pwc-archive/papers-with-abstracts huggingface dataset:
Inverse Cooking: Recipe Generation from Food Images
Authors: Amaia Salvador, Michal Drozdzal, Xavier Giro-i-Nieto, Adriana Romero
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Recipe Generation, Retrieval

People enjoy food photography because they appreciate food. Behind each meal there is a story described in a complex recipe and, unfortunately, by simply looking at a food image we do not have access to its preparation process. Therefore, in this paper we introduce an inverse cooking system that recreates cooking recipes given food images. Our system predicts ingredients as sets by means of a novel architecture, modeling their dependencies without imposing any order, and then generates cooking instructions by attending to both image and its inferred ingredients simultaneously. We extensively evaluate the whole system on the large-scale Recipe1M dataset and show that (1) we improve performance w.r.t. previous baselines for ingredient prediction; (2) we are able to obtain high quality recipes by leveraging both image and ingredients; (3) our system is able to produce more compelling recipes than retrieval-based approaches according to human judgment. We make code and models publicly available.
An Empirical Model of Large-Batch Training
Authors: Sam McCandlish, Jared Kaplan, Dario Amodei, OpenAI Dota Team
Date: 2018-12-14 00:00:00
Tasks: Dota 2, model, Reinforcement Learning

In an increasing number of domains it has been demonstrated that deep
learning models can be trained using relatively large batch sizes without
sacrificing data efficiency. However the limits of this massive data
parallelism seem to differ from domain to domain, ranging from batches of tens
of thousands in ImageNet to batches of millions in RL agents that play the game
Dota 2. To our knowledge there is limited conceptual understanding of why these
limits to batch size differ or how we might choose the correct batch size in a
new domain. In this paper, we demonstrate that a simple and easy-to-measure
statistic called the gradient noise scale predicts the largest useful batch
size across many domains and applications, including a number of supervised
learning datasets (MNIST, SVHN, CIFAR-10, ImageNet, Billion Word),
reinforcement learning domains (Atari and Dota), and even generative model
training (autoencoders on SVHN). We find that the noise scale increases as the
loss decreases over a training run and depends on the model size primarily
through improved model performance. Our empirically-motivated theory also
describes the tradeoff between compute-efficiency and time-efficiency, and
provides a rough model of the benefits of adaptive batch-size training.
Specification-Guided Safety Verification for Feedforward Neural Networks
Authors: Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson
Date: 2018-12-14 00:00:00

This paper presents a specification-guided safety verification method for
feedforward neural networks with general activation functions. As such
feedforward networks are memoryless, they can be abstractly represented as
mathematical functions, and the reachability analysis of the neural network
amounts to interval analysis problems. In the framework of interval analysis, a
computationally efficient formula which can quickly compute the output interval
sets of a neural network is developed. Then, a specification-guided
reachability algorithm is developed. Specifically, the bisection process in the
verification algorithm is completely guided by a given safety specification.
Due to the employment of the safety specification, unnecessary computations are
avoided and thus the computational cost can be reduced significantly.
Experiments show that the proposed method enjoys much more efficiency in safety
verification with significantly less computational cost.
Few-shot classification in Named Entity Recognition Task
Authors: Alexander Fritzler, Varvara Logacheva, Maksim Kretov
Date: 2018-12-14 00:00:00
Tasks: Classification, General Classification, Meta-Learning, Metric Learning, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning, Zero-Shot Learning

For many natural language processing (NLP) tasks the amount of annotated data
is limited. This urges a need to apply semi-supervised learning techniques,
such as transfer learning or meta-learning. In this work we tackle Named Entity
Recognition (NER) task using Prototypical Network - a metric learning
technique. It learns intermediate representations of words which cluster well
into named entity classes. This property of the model allows classifying words
with extremely limited number of training examples, and can potentially be used
as a zero-shot learning method. By coupling this technique with transfer
learning we achieve well-performing classifiers trained on only 20 instances of
a target class.
A Parametric Top-View Representation of Complex Road Scenes
Authors: Ziyan Wang, Buyu Liu, Samuel Schulter, Manmohan Chandraker
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Decision Making, Domain Adaptation

In this paper, we address the problem of inferring the layout of complex road
scenes given a single camera as input. To achieve that, we first propose a
novel parameterized model of road layouts in a top-view representation, which
is not only intuitive for human visualization but also provides an
interpretable interface for higher-level decision making. Moreover, the design
of our top-view scene model allows for efficient sampling and thus generation
of large-scale simulated data, which we leverage to train a deep neural network
to infer our scene model's parameters. Specifically, our proposed training
procedure uses supervised domain-adaptation techniques to incorporate both
simulated as well as manually annotated data. Finally, we design a Conditional
Random Field (CRF) that enforces coherent predictions for a single frame and
encourages temporal smoothness among video frames. Experiments on two public
data sets show that: (1) Our parametric top-view model is representative enough
to describe complex road scenes; (2) The proposed method outperforms baselines
trained on manually-annotated or simulated data only, thus getting the best of
both; (3) Our CRF is able to generate temporally smoothed while semantically
meaningful results.
Siamese Cascaded Region Proposal Networks for Real-Time Visual Tracking
Authors: Heng Fan, Haibin Ling
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Real-Time Visual Tracking, Region Proposal, Visual Tracking

Region proposal networks (RPN) have been recently combined with the Siamese
network for tracking, and shown excellent accuracy with high efficiency.
Nevertheless, previously proposed one-stage Siamese-RPN trackers degenerate in
presence of similar distractors and large scale variation. Addressing these
issues, we propose a multi-stage tracking framework, Siamese Cascaded RPN
(C-RPN), which consists of a sequence of RPNs cascaded from deep high-level to
shallow low-level layers in a Siamese network. Compared to previous solutions,
C-RPN has several advantages: (1) Each RPN is trained using the outputs of RPN
in the previous stage. Such process stimulates hard negative sampling,
resulting in more balanced training samples. Consequently, the RPNs are
sequentially more discriminative in distinguishing difficult background (i.e.,
similar distractors). (2) Multi-level features are fully leveraged through a
novel feature transfer block (FTB) for each RPN, further improving the
discriminability of C-RPN using both high-level semantic and low-level spatial
information. (3) With multiple steps of regressions, C-RPN progressively
refines the location and shape of the target in each RPN with adjusted anchor
boxes in the previous stage, which makes localization more accurate. C-RPN is
trained end-to-end with the multi-task loss function. In inference, C-RPN is
deployed as it is, without any temporal adaption, for real-time tracking. In
extensive experiments on OTB-2013, OTB-2015, VOT-2016, VOT-2017, LaSOT and
TrackingNet, C-RPN consistently achieves state-of-the-art results and runs in
real-time.
Improving the Performance of Unimodal Dynamic Hand-Gesture Recognition with Multimodal Training
Authors: Mahdi Abavisani, Hamid Reza Vaezi Joze, Vishal M. Patel
Date: 2018-12-14 00:00:00
Proceeding: CVPR 2019 6
Tasks: Action Recognition, Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition, Transfer Learning

We present an efficient approach for leveraging the knowledge from multiple modalities in training unimodal 3D convolutional neural networks (3D-CNNs) for the task of dynamic hand gesture recognition. Instead of explicitly combining multimodal information, which is commonplace in many state-of-the-art methods, we propose a different framework in which we embed the knowledge of multiple modalities in individual networks so that each unimodal network can achieve an improved performance. In particular, we dedicate separate networks per available modality and enforce them to collaborate and learn to develop networks with common semantics and better representations. We introduce a "spatiotemporal semantic alignment" loss (SSA) to align the content of the features from different networks. In addition, we regularize this loss with our proposed "focal regularization parameter" to avoid negative knowledge transfer. Experimental results show that our framework improves the test time recognition accuracy of unimodal networks, and provides the state-of-the-art performance on various dynamic hand gesture recognition datasets.
Bias Mitigation Post-processing for Individual and Group Fairness
Authors: Pranay K. Lohia, Karthikeyan Natesan Ramamurthy, Manish Bhide, Diptikalyan Saha, Kush R. Varshney, Ruchir Puri
Date: 2018-12-14 00:00:00
Tasks: Fairness, General Classification

Whereas previous post-processing approaches for increasing the fairness of
predictions of biased classifiers address only group fairness, we propose a
method for increasing both individual and group fairness. Our novel framework
includes an individual bias detector used to prioritize data samples in a bias
mitigation algorithm aiming to improve the group fairness measure of disparate
impact. We show superior performance to previous work in the combination of
classification accuracy, individual fairness and group fairness on several
real-world datasets in applications such as credit, employment, and criminal
justice.
Federated Optimization in Heterogeneous Networks
Authors: Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, Virginia Smith
Date: 2018-12-14 00:00:00
Tasks: Distributed Optimization, Federated Learning

Federated Learning is a distributed learning paradigm with two key challenges that differentiate it from traditional distributed optimization: (1) significant variability in terms of the systems characteristics on each device in the network (systems heterogeneity), and (2) non-identically distributed data across the network (statistical heterogeneity). In this work, we introduce a framework, FedProx, to tackle heterogeneity in federated networks. FedProx can be viewed as a generalization and re-parametrization of FedAvg, the current state-of-the-art method for federated learning. While this re-parameterization makes only minor modifications to the method itself, these modifications have important ramifications both in theory and in practice. Theoretically, we provide convergence guarantees for our framework when learning over data from non-identical distributions (statistical heterogeneity), and while adhering to device-level systems constraints by allowing each participating device to perform a variable amount of work (systems heterogeneity). Practically, we demonstrate that FedProx allows for more robust convergence than FedAvg across a suite of realistic federated datasets. In particular, in highly heterogeneous settings, FedProx demonstrates significantly more stable and accurate convergence behavior relative to FedAvg---improving absolute test accuracy by 22% on average.
Axially-shifted pattern illumination for macroscale turbidity suppression and virtual volumetric confocal imaging without axial scanning
Authors: Shaowei Jiang, Jun Liao, Zichao Bian, Pengming Song, Garrett Soler, Kazunori Hoshino, Guoan Zheng
Date: 2018-12-14 00:00:00

Structured illumination has been widely used for optical sectioning and 3D
surface recovery. In a typical implementation, multiple images under
non-uniform pattern illumination are used to recover a single object section.
Axial scanning of the sample or the objective lens is needed for acquiring the
3D volumetric data. Here we demonstrate the use of axially-shifted pattern
illumination (asPI) for virtual volumetric confocal imaging without axial
scanning. In the reported approach, we project illumination patterns at a
tilted angle with respect to the detection optics. As such, the illumination
patterns shift laterally at different z sections and the sample information at
different z-sections can be recovered based on the captured 2D images. We
demonstrate the reported approach for virtual confocal imaging through a
diffusing layer and underwater 3D imaging through diluted milk. We show that we
can acquire the entire confocal volume in ~1s with a throughput of 420
megapixels per second. Our approach may provide new insights for developing
confocal light ranging and detection systems in degraded visual environments.
Simulation to Scaled City: Zero-Shot Policy Transfer for Traffic Control via Autonomous Vehicles
Authors: Kathy Jang, Eugene Vinitsky, Behdad Chalaki, Ben Remer, Logan Beaver, Andreas Malikopoulos, Alexandre Bayen
Date: 2018-12-14 00:00:00
Tasks: Autonomous Vehicles, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Using deep reinforcement learning, we train control policies for autonomous
vehicles leading a platoon of vehicles onto a roundabout. Using Flow, a library
for deep reinforcement learning in micro-simulators, we train two policies, one
policy with noise injected into the state and action space and one without any
injected noise. In simulation, the autonomous vehicle learns an emergent
metering behavior for both policies in which it slows to allow for smoother
merging. We then directly transfer this policy without any tuning to the
University of Delaware Scaled Smart City (UDSSC), a 1:25 scale testbed for
connected and automated vehicles. We characterize the performance of both
policies on the scaled city. We show that the noise-free policy winds up
crashing and only occasionally metering. However, the noise-injected policy
consistently performs the metering behavior and remains collision-free,
suggesting that the noise helps with the zero-shot policy transfer.
Additionally, the transferred, noise-injected policy leads to a 5% reduction of
average travel time and a reduction of 22% in maximum travel time in the UDSSC.
Videos of the controllers can be found at
https://sites.google.com/view/iccps-policy-transfer.
Dopamine: A Research Framework for Deep Reinforcement Learning
Authors: Pablo Samuel Castro, Subhodeep Moitra, Carles Gelada, Saurabh Kumar, Marc G. Bellemare
Date: 2018-12-14 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dopamine is a research framework for fast prototyping of reinforcement learning algorithms.
Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules
Authors: Michael Rapp, Eneldo Loza Mencía, Johannes Fürnkranz
Date: 2018-12-14 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Exploiting dependencies between labels is considered to be crucial for
multi-label classification. Rules are able to expose label dependencies such as
implications, subsumptions or exclusions in a human-comprehensible and
interpretable manner. However, the induction of rules with multiple labels in
the head is particularly challenging, as the number of label combinations which
must be taken into account for each rule grows exponentially with the number of
available labels. To overcome this limitation, algorithms for exhaustive rule
mining typically use properties such as anti-monotonicity or decomposability in
order to prune the search space. In the present paper, we examine whether
commonly used multi-label evaluation metrics satisfy these properties and
therefore are suited to prune the search space for multi-label heads.
Community structure: A comparative evaluation of community detection methods
Authors: Vinh-Loc Dao, Cécile Bothorel, Philippe Lenca
Date: 2018-12-14 00:00:00
Tasks: Community Detection, Navigate

Discovering community structure in complex networks is a mature field since a tremendous number of community detection methods have been introduced in the literature. Nevertheless, it is still very challenging for practioners to determine which method would be suitable to get insights into the structural information of the networks they study. Many recent efforts have been devoted to investigating various quality scores of the community structure, but the problem of distinguishing between different types of communities is still open. In this paper, we propose a comparative, extensive and empirical study to investigate what types of communities many state-of-the-art and well-known community detection methods are producing. Specifically, we provide comprehensive analyses on computation time, community size distribution, a comparative evaluation of methods according to their optimisation schemes as well as a comparison of their partioning strategy through validation metrics. We process our analyses on a very large corpus of hundreds of networks from five different network categories and propose ways to classify community detection methods, helping a potential user to navigate the complex landscape of community detection.
Semi-Supervised Monaural Singing Voice Separation With a Masking Network Trained on Synthetic Mixtures
Authors: Michael Michelashvili, Sagie Benaim, Lior Wolf
Date: 2018-12-14 00:00:00
Tasks: Music Source Separation, Speech Separation

We study the problem of semi-supervised singing voice separation, in which the training data contains a set of samples of mixed music (singing and instrumental) and an unmatched set of instrumental music. Our solution employs a single mapping function g, which, applied to a mixed sample, recovers the underlying instrumental music, and, applied to an instrumental sample, returns the same sample. The network g is trained using purely instrumental samples, as well as on synthetic mixed samples that are created by mixing reconstructed singing voices with random instrumental samples. Our results indicate that we are on a par with or better than fully supervised methods, which are also provided with training samples of unmixed singing voices, and are better than other recent semi-supervised methods.
An IoT Analytics Embodied Agent Model based on Context-Aware Machine Learning
Authors: Nathalia Nascimento, Paulo Alencar, Carlos Lucena, Donald Cowan
Date: 2018-12-14 00:00:00
Tasks: BIG-bench Machine Learning

Agent-based Internet of Things (IoT) applications have recently emerged as
applications that can involve sensors, wireless devices, machines and software
that can exchange data and be accessed remotely. Such applications have been
proposed in several domains including health care, smart cities and
agriculture. However, despite their increased adoption, deploying these
applications in specific settings has been very challenging because of the
complex static and dynamic variability of the physical devices such as sensors
and actuators, the software application behavior and the environment in which
the application is embedded. In this paper, we propose a modeling approach for
IoT analytics based on learning embodied agents (i.e. situated agents). The
approach involves: (i) a variability model of IoT embodied agents; (ii)
feedback evaluative machine learning; and (iii) reconfiguration of a group of
agents in accordance with environmental context. The proposed approach advances
the state of the art in that it facilitates the development of Agent-based IoT
applications by explicitly capturing their complex and dynamic variabilities
and supporting their self-configuration based on an context-aware and machine
learning-based approach.
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding
Authors: JIhwan Lee, Dongchan Kim, Ruhi Sarikaya, Young-Bum Kim
Date: 2018-12-13 00:00:00
Tasks: Representation Learning, Reranking, Spoken Language Understanding

Representation learning is an essential problem in a wide range of
applications and it is important for performing downstream tasks successfully.
In this paper, we propose a new model that learns coupled representations of
domains, intents, and slots by taking advantage of their hierarchical
dependency in a Spoken Language Understanding system. Our proposed model learns
the vector representation of intents based on the slots tied to these intents
by aggregating the representations of the slots. Similarly, the vector
representation of a domain is learned by aggregating the representations of the
intents tied to a specific domain. To the best of our knowledge, it is the
first approach to jointly learning the representations of domains, intents, and
slots using their hierarchical relationships. The experimental results
demonstrate the effectiveness of the representations learned by our model, as
evidenced by improved performance on the contextual cross-domain reranking
task.
Feedback alignment in deep convolutional networks
Authors: Theodore H. Moskovitz, Ashok Litwin-Kumar, L. F. Abbott
Date: 2018-12-12 00:00:00

Ongoing studies have identified similarities between neural representations in biological networks and in deep artificial neural networks. This has led to renewed interest in developing analogies between the backpropagation learning algorithm used to train artificial networks and the synaptic plasticity rules operative in the brain. These efforts are challenged by biologically implausible features of backpropagation, one of which is a reliance on symmetric forward and backward synaptic weights. A number of methods have been proposed that do not rely on weight symmetry but, thus far, these have failed to scale to deep convolutional networks and complex data. We identify principal obstacles to the scalability of such algorithms and introduce several techniques to mitigate them. We demonstrate that a modification of the feedback alignment method that enforces a weaker form of weight symmetry, one that requires agreement of weight sign but not magnitude, can achieve performance competitive with backpropagation. Our results complement those of Bartunov et al. (2018) and Xiao et al. (2018b) and suggest that mechanisms that promote alignment of feedforward and feedback weights are critical for learning in deep networks.
SMART: An Open Source Data Labeling Platform for Supervised Learning
Authors: Rob Chew, Michael Wenger, Caroline Kery, Jason Nance, Keith Richards, Emily Hadley, Peter Baumgartner
Date: 2018-12-11 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

SMART is an open source web application designed to help data scientists and
research teams efficiently build labeled training data sets for supervised
machine learning tasks. SMART provides users with an intuitive interface for
creating labeled data sets, supports active learning to help reduce the
required amount of labeled data, and incorporates inter-rater reliability
statistics to provide insight into label quality. SMART is designed to be
platform agnostic and easily deployable to meet the needs of as many different
research teams as possible. The project website contains links to the code
repository and extensive user documentation.