pwc-archive/papers-with-abstracts huggingface dataset:
Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation
Authors: Hongyu Xiong, Ruixiao Sun
Date: 2018-12-04 00:00:00
Tasks: Data Augmentation, Domain Adaptation, General Knowledge, Semantic Parsing

A natural language interface (NLI) to structured query is intriguing due to
its wide industrial applications and high economical values. In this work, we
tackle the problem of domain adaptation for NLI with limited data on target
domain. Two important approaches are considered: (a) effective
general-knowledge-learning on source domain semantic parsing, and (b) data
augmentation on target domain. We present a Structured Query Inference Network
(SQIN) to enhance learning for domain adaptation, by separating schema
information from NL and decoding SQL in a more structural-aware manner; we also
propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue
of lacking target domain data. We report solid results on GeoQuery, Overnight,
and WikiSQL to demonstrate state-of-the-art performances for both in-domain and
domain-transfer tasks.
Efficient Attention: Attention with Linear Complexities
Authors: Zhuoran Shen, Mingyuan Zhang, Haiyu Zhao, Shuai Yi, Hongsheng Li
Date: 2018-12-04 00:00:00
Tasks: Depth Estimation, Extractive Text Summarization, Image Classification, Instance Segmentation, Object Detection, Object Recognition, Semantic Segmentation, Stereo Depth Estimation

Dot-product attention has wide applications in computer vision and natural language processing. However, its memory and computational costs grow quadratically with the input size. Such growth prohibits its application on high-resolution inputs. To remedy this drawback, this paper proposes a novel efficient attention mechanism equivalent to dot-product attention but with substantially less memory and computational costs. Its resource efficiency allows more widespread and flexible integration of attention modules into a network, which leads to better accuracies. Empirical evaluations demonstrated the effectiveness of its advantages. Efficient attention modules brought significant performance boosts to object detectors and instance segmenters on MS-COCO 2017. Further, the resource efficiency democratizes attention to complex models, where high costs prohibit the use of dot-product attention. As an exemplar, a model with efficient attention achieved state-of-the-art accuracies for stereo depth estimation on the Scene Flow dataset. Code is available at https://github.com/cmsflash/efficient-attention.
Spatio-Temporal Action Graph Networks
Authors: Roei Herzig, Elad Levi, Huijuan Xu, Hang Gao, Eli Brosh, Xiaolong Wang, Amir Globerson, Trevor Darrell
Date: 2018-12-04 00:00:00
Tasks: Activity Recognition, Autonomous Driving, General Classification, Graph Embedding, Object

Events defined by the interaction of objects in a scene are often of critical importance; yet important events may have insufficient labeled examples to train a conventional deep model to generalize to future object appearance. Activity recognition models that represent object interactions explicitly have the potential to learn in a more efficient manner than those that represent scenes with global descriptors. We propose a novel inter-object graph representation for activity recognition based on a disentangled graph embedding with direct observation of edge appearance. We employ a novel factored embedding of the graph structure, disentangling a representation hierarchy formed over spatial dimensions from that found over temporal variation. We demonstrate the effectiveness of our model on the Charades activity recognition benchmark, as well as a new dataset of driving activities focusing on multi-object interactions with near-collision events. Our model offers significantly improved performance compared to baseline approaches without object-graph representations, or with previous graph-based models.
The Alignment of the Spheres: Globally-Optimal Spherical Mixture Alignment for Camera Pose Estimation
Authors: Dylan Campbell, Lars Petersson, Laurent Kneip, Hongdong Li, Stephen Gould
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Camera Pose Estimation, GPU, Pose Estimation

Determining the position and orientation of a calibrated camera from a single image with respect to a 3D model is an essential task for many applications. When 2D-3D correspondences can be obtained reliably, perspective-n-point solvers can be used to recover the camera pose. However, without the pose it is non-trivial to find cross-modality correspondences between 2D images and 3D models, particularly when the latter only contains geometric information. Consequently, the problem becomes one of estimating pose and correspondences jointly. Since outliers and local optima are so prevalent, robust objective functions and global search strategies are desirable. Hence, we cast the problem as a 2D-3D mixture model alignment task and propose the first globally-optimal solution to this formulation under the robust $L_2$ distance between mixture distributions. We search the 6D camera pose space using branch-and-bound, which requires novel bounds, to obviate the need for a pose estimate and guarantee global optimality. To accelerate convergence, we integrate local optimization, implement GPU bound computations, and provide an intuitive way to incorporate side information such as semantic labels. The algorithm is evaluated on challenging synthetic and real datasets, outperforming existing approaches and reliably converging to the global optimum.
Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration
Authors: Po-Hsuan Cameron Chen, Krishna Gadepalli, Robert MacDonald, Yun Liu, Kunal Nagpal, Timo Kohlberger, Jeffrey Dean, Greg S. Corrado, Jason D. Hipp, Martin C. Stumpe
Date: 2018-11-21 00:00:00
Tasks: Diagnostic

The brightfield microscope is instrumental in the visual examination of both
biological and physical samples at sub-millimeter scales. One key clinical
application has been in cancer histopathology, where the microscopic assessment
of the tissue samples is used for the diagnosis and staging of cancer and thus
guides clinical therapy. However, the interpretation of these samples is
inherently subjective, resulting in significant diagnostic variability.
Moreover, in many regions of the world, access to pathologists is severely
limited due to lack of trained personnel. In this regard, Artificial
Intelligence (AI) based tools promise to improve the access and quality of
healthcare. However, despite significant advances in AI research, integration
of these tools into real-world cancer diagnosis workflows remains challenging
because of the costs of image digitization and difficulties in deploying AI
solutions. Here we propose a cost-effective solution to the integration of AI:
the Augmented Reality Microscope (ARM). The ARM overlays AI-based information
onto the current view of the sample through the optical pathway in real-time,
enabling seamless integration of AI into the regular microscopy workflow. We
demonstrate the utility of ARM in the detection of lymph node metastases in
breast cancer and the identification of prostate cancer with a latency that
supports real-time workflows. We anticipate that ARM will remove barriers
towards the use of AI in microscopic analysis and thus improve the accuracy and
efficiency of cancer diagnosis. This approach is applicable to other microscopy
tasks and AI algorithms in the life sciences and beyond.
Learning Vine Copula Models For Synthetic Data Generation
Authors: Yi Sun, Alfredo Cuesta-Infante, Kalyan Veeramachaneni
Date: 2018-12-04 00:00:00
Tasks: Model Selection, Reinforcement Learning, Reinforcement Learning (RL), Synthetic Data Generation

A vine copula model is a flexible high-dimensional dependence model which
uses only bivariate building blocks. However, the number of possible
configurations of a vine copula grows exponentially as the number of variables
increases, making model selection a major challenge in development. In this
work, we formulate a vine structure learning problem with both vector and
reinforcement learning representation. We use neural network to find the
embeddings for the best possible vine model and generate a structure.
Throughout experiments on synthetic and real-world datasets, we show that our
proposed approach fits the data better in terms of log-likelihood. Moreover, we
demonstrate that the model is able to generate high-quality samples in a
variety of applications, making it a good candidate for synthetic data
generation.
Inferring Remote Channel State Information: Cramér-Rao Lower Bound and Deep Learning Implementation
Authors: Zhiyuan Jiang, Ziyan He, Sheng Chen, Andreas F. Molisch, Sheng Zhou, Zhisheng Niu
Date: 2018-12-04 00:00:00

Channel state information (CSI) is of vital importance in wireless
communication systems. Existing CSI acquisition methods usually rely on pilot
transmissions, and geographically separated base stations (BSs) with
non-correlated CSI need to be assigned with orthogonal pilots which occupy
excessive system resources. Our previous work adopts a data-driven deep
learning based approach which leverages the CSI at a local BS to infer the CSI
remotely, however the relevance of CSI between separated BSs is not specified
explicitly. In this paper, we exploit a model-based methodology to derive the
Cram\'er-Rao lower bound (CRLB) of remote CSI inference given the local CSI.
Although the model is simplified, the derived CRLB explicitly illustrates the
relationship between the inference performance and several key system
parameters, e.g., terminal distance and antenna array size. In particular, it
shows that by leveraging multiple local BSs, the inference error exhibits a
larger power-law decay rate (w.r.t. number of antennas), compared with a single
local BS; this explains and validates our findings in evaluating the
deep-neural-network-based (DNN-based) CSI inference. We further improve on the
DNN-based method by employing dropout and deeper networks, and show an
inference performance of approximately $90\%$ accuracy in a realistic scenario
with CSI generated by a ray-tracing simulator.
Ladder Networks for Semi-Supervised Hyperspectral Image Classification
Authors: Julian Büchel, Okan Ersoy
Date: 2018-12-04 00:00:00
Tasks: Classification, General Classification, Hyperspectral Image Classification, image-classification, Image Classification

We used the Ladder Network [Rasmus et al. (2015)] to perform Hyperspectral
Image Classification in a semi-supervised setting. The Ladder Network
distinguishes itself from other semi-supervised methods by jointly optimizing a
supervised and unsupervised cost. In many settings this has proven to be more
successful than other semi-supervised techniques, such as pretraining using
unlabeled data. We furthermore show that the convolutional Ladder Network
outperforms most of the current techniques used in hyperspectral image
classification and achieves new state-of-the-art performance on the Pavia
University dataset given only 5 labeled data points per class.
Time-Sequence Channel Inference for Beam Alignment in Vehicular Networks
Authors: Sheng Chen, Zhiyuan Jiang, Sheng Zhou, Zhisheng Niu
Date: 2018-12-04 00:00:00
Tasks: Neural Network simulation

In this paper, we propose a learning-based low-overhead beam alignment method
for vehicle-to-infrastructure communication in vehicular networks. The main
idea is to remotely infer the optimal beam directions at a target base station
in future time slots, based on the CSI of a source base station in previous
time slots. The proposed scheme can reduce channel acquisition and beam
training overhead by replacing pilot-aided beam training with online inference
from a sequence-to-sequence neural network. Simulation results based on
ray-tracing channel data show that our proposed scheme achieves a $8.86\%$
improvement over location-based beamforming schemes with a positioning error of
$1$m, and is within a $4.93\%$ performance loss compared with the genie-aided
optimal beamformer.
Using Multitask Learning to Improve 12-Lead Electrocardiogram Classification
Authors: J. Weston Hughes, Taylor Sittler, Anthony D. Joseph, Jeffrey E. Olgin, Joseph E. Gonzalez, Geoffrey H. Tison
Date: 2018-12-03 00:00:00
Tasks: Classification, General Classification

We develop a multi-task convolutional neural network (CNN) to classify
multiple diagnoses from 12-lead electrocardiograms (ECGs) using a dataset
comprised of over 40,000 ECGs, with labels derived from cardiologist clinical
interpretations. Since many clinically important classes can occur in low
frequencies, approaches are needed to improve performance on rare classes. We
compare the performance of several single-class classifiers on rare classes to
a multi-headed classifier across all available classes. We demonstrate that the
addition of common classes can significantly improve CNN performance on rarer
classes when compared to a model trained on the rarer class in isolation. Using
this method, we develop a model with high performance as measured by F1 score
on multiple clinically relevant classes compared against the gold-standard
cardiologist interpretation.
Set Cross Entropy: Likelihood-based Permutation Invariant Loss Function for Probability Distributions
Authors: Masataro Asai
Date: 2018-12-04 00:00:00
Tasks: Object Reconstruction

We propose a permutation-invariant loss function designed for the neural
networks reconstructing a set of elements without considering the order within
its vector representation. Unlike popular approaches for encoding and decoding
a set, our work does not rely on a carefully engineered network topology nor by
any additional sequential algorithm. The proposed method, Set Cross Entropy,
has a natural information-theoretic interpretation and is related to the
metrics defined for sets. We evaluate the proposed approach in two object
reconstruction tasks and a rule learning task.
Parameter Re-Initialization through Cyclical Batch Size Schedules
Authors: Norman Mu, Zhewei Yao, Amir Gholami, Kurt Keutzer, Michael Mahoney
Date: 2018-12-04 00:00:00
Tasks: General Classification, image-classification, Image Classification, Language Modeling, Language Modelling, Natural Language Inference

Optimal parameter initialization remains a crucial problem for neural network
training. A poor weight initialization may take longer to train and/or converge
to sub-optimal solutions. Here, we propose a method of weight re-initialization
by repeated annealing and injection of noise in the training process. We
implement this through a cyclical batch size schedule motivated by a Bayesian
perspective of neural network training. We evaluate our methods through
extensive experiments on tasks in language modeling, natural language
inference, and image classification. We demonstrate the ability of our method
to improve language modeling performance by up to 7.91 perplexity and reduce
training iterations by up to $61\%$, in addition to its flexibility in enabling
snapshot ensembling and use with adversarial training.
Prototype-based Neural Network Layers: Incorporating Vector Quantization
Authors: Sascha Saralajew, Lars Holdijk, Maike Rees, Thomas Villmann
Date: 2018-12-04 00:00:00
Tasks: General Classification, image-classification, Image Classification, Quantization

Neural networks currently dominate the machine learning community and they do
so for good reasons. Their accuracy on complex tasks such as image
classification is unrivaled at the moment and with recent improvements they are
reasonably easy to train. Nevertheless, neural networks are lacking robustness
and interpretability. Prototype-based vector quantization methods on the other
hand are known for being robust and interpretable. For this reason, we propose
techniques and strategies to merge both approaches. This contribution will
particularly highlight the similarities between them and outline how to
construct a prototype-based classification layer for multilayer networks.
Additionally, we provide an alternative, prototype-based, approach to the
classical convolution operation. Numerical results are not part of this report,
instead the focus lays on establishing a strong theoretical framework. By
publishing our framework and the respective theoretical considerations and
justifications before finalizing our numerical experiments we hope to
jump-start the incorporation of prototype-based learning in neural networks and
vice versa.
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination
Authors: Liangzhe Yuan, Yibo Chen, Hantian Liu, Tao Kong, Jianbo Shi
Date: 2018-12-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Generation, object-detection, Object Detection, Optical Flow Estimation, Video Frame Interpolation

We propose a light-weight video frame interpolation algorithm. Our key
innovation is an instance-level supervision that allows information to be
learned from the high-resolution version of similar objects. Our experiment
shows that the proposed method can generate state-of-the-art results across
different datasets, with fractional computation resources (time and memory) of
competing methods. Given two image frames, a cascade network creates an
intermediate frame with 1) a flow-warping module that computes coarse
bi-directional optical flow and creates an interpolated image via flow-based
warping, followed by 2) an image synthesis module to make fine-scale
corrections. In the learning stage, object detection proposals are generated on
the interpolated image.Lower resolution objects are zoomed into, and the
learning algorithms using an adversarial loss trained on high-resolution
objects to guide the system towards the instance-level refinement corrects
details of object shape and boundaries.
Practical Text Classification With Large Pre-Trained Language Models
Authors: Neel Kant, Raul Puri, Nikolai Yakovenko, Bryan Catanzaro
Date: 2018-12-04 00:00:00
Tasks: Classification, Emotion Classification, General Classification, Language Modeling, Language Modelling, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Multi-emotion sentiment classification is a natural language processing (NLP)
problem with valuable use cases on real-world data. We demonstrate that
large-scale unsupervised language modeling combined with finetuning offers a
practical solution to this task on difficult datasets, including those with
label class imbalance and domain-specific context. By training an
attention-based Transformer network (Vaswani et al. 2017) on 40GB of text
(Amazon reviews) (McAuley et al. 2015) and fine-tuning on the training set, our
model achieves a 0.69 F1 score on the SemEval Task 1:E-c multi-dimensional
emotion classification problem (Mohammad et al. 2018), based on the Plutchik
wheel of emotions (Plutchik 1979). These results are competitive with state of
the art models, including strong F1 scores on difficult (emotion) categories
such as Fear (0.73), Disgust (0.77) and Anger (0.78), as well as competitive
results on rare categories such as Anticipation (0.42) and Surprise (0.37).
Furthermore, we demonstrate our application on a real world text classification
task. We create a narrowly collected text dataset of real tweets on several
topics, and show that our finetuned model outperforms general purpose
commercially available APIs for sentiment and multidimensional emotion
classification on this dataset by a significant margin. We also perform a
variety of additional studies, investigating properties of deep learning
architectures, datasets and algorithms for achieving practical multidimensional
sentiment classification. Overall, we find that unsupervised language modeling
and finetuning is a simple framework for achieving high quality results on
real-world sentiment classification.
Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture
Authors: Micha Livne, Leonid Sigal, Marcus A. Brubaker, David J. Fleet
Date: 2018-12-04 00:00:00
Tasks: Markerless Motion Capture

We propose a generative approach to physics-based motion capture. Unlike
prior attempts to incorporate physics into tracking that assume the subject and
scene geometry are calibrated and known a priori, our approach is automatic and
online. This distinction is important since calibration of the environment is
often difficult, especially for motions with props, uneven surfaces, or outdoor
scenes. The use of physics in this context provides a natural framework to
reason about contact and the plausibility of recovered motions. We propose a
fast data-driven parametric body model, based on linear-blend skinning, which
decouples deformations due to pose, anthropometrics and body shape. Pose (and
shape) parameters are estimated using robust ICP optimization with
physics-based dynamic priors that incorporate contact. Contact is estimated
from torque trajectories and predictions of which contact points were active.
To our knowledge, this is the first approach to take physics into account
without explicit {\em a priori} knowledge of the environment or body
dimensions. We demonstrate effective tracking from a noisy single depth camera,
improving on state-of-the-art results quantitatively and producing better
qualitative results, reducing visual artifacts like foot-skate and jitter.
Twitter-based traffic information system based on vector representations for words
Authors: Sina Dabiri, Kevin Heaslip
Date: 2018-12-04 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

Recently, researchers have shown an increased interest in harnessing Twitter
data for dynamic monitoring of traffic conditions. Bag-of-words representation
is a common method in literature for tweet modeling and retrieving traffic
information, yet it suffers from the curse of dimensionality and sparsity. To
address these issues, our specific objective is to propose a simple and robust
framework on the top of word embedding for distinguishing traffic-related
tweets against non-traffic-related ones. In our proposed model, a tweet is
classified as traffic-related if semantic similarity between its words and a
small set of traffic keywords exceeds a threshold value. Semantic similarity
between words is captured by means of word-embedding models, which is an
unsupervised learning tool. The proposed model is as simple as having only one
trainable parameter. The model takes advantage of outstanding merits, which are
demonstrated through several evaluation steps. The state-of-the-art test
accuracy for our proposed model is 95.9%.
Explainable Genetic Inheritance Pattern Prediction
Authors: Edmond Cunningham, Dana Schlegel, Andrew DeOrio
Date: 2018-12-01 00:00:00
Tasks: Causal Inference, Prediction, State Space Models

Diagnosing an inherited disease often requires identifying the pattern of
inheritance in a patient's family. We represent family trees with genetic
patterns of inheritance using hypergraphs and latent state space models to
provide explainable inheritance pattern predictions. Our approach allows for
exact causal inference over a patient's possible genotypes given their
relatives' phenotypes. By design, inference can be examined at a low level to
provide explainable predictions. Furthermore, we make use of human intuition by
providing a method to assign hypothetical evidence to any inherited gene
alleles. Our analysis supports the application of latent state space models to
improve patient care in cases of rare inherited diseases where access to
genetic specialists is limited.