pwc-archive/papers-with-abstracts huggingface dataset:
SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation
Authors: Weiyue Wang, Ronald Yu, Qiangui Huang, Ulrich Neumann
Date: 2017-11-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Instance Segmentation, 3D Object Detection, 3D Part Segmentation, 3D Semantic Instance Segmentation, Instance Segmentation, object-detection, Object Detection, Scene Segmentation, Segmentation, Semantic Segmentation

We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. To the best of our knowledge, SGPN is the first framework to learn 3D instance-aware semantic segmentation on point clouds. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance.
Hyperbolic Representation Learning for Fast and Efficient Neural Question Answering
Authors: Yi Tay, Luu Anh Tuan, Siu Cheung Hui
Date: 2017-07-25 00:00:00
Tasks: Efficient Neural Network, Feature Engineering, Question Answering, Representation Learning, Retrieval

The dominant neural architectures in question answer retrieval are based on
recurrent or convolutional encoders configured with complex word matching
layers. Given that recent architectural innovations are mostly new word
interaction layers or attention-based matching mechanisms, it seems to be a
well-established fact that these components are mandatory for good performance.
Unfortunately, the memory and computation cost incurred by these complex
mechanisms are undesirable for practical applications. As such, this paper
tackles the question of whether it is possible to achieve competitive
performance with simple neural architectures. We propose a simple but novel
deep learning architecture for fast and efficient question-answer ranking and
retrieval. More specifically, our proposed model, \textsc{HyperQA}, is a
parameter efficient neural network that outperforms other parameter intensive
models such as Attentive Pooling BiLSTMs and Multi-Perspective CNNs on multiple
QA benchmarks. The novelty behind \textsc{HyperQA} is a pairwise ranking
objective that models the relationship between question and answer embeddings
in Hyperbolic space instead of Euclidean space. This empowers our model with a
self-organizing ability and enables automatic discovery of latent hierarchies
while learning embeddings of questions and answers. Our model requires no
feature engineering, no similarity matrix matching, no complicated attention
mechanisms nor over-parameterized layers and yet outperforms and remains
competitive to many models that have these functionalities on multiple
benchmarks.
Multi-Modal Imitation Learning from Unstructured Demonstrations using Generative Adversarial Nets
Authors: Karol Hausman, Yevgen Chebotar, Stefan Schaal, Gaurav Sukhatme, Joseph Lim
Date: 2017-05-30 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Imitation Learning

Imitation learning has traditionally been applied to learn a single task from
demonstrations thereof. The requirement of structured and isolated
demonstrations limits the scalability of imitation learning approaches as they
are difficult to apply to real-world scenarios, where robots have to be able to
execute a multitude of tasks. In this paper, we propose a multi-modal imitation
learning framework that is able to segment and imitate skills from unlabelled
and unstructured demonstrations by learning skill segmentation and imitation
learning jointly. The extensive simulation results indicate that our method can
efficiently separate the demonstrations into individual skills and learn to
imitate them using a single multi-modal policy. The video of our experiments is
available at http://sites.google.com/view/nips17intentiongan
Knowledge Concentration: Learning 100K Object Classifiers in a Single CNN
Authors: Jiyang Gao, Zijian, Guo, Zhen Li, Ram Nevatia
Date: 2017-11-21 00:00:00
Tasks: General Classification, image-classification, Image Classification, Knowledge Distillation

Fine-grained image labels are desirable for many computer vision
applications, such as visual search or mobile AI assistant. These applications
rely on image classification models that can produce hundreds of thousands
(e.g. 100K) of diversified fine-grained image labels on input images. However,
training a network at this vocabulary scale is challenging, and suffers from
intolerable large model size and slow training speed, which leads to
unsatisfying classification performance. A straightforward solution would be
training separate expert networks (specialists), with each specialist focusing
on learning one specific vertical (e.g. cars, birds...). However, deploying
dozens of expert networks in a practical system would significantly increase
system complexity and inference latency, and consumes large amounts of
computational resources. To address these challenges, we propose a Knowledge
Concentration method, which effectively transfers the knowledge from dozens of
specialists (multiple teacher networks) into one single model (one student
network) to classify 100K object categories. There are three salient aspects in
our method: (1) a multi-teacher single-student knowledge distillation
framework; (2) a self-paced learning mechanism to allow the student to learn
from different teachers at various paces; (3) structurally connected layers to
expand the student network capacity with limited extra parameters. We validate
our method on OpenImage and a newly collected dataset, Entity-Foto-Tree (EFT),
with 100K categories, and show that the proposed model performs significantly
better than the baseline generalist model.
A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
Authors: Jingjing Xu, Ji Wen, Xu sun, Qi Su
Date: 2017-11-19 00:00:00
Tasks: Articles, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation, Relation Extraction

Named Entity Recognition and Relation Extraction for Chinese literature text is regarded as the highly difficult problem, partially because of the lack of tagging sets. In this paper, we build a discourse-level dataset from hundreds of Chinese literature articles for improving this task. To build a high quality dataset, we propose two tagging methods to solve the problem of data inconsistency, including a heuristic tagging method and a machine auxiliary tagging method. Based on this corpus, we also introduce several widely used models to conduct experiments. Experimental results not only show the usefulness of the proposed dataset, but also provide baselines for further research. The dataset is available at https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
Geometric Cross-Modal Comparison of Heterogeneous Sensor Data
Authors: Christopher J. Tralie, Abraham Smith, Nathan Borggren, Jay Hineman, Paul Bendich, Peter Zulch, John Harer
Date: 2017-11-23 00:00:00

In this work, we address the problem of cross-modal comparison of aerial data
streams. A variety of simulated automobile trajectories are sensed using two
different modalities: full-motion video, and radio-frequency (RF) signals
received by detectors at various locations. The information represented by the
two modalities is compared using self-similarity matrices (SSMs) corresponding
to time-ordered point clouds in feature spaces of each of these data sources;
we note that these feature spaces can be of entirely different scale and
dimensionality. Several metrics for comparing SSMs are explored, including a
cutting-edge time-warping technique that can simultaneously handle local time
warping and partial matches, while also controlling for the change in geometry
between feature spaces of the two modalities. We note that this technique is
quite general, and does not depend on the choice of modalities. In this
particular setting, we demonstrate that the cross-modal distance between SSMs
corresponding to the same trajectory type is smaller than the cross-modal
distance between SSMs corresponding to distinct trajectory types, and we
formalize this observation via precision-recall metrics in experiments.
Finally, we comment on promising implications of these ideas for future
integration into multiple-hypothesis tracking systems.
Voltage-Driven Domain-Wall Motion based Neuro-Synaptic Devices for Dynamic On-line Learning
Authors: Akhilesh Jaiswal, Amogh Agrawal, Priyadarshini Panda, Kaushik Roy
Date: 2017-05-19 00:00:00
Tasks: Lifelong learning, speech-recognition, Speech Recognition

Conventional von-Neumann computing models have achieved remarkable feats for
the past few decades. However, they fail to deliver the required efficiency for
certain basic tasks like image and speech recognition when compared to
biological systems. As such, taking cues from biological systems, novel
computing paradigms are being explored for efficient hardware implementations
of recognition/classification tasks. The basic building blocks of such
neuromorphic systems are neurons and synapses. Towards that end, we propose a
leaky-integrate-fire (LIF) neuron and a programmable non-volatile synapse using
domain wall motion induced by magneto-electric effect. Due to a strong elastic
pinning between the ferro-magnetic domain wall (FM-DW) and the underlying
ferro-electric domain wall (FE-DW), the FM-DW gets dragged by the FE-DW on
application of a voltage pulse. The fact that FE materials are insulators
allows for pure voltage-driven FM-DW motion, which in turn can be used to mimic
the behaviors of biological spiking neurons and synapses. The voltage driven
nature of the proposed devices allows energy-efficient operation. A detailed
device to system level simulation framework based on micromagnetic simulations
has been developed to analyze the feasibility of the proposed neuro-synaptic
devices. We also demonstrate that the energy-efficient voltage-controlled
behavior of the proposed devices make them suitable for dynamic on-line and
lifelong learning in spiking neural networks (SNNs).
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
Authors: Longhui Wei, Shiliang Zhang, Wen Gao, Qi Tian
Date: 2017-11-23 00:00:00
Proceeding: CVPR 2018 6
Tasks: Generative Adversarial Network, Person Re-Identification, Unsupervised Domain Adaptation

Although the performance of person Re-Identification (ReID) has been
significantly boosted, many challenging issues in real scenarios have not been
fully investigated, e.g., the complex scenes and lighting variations, viewpoint
and pose changes, and the large number of identities in a camera network. To
facilitate the research towards conquering those issues, this paper contributes
a new dataset called MSMT17 with many important features, e.g., 1) the raw
videos are taken by an 15-camera network deployed in both indoor and outdoor
scenes, 2) the videos cover a long period of time and present complex lighting
variations, and 3) it contains currently the largest number of annotated
identities, i.e., 4,101 identities and 126,441 bounding boxes. We also observe
that, domain gap commonly exists between datasets, which essentially causes
severe performance drop when training and testing on different datasets. This
results in that available training data cannot be effectively leveraged for new
testing domains. To relieve the expensive costs of annotating new training
samples, we propose a Person Transfer Generative Adversarial Network (PTGAN) to
bridge the domain gap. Comprehensive experiments show that the domain gap could
be substantially narrowed-down by the PTGAN.
Light-Head R-CNN: In Defense of Two-Stage Object Detector
Authors: Zeming Li, Chao Peng, Gang Yu, Xiangyu Zhang, Yangdong Deng, Jian Sun
Date: 2017-11-20 00:00:00
Tasks: Vocal Bursts Valence Prediction

In this paper, we first investigate why typical two-stage methods are not as
fast as single-stage, fast detectors like YOLO and SSD. We find that Faster
R-CNN and R-FCN perform an intensive computation after or before RoI warping.
Faster R-CNN involves two fully connected layers for RoI recognition, while
R-FCN produces a large score maps. Thus, the speed of these networks is slow
due to the heavy-head design in the architecture. Even if we significantly
reduce the base model, the computation cost cannot be largely decreased
accordingly.
  We propose a new two-stage detector, Light-Head R-CNN, to address the
shortcoming in current two-stage approaches. In our design, we make the head of
network as light as possible, by using a thin feature map and a cheap R-CNN
subnet (pooling and single fully-connected layer). Our ResNet-101 based
light-head R-CNN outperforms state-of-art object detectors on COCO while
keeping time efficiency. More importantly, simply replacing the backbone with a
tiny network (e.g, Xception), our Light-Head R-CNN gets 30.7 mmAP at 102 FPS on
COCO, significantly outperforming the single-stage, fast detectors like YOLO
and SSD on both speed and accuracy. Code will be made publicly available.
Multiple component decomposition from millimeter single-channel data
Authors: Iván Rodríguez-Montoya, David Sánchez-Argüelles, Itziar Aretxaga, Emanuele Bertone, Miguel Chávez-Dagostino, David H. Hughes, Alfredo Montaña, Grant W. Wilson, Milagros Zeballos
Date: 2017-11-23 00:00:00
Tasks: blind source separation, Survey

We present an implementation of a blind source separation algorithm to remove
foregrounds off millimeter surveys made by single-channel instruments. In order
to make possible such a decomposition over single-wavelength data: we generate
levels of artificial redundancy, then perform a blind decomposition, calibrate
the resulting maps, and lastly measure physical information. We simulate the
reduction pipeline using mock data: atmospheric fluctuations, extended
astrophysical foregrounds, and point-like sources, but we apply the same
methodology to the AzTEC/ASTE survey of the Great Observatories Origins Deep
Survey-South (GOODS-S). In both applications, our technique robustly decomposes
redundant maps into their underlying components, reducing flux bias, improving
signal-to-noise, and minimizing information loss. In particular, the GOODS-S
survey is decomposed into four independent physical components, one of them is
the already known map of point sources, two are atmospheric and systematic
foregrounds, and the fourth component is an extended emission that can be
interpreted as the confusion background of faint sources.
No Classification without Representation: Assessing Geodiversity Issues in Open Data Sets for the Developing World
Authors: Shreya Shankar, Yoni Halpern, Eric Breck, James Atwood, Jimbo Wilson, D. Sculley
Date: 2017-11-22 00:00:00
Tasks: Diversity, General Classification

Modern machine learning systems such as image classifiers rely heavily on
large scale data sets for training. Such data sets are costly to create, thus
in practice a small number of freely available, open source data sets are
widely used. We suggest that examining the geo-diversity of open data sets is
critical before adopting a data set for use cases in the developing world. We
analyze two large, publicly available image data sets to assess geo-diversity
and find that these data sets appear to exhibit an observable amerocentric and
eurocentric representation bias. Further, we analyze classifiers trained on
these data sets to assess the impact of these training distributions and find
strong differences in the relative performance on images from different
locales. These results emphasize the need to ensure geo-representation when
constructing data sets for use in the developing world.
Inductive Representation Learning in Large Attributed Graphs
Authors: Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
Date: 2017-10-25 00:00:00
Tasks: Anomaly Detection, Attribute, Link Prediction, Representation Learning

Graphs (networks) are ubiquitous and allow us to model entities (nodes) and
the dependencies (edges) between them. Learning a useful feature representation
from graph data lies at the heart and success of many machine learning tasks
such as classification, anomaly detection, link prediction, among many others.
Many existing techniques use random walks as a basis for learning features or
estimating the parameters of a graph model for a downstream prediction task.
Examples include recent node embedding methods such as DeepWalk, node2vec, as
well as graph-based deep learning algorithms. However, the simple random walk
used by these methods is fundamentally tied to the identity of the node. This
has three main disadvantages. First, these approaches are inherently
transductive and do not generalize to unseen nodes and other graphs. Second,
they are not space-efficient as a feature vector is learned for each node which
is impractical for large graphs. Third, most of these approaches lack support
for attributed graphs.
  To make these methods more generally applicable, we propose a framework for
inductive network representation learning based on the notion of attributed
random walk that is not tied to node identity and is instead based on learning
a function $\Phi : \mathrm{\rm \bf x} \rightarrow w$ that maps a node attribute
vector $\mathrm{\rm \bf x}$ to a type $w$. This framework serves as a basis for
generalizing existing methods such as DeepWalk, node2vec, and many other
previous methods that leverage traditional random walks.
The De-Biased Whittle Likelihood
Authors: Adam M. Sykulski, Sofia C. Olhede, Arthur P. Guillaumin, Jonathan M. Lilly, Jeffrey J. Early
Date: 2016-05-22 00:00:00

The Whittle likelihood is a widely used and computationally efficient
pseudo-likelihood. However, it is known to produce biased parameter estimates
for large classes of models. We propose a method for de-biasing Whittle
estimates for second-order stationary stochastic processes. The de-biased
Whittle likelihood can be computed in the same $\mathcal{O}(n\log n)$
operations as the standard approach. We demonstrate the superior performance of
the method in simulation studies and in application to a large-scale
oceanographic dataset, where in both cases the de-biased approach reduces bias
by up to two orders of magnitude, achieving estimates that are close to exact
maximum likelihood, at a fraction of the computational cost. We prove that the
method yields estimates that are consistent at an optimal convergence rate of
$n^{-1/2}$, under weaker assumptions than standard theory, where we do not
require that the power spectral density is continuous in frequency. We describe
how the method can be easily combined with standard methods of bias reduction,
such as tapering and differencing, to further reduce bias in parameter
estimates.
Safer Classification by Synthesis
Authors: William Wang, Angelina Wang, Aviv Tamar, Xi Chen, Pieter Abbeel
Date: 2017-11-22 00:00:00
Tasks: Classification, General Classification

The discriminative approach to classification using deep neural networks has
become the de-facto standard in various fields. Complementing recent
reservations about safety against adversarial examples, we show that
conventional discriminative methods can easily be fooled to provide incorrect
labels with very high confidence to out of distribution examples. We posit that
a generative approach is the natural remedy for this problem, and propose a
method for classification using generative models. At training time, we learn a
generative model for each class, while at test time, given an example to
classify, we query each generator for its most similar generation, and select
the class corresponding to the most similar one. Our approach is general and
can be used with expressive models such as GANs and VAEs. At test time, our
method accurately "knows when it does not know," and provides resilience to out
of distribution examples while maintaining competitive performance for standard
examples.
DeepIoT: Compressing Deep Neural Network Structures for Sensing Systems with a Compressor-Critic Framework
Authors: Shuochao Yao, Yiran Zhao, Aston Zhang, Lu Su, Tarek Abdelzaher
Date: 2017-06-05 00:00:00

Recent advances in deep learning motivate the use of deep neutral networks in
sensing applications, but their excessive resource needs on constrained
embedded devices remain an important impediment. A recently explored solution
space lies in compressing (approximating or simplifying) deep neural networks
in some manner before use on the device. We propose a new compression solution,
called DeepIoT, that makes two key contributions in that space. First, unlike
current solutions geared for compressing specific types of neural networks,
DeepIoT presents a unified approach that compresses all commonly used deep
learning structures for sensing applications, including fully-connected,
convolutional, and recurrent neural networks, as well as their combinations.
Second, unlike solutions that either sparsify weight matrices or assume linear
structure within weight matrices, DeepIoT compresses neural network structures
into smaller dense matrices by finding the minimum number of non-redundant
hidden elements, such as filters and dimensions required by each layer, while
keeping the performance of sensing applications the same. Importantly, it does
so using an approach that obtains a global view of parameter redundancies,
which is shown to produce superior compression. We conduct experiments with
five different sensing-related tasks on Intel Edison devices. DeepIoT
outperforms all compared baseline algorithms with respect to execution time and
energy consumption by a significant margin. It reduces the size of deep neural
networks by 90% to 98.9%. It is thus able to shorten execution time by 71.4% to
94.5%, and decrease energy consumption by 72.2% to 95.7%. These improvements
are achieved without loss of accuracy. The results underscore the potential of
DeepIoT for advancing the exploitation of deep neural networks on
resource-constrained embedded devices.
Bridging the Gap Between Value and Policy Based Reinforcement Learning
Authors: Ofir Nachum, Mohammad Norouzi, Kelvin Xu, Dale Schuurmans
Date: 2017-02-28 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We establish a new connection between value and policy based reinforcement
learning (RL) based on a relationship between softmax temporal value
consistency and policy optimality under entropy regularization. Specifically,
we show that softmax consistent action values correspond to optimal entropy
regularized policy probabilities along any action sequence, regardless of
provenance. From this observation, we develop a new RL algorithm, Path
Consistency Learning (PCL), that minimizes a notion of soft consistency error
along multi-step action sequences extracted from both on- and off-policy
traces. We examine the behavior of PCL in different scenarios and show that PCL
can be interpreted as generalizing both actor-critic and Q-learning algorithms.
We subsequently deepen the relationship by showing how a single model can be
used to represent both a policy and the corresponding softmax state values,
eliminating the need for a separate critic. The experimental evaluation
demonstrates that PCL significantly outperforms strong actor-critic and
Q-learning baselines across several benchmarks.