pwc-archive/papers-with-abstracts huggingface dataset:
Knowledge-Augmented Language Model and its Application to Unsupervised Named-Entity Recognition
Authors: Angli Liu, Jingfei Du, Veselin Stoyanov
Date: 2019-04-09 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, World Knowledge

Traditional language models are unable to efficiently model entity names observed in text. All but the most popular named entities appear infrequently in text providing insufficient context. Recent efforts have recognized that context can be generalized between entity names that share the same type (e.g., \emph{person} or \emph{location}) and have equipped language models with access to an external knowledge base (KB). Our Knowledge-Augmented Language Model (KALM) continues this line of work by augmenting a traditional model with a KB. Unlike previous methods, however, we train with an end-to-end predictive objective optimizing the perplexity of text. We do not require any additional information such as named entity tags. In addition to improving language modeling performance, KALM learns to recognize named entities in an entirely unsupervised way by using entity type information latent in the model. On a Named Entity Recognition (NER) task, KALM achieves performance comparable with state-of-the-art supervised models. Our work demonstrates that named entities (and possibly other types of world knowledge) can be modeled successfully using predictive learning and training on large corpora of text without any additional information.
Ultrafast Video Attention Prediction with Coupled Knowledge Distillation
Authors: Kui Fu, Peipei Shi, Yafei Song, Shiming Ge, Xiangju Lu, Jia Li
Date: 2019-04-09 00:00:00
Tasks: CPU, GPU, Knowledge Distillation, Prediction

Large convolutional neural network models have recently demonstrated impressive performance on video attention prediction. Conventionally, these models are with intensive computation and large memory. To address these issues, we design an extremely light-weight network with ultrafast speed, named UVA-Net. The network is constructed based on depth-wise convolutions and takes low-resolution images as input. However, this straight-forward acceleration method will decrease performance dramatically. To this end, we propose a coupled knowledge distillation strategy to augment and train the network effectively. With this strategy, the model can further automatically discover and emphasize implicit useful cues contained in the data. Both spatial and temporal knowledge learned by the high-resolution complex teacher networks also can be distilled and transferred into the proposed low-resolution light-weight spatiotemporal network. Experimental results show that the performance of our model is comparable to 11 state-of-the-art models in video attention prediction, while it costs only 0.68 MB memory footprint, runs about 10,106 FPS on GPU and 404 FPS on CPU, which is 206 times faster than previous models.
Feature Generation by Convolutional Neural Network for Click-Through Rate Prediction
Authors: Bin Liu, Ruiming Tang, Yingzhi Chen, Jinkai Yu, Huifeng Guo, Yuzhou Zhang
Date: 2019-04-09 00:00:00
Tasks: Click-Through Rate Prediction, Deep Learning, Recommendation Systems

Easy-to-use,Modular and Extendible package of deep-learning based CTR models.DeepFM,DeepInterestNetwork(DIN),DeepInterestEvolutionNetwork(DIEN),DeepCrossNetwork(DCN),AttentionalFactorizationMachine(AFM),Neural Factorization Machine(NFM),AutoInt,Deep Session Interest Network(DSIN)
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition
Authors: Wenxiang Jiao, Haiqin Yang, Irwin King, Michael R. Lyu
Date: 2019-04-09 00:00:00
Proceeding: NAACL 2019 6
Tasks: Emotion Recognition

In this paper, we address three challenges in utterance-level emotion
recognition in dialogue systems: (1) the same word can deliver different
emotions in different contexts; (2) some emotions are rarely seen in general
dialogues; (3) long-range contextual information is hard to be effectively
captured. We therefore propose a hierarchical Gated Recurrent Unit (HiGRU)
framework with a lower-level GRU to model the word-level inputs and an
upper-level GRU to capture the contexts of utterance-level embeddings.
Moreover, we promote the framework to two variants, HiGRU with individual
features fusion (HiGRU-f) and HiGRU with self-attention and features fusion
(HiGRU-sf), so that the word/utterance-level individual inputs and the
long-range contextual information can be sufficiently utilized. Experiments on
three dialogue emotion datasets, IEMOCAP, Friends, and EmotionPush demonstrate
that our proposed HiGRU models attain at least 8.7%, 7.5%, 6.0% improvement
over the state-of-the-art methods on each dataset, respectively. Particularly,
by utilizing only the textual feature in IEMOCAP, our HiGRU models gain at
least 3.8% improvement over the state-of-the-art conversational memory network
(CMN) with the trimodal features of text, video, and audio.
Multimodal Style Transfer via Graph Cuts
Authors: Yulun Zhang, Chen Fang, Yilin Wang, Zhaowen Wang, Zhe Lin, Yun Fu, Jimei Yang
Date: 2019-04-09 00:00:00
Proceeding: ICCV 2019 10
Tasks: Style Transfer

An assumption widely used in recent neural style transfer methods is that image styles can be described by global statics of deep features like Gram or covariance matrices. Alternative approaches have represented styles by decomposing them into local pixel or neural patches. Despite the recent progress, most existing methods treat the semantic patterns of style image uniformly, resulting unpleasing results on complex styles. In this paper, we introduce a more flexible and general universal style transfer technique: multimodal style transfer (MST). MST explicitly considers the matching of semantic patterns in content and style images. Specifically, the style image features are clustered into sub-style components, which are matched with local content features under a graph cut formulation. A reconstruction network is trained to transfer each sub-style and render the final stylized result. We also generalize MST to improve some existing methods. Extensive experiments demonstrate the superior effectiveness, robustness, and flexibility of MST.
$L_0$-ARM: Network Sparsification via Stochastic Binary Optimization
Authors: Yang Li, Shihao Ji
Date: 2019-04-09 00:00:00

We consider network sparsification as an $L_0$-norm regularized binary optimization problem, where each unit of a neural network (e.g., weight, neuron, or channel, etc.) is attached with a stochastic binary gate, whose parameters are jointly optimized with original network parameters. The Augment-Reinforce-Merge (ARM), a recently proposed unbiased gradient estimator, is investigated for this binary optimization problem. Compared to the hard concrete gradient estimator from Louizos et al., ARM demonstrates superior performance of pruning network architectures while retaining almost the same accuracies of baseline methods. Similar to the hard concrete estimator, ARM also enables conditional computation during model training but with improved effectiveness due to the exact binary stochasticity. Thanks to the flexibility of ARM, many smooth or non-smooth parametric functions, such as scaled sigmoid or hard sigmoid, can be used to parameterize this binary optimization problem and the unbiasness of the ARM estimator is retained, while the hard concrete estimator has to rely on the hard sigmoid function to achieve conditional computation and thus accelerated training. Extensive experiments on multiple public datasets demonstrate state-of-the-art pruning rates with almost the same accuracies of baseline methods. The resulting algorithm $L_0$-ARM sparsifies the Wide-ResNet models on CIFAR-10 and CIFAR-100 while the hard concrete estimator cannot. The code is public available at https://github.com/leo-yangli/l0-arm.
Passive TCP Identification for Wired and WirelessNetworks: A Long-Short Term Memory Approach
Authors: Xiaoyu Chen, Shugong Xu, Xudong Chen, Shan Cao, Shunqing Zhang, Yanzan Sun
Date: 2019-04-09 00:00:00
Tasks: BIG-bench Machine Learning

Transmission control protocol (TCP) congestion control is one of the key
techniques to improve network performance. TCP congestion control algorithm
identification (TCP identification) can be used to significantly improve
network efficiency. Existing TCP identification methods can only be applied to
limited number of TCP congestion control algorithms and focus on wired
networks. In this paper, we proposed a machine learning based passive TCP
identification method for wired and wireless networks. After comparing among
three typical machine learning models, we concluded that the 4-layers Long
Short Term Memory (LSTM) model achieves the best identification accuracy. Our
approach achieves better than 98% accuracy in wired and wireless networks and
works for newly proposed TCP congestion control algorithms.
Label Super Resolution with Inter-Instance Loss
Authors: Maozheng Zhao, Le Hou, Han Le, Dimitris Samaras, Nebojsa Jojic, Danielle Fassler, Tahsin Kurc, Rajarsi Gupta, Kolya Malkin, Shroyer Kenneth, Joel Saltz
Date: 2019-04-09 00:00:00
Tasks: Segmentation, Semantic Segmentation, Super-Resolution

For the task of semantic segmentation, high-resolution (pixel-level) ground truth is very expensive to collect, especially for high resolution images such as gigapixel pathology images. On the other hand, collecting low resolution labels (labels for a block of pixels) for these high resolution images is much more cost efficient. Conventional methods trained on these low-resolution labels are only capable of giving low-resolution predictions. The existing state-of-the-art label super resolution (LSR) method is capable of predicting high resolution labels, using only low-resolution supervision, given the joint distribution between low resolution and high resolution labels. However, it does not consider the inter-instance variance which is crucial in the ideal mathematical formulation. In this work, we propose a novel loss function modeling the inter-instance variance. We test our method on a real world application: infiltrating breast cancer region segmentation in histopathology slides. Experimental results show the effectiveness of our method.
Text Generation with Exemplar-based Adaptive Decoding
Authors: Hao Peng, Ankur P. Parikh, Manaal Faruqui, Bhuwan Dhingra, Dipanjan Das
Date: 2019-04-09 00:00:00
Proceeding: NAACL 2019 6
Tasks: Abstractive Text Summarization, Data-to-Text Generation, Decoder, Text Generation, Text Summarization

We propose a novel conditioned text generation model. It draws inspiration
from traditional template-based text generation techniques, where the source
provides the content (i.e., what to say), and the template influences how to
say it. Building on the successful encoder-decoder paradigm, it first encodes
the content representation from the given input text; to produce the output, it
retrieves exemplar text from the training data as "soft templates," which are
then used to construct an exemplar-specific decoder. We evaluate the proposed
model on abstractive text summarization and data-to-text generation. Empirical
results show that this model achieves strong performance and outperforms
comparable baselines.
3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
Authors: Chaojing Duan, Siheng Chen, Jelena Kovacevic
Date: 2019-04-09 00:00:00
Tasks: Denoising

We present a neural-network-based architecture for 3D point cloud denoising
called neural projection denoising (NPD). In our previous work, we proposed a
two-stage denoising algorithm, which first estimates reference planes and
follows by projecting noisy points to estimated reference planes. Since the
estimated reference planes are inevitably noisy, multi-projection is applied to
stabilize the denoising performance. NPD algorithm uses a neural network to
estimate reference planes for points in noisy point clouds. With more accurate
estimations of reference planes, we are able to achieve better denoising
performances with only one-time projection. To the best of our knowledge, NPD
is the first work to denoise 3D point clouds with deep learning techniques. To
conduct the experiments, we sample 40000 point clouds from the 3D data in
ShapeNet to train a network and sample 350 point clouds from the 3D data in
ModelNet10 to test. Experimental results show that our algorithm can estimate
normal vectors of points in noisy point clouds. Comparing to five competitive
methods, the proposed algorithm achieves better denoising performance and
produces much smaller variances.
FPGA/DNN Co-Design: An Efficient Design Methodology for IoT Intelligence on the Edge
Authors: Cong Hao, Xiaofan Zhang, Yuhong Li, Sitao Huang, JinJun Xiong, Kyle Rupnow, Wen-mei Hwu, Deming Chen
Date: 2019-04-09 00:00:00
Tasks: C++ code, GPU, object-detection, Object Detection

While embedded FPGAs are attractive platforms for DNN acceleration on
edge-devices due to their low latency and high energy efficiency, the scarcity
of resources of edge-scale FPGA devices also makes it challenging for DNN
deployment. In this paper, we propose a simultaneous FPGA/DNN co-design
methodology with both bottom-up and top-down approaches: a bottom-up
hardware-oriented DNN model search for high accuracy, and a top-down FPGA
accelerator design considering DNN-specific characteristics. We also build an
automatic co-design flow, including an Auto-DNN engine to perform
hardware-oriented DNN model search, as well as an Auto-HLS engine to generate
synthesizable C code of the FPGA accelerator for explored DNNs. We demonstrate
our co-design approach on an object detection task using PYNQ-Z1 FPGA. Results
show that our proposed DNN model and accelerator outperform the
state-of-the-art FPGA designs in all aspects including Intersection-over-Union
(IoU) (6.2% higher), frames per second (FPS) (2.48X higher), power consumption
(40% lower), and energy efficiency (2.5X higher). Compared to GPU-based
solutions, our designs deliver similar accuracy but consume far less energy.
Embryo staging with weakly-supervised region selection and dynamically-decoded predictions
Authors: Tingfung Lau, Nathan Ng, Julian Gingold, Nina Desai, Julian McAuley, Zachary C. Lipton
Date: 2019-04-09 00:00:00
Tasks: Decoder, Region Proposal, Reinforcement Learning

To optimize clinical outcomes, fertility clinics must strategically select
which embryos to transfer. Common selection heuristics are formulas expressed
in terms of the durations required to reach various developmental milestones,
quantities historically annotated manually by experienced embryologists based
on time-lapse EmbryoScope videos. We propose a new method for automatic embryo
staging that exploits several sources of structure in this time-lapse data.
First, noting that in each image the embryo occupies a small subregion, we
jointly train a region proposal network with the downstream classifier to
isolate the embryo. Notably, because we lack ground-truth bounding boxes, our
we weakly supervise the region proposal network optimizing its parameters via
reinforcement learning to improve the downstream classifier's loss. Moreover,
noting that embryos reaching the blastocyst stage progress monotonically
through earlier stages, we develop a dynamic-programming-based decoder that
post-processes our predictions to select the most likely monotonic sequence of
developmental stages. Our methods outperform vanilla residual networks and
rival the best numbers in contemporary papers, as measured by both per-frame
accuracy and transition prediction error, despite operating on smaller data
than many.
3D Quantum Cuts for Automatic Segmentation of Porous Media in Tomography Images
Authors: Junaid Malik, Serkan Kiranyaz, Riyadh Al-Raoush, Olivier Monga, Patricia Garnier, Sebti Foufou, Abdelaziz Bouras, Alexandros Iosifidis, Moncef Gabbouj, Philippe C. Baveye
Date: 2019-04-09 00:00:00
Tasks: Clustering, Image Segmentation, Segmentation, Semantic Segmentation

Binary segmentation of volumetric images of porous media is a crucial step
towards gaining a deeper understanding of the factors governing biogeochemical
processes at minute scales. Contemporary work primarily revolves around
primitive techniques based on global or local adaptive thresholding that have
known common drawbacks in image segmentation. Moreover, absence of a unified
benchmark prohibits quantitative evaluation, which further clouds the impact of
existing methodologies. In this study, we tackle the issue on both fronts.
Firstly, by drawing parallels with natural image segmentation, we propose a
novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D)
grounded on a state-of-the-art spectral clustering technique. Secondly, we
curate and present a publicly available dataset of 68 multiphase volumetric
images of porous media with diverse solid geometries, along with voxel-wise
ground truth annotations for each constituting phase. We provide comparative
evaluations between QCuts-3D and the current state-of-the-art over this dataset
across a variety of evaluation metrics. The proposed systematic approach
achieves a 26% increase in AUROC while achieving a substantial reduction of the
computational complexity of the state-of-the-art competitors. Moreover,
statistical analysis reveals that the proposed method exhibits significant
robustness against the compositional variations of porous media.
Context-Aware Query Selection for Active Learning in Event Recognition
Authors: Mahmudul Hasan, Sujoy Paul, Anastasios I. Mourikis, Amit K. Roy-Chowdhury
Date: 2019-04-09 00:00:00
Tasks: Active Learning, Activity Recognition, Informativeness

Activity recognition is a challenging problem with many practical
applications. In addition to the visual features, recent approaches have
benefited from the use of context, e.g., inter-relationships among the
activities and objects. However, these approaches require data to be labeled,
entirely available beforehand, and not designed to be updated continuously,
which make them unsuitable for surveillance applications. In contrast, we
propose a continuous-learning framework for context-aware activity recognition
from unlabeled video, which has two distinct advantages over existing methods.
First, it employs a novel active-learning technique that not only exploits the
informativeness of the individual activities but also utilizes their contextual
information during query selection; this leads to significant reduction in
expensive manual annotation effort. Second, the learned models can be adapted
online as more data is available. We formulate a conditional random field model
that encodes the context and devise an information-theoretic approach that
utilizes entropy and mutual information of the nodes to compute the set of most
informative queries, which are labeled by a human. These labels are combined
with graphical inference techniques for incremental updates. We provide a
theoretical formulation of the active learning framework with an analytic
solution. Experiments on six challenging datasets demonstrate that our
framework achieves superior performance with significantly less manual
labeling.
Embodied Visual Recognition
Authors: Jianwei Yang, Zhile Ren, Mingze Xu, Xinlei Chen, David Crandall, Devi Parikh, Dhruv Batra
Date: 2019-04-09 00:00:00
Tasks: Object, Object Localization, Semantic Segmentation

Passive visual systems typically fail to recognize objects in the amodal
setting where they are heavily occluded. In contrast, humans and other embodied
agents have the ability to move in the environment, and actively control the
viewing angle to better understand object shapes and semantics. In this work,
we introduce the task of Embodied Visual Recognition (EVR): An agent is
instantiated in a 3D environment close to an occluded target object, and is
free to move in the environment to perform object classification, amodal object
localization, and amodal object segmentation. To address this, we develop a new
model called Embodied Mask R-CNN, for agents to learn to move strategically to
improve their visual recognition abilities. We conduct experiments using the
House3D environment. Experimental results show that: 1) agents with embodiment
(movement) achieve better visual recognition performance than passive ones; 2)
in order to improve visual recognition abilities, agents can learn strategical
moving paths that are different from shortest paths.
Discovering Bands from Graphs
Authors: Nikolaj Tatti
Date: 2019-04-09 00:00:00
Tasks: Graph Mining

Discovering the underlying structure of a given graph is one of the
fundamental goals in graph mining. Given a graph, we can often order vertices
in a way that neighboring vertices have a higher probability of being connected
to each other. This implies that the edges form a band around the diagonal in
the adjacency matrix. Such structure may rise for example if the graph was
created over time: each vertex had an active time interval during which the
vertex was connected with other active vertices.
  The goal of this paper is to model this phenomenon. To this end, we formulate
an optimization problem: given a graph and an integer $K$, we want to order
graph vertices and partition the ordered adjacency matrix into $K$ bands such
that bands closer to the diagonal are more dense. We measure the goodness of a
segmentation using the log-likelihood of a log-linear model, a flexible family
of distributions containing many standard distributions. We divide the problem
into two subproblems: finding the order and finding the bands. We show that
discovering bands can be done in polynomial time with isotonic regression, and
we also introduce a heuristic iterative approach. For discovering the order we
use Fiedler order accompanied with a simple combinatorial refinement. We
demonstrate empirically that our heuristic works well in practice.
Sketchforme: Composing Sketched Scenes from Text Descriptions for Interactive Applications
Authors: Forrest Huang, John F. Canny
Date: 2019-04-08 00:00:00

Sketching and natural languages are effective communication media for
interactive applications. We introduce Sketchforme, the first
neural-network-based system that can generate sketches based on text
descriptions specified by users. Sketchforme is capable of gaining high-level
and low-level understanding of multi-object sketched scenes without being
trained on sketched scene datasets annotated with text descriptions. The
sketches composed by Sketchforme are expressive and realistic: we show in our
user study that these sketches convey descriptions better than human-generated
sketches in multiple cases, and 36.5% of those sketches are considered to be
human-generated. We develop multiple interactive applications using these
generated sketches, and show that Sketchforme can significantly improve
language learning applications and support intelligent language-based sketching
assistants.
Disfluencies and Human Speech Transcription Errors
Authors: Vicky Zayats, Trang Tran, Richard Wright, Courtney Mansfield, Mari Ostendorf
Date: 2019-04-08 00:00:00

This paper explores contexts associated with errors in transcrip-tion of
spontaneous speech, shedding light on human perceptionof disfluencies and other
conversational speech phenomena. Anew version of the Switchboard corpus is
provided with disfluency annotations for careful speech transcripts, together
with results showing the impact of transcription errors on evaluation of
automatic disfluency detection.