fusion, particularly in presence of corrupted data. In
addition, we study the interpretability of the fusion networks by visualising
the masking layers in different scenarios and with varying data corruption,
revealing interesting correlations between the fusion networks and imperfect
sensory input data.
Ternary Hybrid Neural-Tree Networks for Highly Constrained IoT Applications
Authors: Dibakar Gope, Ganesh Dasika, Matthew Mattina
Date: 2019-03-04 00:00:00
Tasks: Keyword Spotting, Quantization

Machine learning-based applications are increasingly prevalent in IoT
devices. The power and storage constraints of these devices make it
particularly challenging to run modern neural networks, limiting the number of
new applications that can be deployed on an IoT system. A number of compression
techniques have been proposed, each with its own trade-offs. We propose a
hybrid network which combines the strengths of current neural- and tree-based
learning techniques in conjunction with ternary quantization, and show a
detailed analysis of the associated model design space. Using this hybrid model
we obtained a 11.1% reduction in the number of computations, a 52.2% reduction
in the model size, and a 30.6% reduction in the overall memory footprint over a
state-of-the-art keyword-spotting neural network, with negligible loss in
accuracy.
Learning of Image Dehazing Models for Segmentation Tasks
Authors: Sébastien de Blois, Ihsen Hedhli, Christian Gagné
Date: 2019-03-04 00:00:00
Tasks: Image Dehazing, Image Segmentation, Segmentation, Semantic Segmentation

To evaluate their performance, existing dehazing approaches generally rely on distance measures between the generated image and its corresponding ground truth. Despite its ability to produce visually good images, using pixel-based or even perceptual metrics do not guarantee, in general, that the produced image is fit for being used as input for low-level computer vision tasks such as segmentation. To overcome this weakness, we are proposing a novel end-to-end approach for image dehazing, fit for being used as input to an image segmentation procedure, while maintaining the visual quality of the generated images. Inspired by the success of Generative Adversarial Networks (GAN), we propose to optimize the generator by introducing a discriminator network and a loss function that evaluates segmentation quality of dehazed images. In addition, we make use of a supplementary loss function that verifies that the visual and the perceptual quality of the generated image are preserved in hazy conditions. Results obtained using the proposed technique are appealing, with a favorable comparison to state-of-the-art approaches when considering the performance of segmentation algorithms on the hazy images.
TKD: Temporal Knowledge Distillation for Active Perception
Authors: Mohammad Farhadi, Yezhou Yang
Date: 2019-03-04 00:00:00
Tasks: Knowledge Distillation, Object, object-detection, Object Detection, Object Recognition

Deep neural networks based methods have been proved to achieve outstanding performance on object detection and classification tasks. Despite significant performance improvement, due to the deep structures, they still require prohibitive runtime to process images and maintain the highest possible performance for real-time applications. Observing the phenomenon that human vision system (HVS) relies heavily on the temporal dependencies among frames from the visual input to conduct recognition efficiently, we propose a novel framework dubbed as TKD: temporal knowledge distillation. This framework distills the temporal knowledge from a heavy neural networks based model over selected video frames (the perception of the moments) to a light-weight model. To enable the distillation, we put forward two novel procedures: 1) an Long-short Term Memory (LSTM) based key frame selection method; and 2) a novel teacher-bounded loss design. To validate, we conduct comprehensive empirical evaluations using different object detection methods over multiple datasets including Youtube-Objects and Hollywood scene dataset. Our results show consistent improvement in accuracy-speed trad-offs for object detection over the frames of the dynamic scene, compare to other modern object recognition methods.
Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs
Authors: Partha Maji, Andrew Mundy, Ganesh Dasika, Jesse Beu, Matthew Mattina, Robert Mullins
Date: 2019-03-04 00:00:00

The Winograd or Cook-Toom class of algorithms help to reduce the overall
compute complexity of many modern deep convolutional neural networks (CNNs).
Although there has been a lot of research done on model and algorithmic
optimization of CNN, little attention has been paid to the efficient
implementation of these algorithms on embedded CPUs, which usually have very
limited memory and low power budget. This paper aims to fill this gap and
focuses on the efficient implementation of Winograd or Cook-Toom based
convolution on modern Arm Cortex-A CPUs, widely used in mobile devices today.
Specifically, we demonstrate a reduction in inference latency by using a set of
optimization strategies that improve the utilization of computational
resources, and by effectively leveraging the ARMv8-A NEON SIMD instruction set.
We evaluated our proposed region-wise multi-channel implementations on Arm
Cortex-A73 platform using several representative CNNs. The results show
significant performance improvements in full network, up to 60%, over existing
im2row/im2col based optimization techniques
Fine-grained lesion annotation in CT images with knowledge mined from radiology reports
Authors: Ke Yan, Yifan Peng, Zhiyong Lu, Ronald M. Summers
Date: 2019-03-04 00:00:00
Tasks: Sentence

In radiologists' routine work, one major task is to read a medical image,
e.g., a CT scan, find significant lesions, and write sentences in the radiology
report to describe them. In this paper, we study the lesion description or
annotation problem as an important step of computer-aided diagnosis (CAD).
Given a lesion image, our aim is to predict multiple relevant labels, such as
the lesion's body part, type, and attributes. To address this problem, we
define a set of 145 labels based on RadLex to describe a large variety of
lesions in the DeepLesion dataset. We directly mine training labels from the
lesion's corresponding sentence in the radiology report, which requires minimal
manual effort and is easily generalizable to large data and label sets. A
multi-label convolutional neural network is then proposed for images with
multi-scale structure and a noise-robust loss. Quantitative and qualitative
experiments demonstrate the effectiveness of the framework. The average area
under ROC curve on 1,872 test lesions is 0.9083.
Voyageur: An Experiential Travel Search Engine
Authors: Sara Evensen, Aaron Feng, Alon Halevy, Jinfeng Li, Vivian Li, Yuliang Li, Huining Liu, George Mihaila, John Morales, Natalie Nuno, Ekaterina Pavlovic, Wang-Chiew Tan, Xiaolan Wang
Date: 2019-03-04 00:00:00

We describe Voyageur, which is an application of experiential search to the
domain of travel. Unlike traditional search engines for online services,
experiential search focuses on the experiential aspects of the service under
consideration. In particular, Voyageur needs to handle queries for subjective
aspects of the service (e.g., quiet hotel, friendly staff) and combine these
with objective attributes, such as price and location. Voyageur also highlights
interesting facts and tips about the services the user is considering to
provide them with further insights into their choices.
M-VAD Names: a Dataset for Video Captioning with Naming
Authors: Stefano Pini, Marcella Cornia, Federico Bolelli, Lorenzo Baraldi, Rita Cucchiara
Date: 2019-03-04 00:00:00
Tasks: TAG, Video Captioning

Current movie captioning architectures are not capable of mentioning
characters with their proper name, replacing them with a generic "someone" tag.
The lack of movie description datasets with characters' visual annotations
surely plays a relevant role in this shortage. Recently, we proposed to extend
the M-VAD dataset by introducing such information. In this paper, we present an
improved version of the dataset, namely M-VAD Names, and its semi-automatic
annotation procedure. The resulting dataset contains 63k visual tracks and 34k
textual mentions, all associated with character identities. To showcase the
features of the dataset and quantify the complexity of the naming task, we
investigate multimodal architectures to replace the "someone" tags with proper
character names in existing video captions. The evaluation is further extended
by testing this application on videos outside of the M-VAD Names dataset.
SGD without Replacement: Sharper Rates for General Smooth Convex Functions
Authors: Prateek Jain, Dheeraj Nagaraj, Praneeth Netrapalli
Date: 2019-03-04 00:00:00

We study stochastic gradient descent {\em without replacement} (\sgdwor) for smooth convex functions. \sgdwor is widely observed to converge faster than true \sgd where each sample is drawn independently {\em with replacement} \cite{bottou2009curiously} and hence, is more popular in practice. But it's convergence properties are not well understood as sampling without replacement leads to coupling between iterates and gradients. By using method of exchangeable pairs to bound Wasserstein distance, we provide the first non-asymptotic results for \sgdwor when applied to {\em general smooth, strongly-convex} functions. In particular, we show that \sgdwor converges at a rate of $O(1/K^2)$ while \sgd is known to converge at $O(1/K)$ rate, where $K$ denotes the number of passes over data and is required to be {\em large enough}. Existing results for \sgdwor in this setting require additional {\em Hessian Lipschitz assumption} \cite{gurbuzbalaban2015random,haochen2018random}. For {\em small} $K$, we show \sgdwor can achieve same convergence rate as \sgd for {\em general smooth strongly-convex} functions. Existing results in this setting require $K=1$ and hold only for generalized linear models \cite{shamir2016without}. In addition, by careful analysis of the coupling, for both large and small $K$, we obtain better dependence on problem dependent parameters like condition number.
Deep learning based pulse shape discrimination for germanium detectors
Authors: P. Holl, L. Hauertmann, B. Majorovits, O. Schulz, M. Schuster, A. J. Zsigmond
Date: 2019-03-04 00:00:00
Tasks: Deep Learning, General Classification

Experiments searching for rare processes like neutrinoless double beta decay heavily rely on the identification of background events to reduce their background level and increase their sensitivity. We present a novel machine learning based method to recognize one of the most abundant classes of background events in these experiments. By combining a neural network for feature extraction with a smaller classification network, our method can be trained with only a small number of labeled events. To validate our method, we use signals from a broad-energy germanium detector irradiated with a $^{228}$Th gamma source. We find that it matches the performance of state-of-the-art algorithms commonly used for this detector type. However, it requires less tuning and calibration and shows potential to identify certain types of background events missed by other methods.
The Fuzzy ROC
Authors: Giovanni Parmigiani
Date: 2019-03-04 00:00:00
Tasks: Sensitivity, Specificity

The fuzzy ROC extends Receiver Operating Curve (ROC) visualization to the
situation where some data points, falling in an indeterminacy region, are not
classified. It addresses two challenges: definition of sensitivity and
specificity bounds under indeterminacy; and visual summarization of the large
number of possibilities arising from different choices of indeterminacy zones.
Hedging the Drift: Learning to Optimize under Non-Stationarity
Authors: Wang Chi Cheung, David Simchi-Levi, Ruihao Zhu
Date: 2019-03-04 00:00:00
Tasks: Decision Making

We introduce data-driven decision-making algorithms that achieve state-of-the-art \emph{dynamic regret} bounds for non-stationary bandit settings. These settings capture applications such as advertisement allocation, dynamic pricing, and traffic network routing in changing environments. We show how the difficulty posed by the (unknown \emph{a priori} and possibly adversarial) non-stationarity can be overcome by an unconventional marriage between stochastic and adversarial bandit learning algorithms. Our main contribution is a general algorithmic recipe for a wide variety of non-stationary bandit problems. Specifically, we design and analyze the sliding window-upper confidence bound algorithm that achieves the optimal dynamic regret bound for each of the settings when we know the respective underlying \emph{variation budget}, which quantifies the total amount of temporal variation of the latent environments. Boosted by the novel bandit-over-bandit framework that adapts to the latent changes, we can further enjoy the (nearly) optimal dynamic regret bounds in a (surprisingly) parameter-free manner. In addition to the classical exploration-exploitation trade-off, our algorithms leverage the power of the "forgetting principle" in the learning processes, which is vital in changing environments. Our extensive numerical experiments on both synthetic and real world online auto-loan datasets show that our proposed algorithms achieve superior empirical performance compared to existing algorithms.
Deep Learning for Cognitive Neuroscience
Authors: Katherine R. Storrs, Nikolaus Kriegeskorte
Date: 2019-03-04 00:00:00
Tasks: Deep Learning

Neural network models can now recognise images, understand text, translate
languages, and play many human games at human or superhuman levels. These
systems are highly abstracted, but are inspired by biological brains and use
only biologically plausible computations. In the coming years, neural networks
are likely to become less reliant on learning from massive labelled datasets,
and more robust and generalisable in their task performance. From their
successes and failures, we can learn about the computational requirements of
the different tasks at which brains excel. Deep learning also provides the
tools for testing cognitive theories. In order to test a theory, we need to
realise the proposed information-processing system at scale, so as to be able
to assess its feasibility and emergent behaviours. Deep learning allows us to
scale up from principles and circuit models to end-to-end trainable models
capable of performing complex tasks. There are many levels at which cognitive
neuroscientists can use deep learning in their work, from inspiring theories to
serving as full computational models. Ongoing advances in deep learning bring
us closer to understanding how cognition and perception may be implemented in
the brain -- the grand challenge at the core of cognitive neuroscience.
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Authors: Hoda Heidari, Vedant Nanda, Krishna P. Gummadi
Date: 2019-03-04 00:00:00
Tasks: Decision Making, Fairness, Sociology

Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population. We take a broader perspective on algorithmic fairness. We propose an effort-based measure of fairness and present a data-driven framework for characterizing the long-term impact of algorithmic policies on reshaping the underlying population. Motivated by the psychological literature on \emph{social learning} and the economic literature on equality of opportunity, we propose a micro-scale model of how individuals may respond to decision-making algorithms. We employ existing measures of segregation from sociology and economics to quantify the resulting macro-scale population-level change. Importantly, we observe that different models may shift the group-conditional distribution of qualifications in different directions. Our findings raise a number of important questions regarding the formalization of fairness for decision-making models.
Two-phase protein folding optimization on a three-dimensional AB off-lattice model
Authors: Borko Bošković, Janez Brest
Date: 2019-03-04 00:00:00
Tasks: Protein Folding

This paper presents a two-phase protein folding optimization on a three-dimensional AB off-lattice model. The first phase is responsible for forming conformations with a good hydrophobic core or a set of compact hydrophobic amino acid positions. These conformations are forwarded to the second phase, where an accurate search is performed with the aim of locating conformations with the best energy value. The optimization process switches between these two phases until the stopping condition is satisfied. An auxiliary fitness function was designed for the first phase, while the original fitness function is used in the second phase. The auxiliary fitness function includes an expression about the quality of the hydrophobic core. This expression is crucial for leading the search process to the promising solutions that have a good hydrophobic core and, consequently, improves the efficiency of the whole optimization process. Our differential evolution algorithm was used for demonstrating the efficiency of two-phase optimization. It was analyzed on well-known amino acid sequences that are used frequently in the literature. The obtained experimental results show that the employed two-phase optimization improves the efficiency of our algorithm significantly and that the proposed algorithm is superior to other state-of-the-art algorithms.