pwc-archive/papers-with-abstracts huggingface dataset:
An Encoder-Decoder Framework Translating Natural Language to Database Queries
Authors: Ruichu Cai, Boyan Xu, Xiaoyan Yang, Zhenjie Zhang, Zijian Li, Zhihao Liang
Date: 2017-11-16 00:00:00
Tasks: Decoder, Machine Translation, Management, Retrieval, Translation

Machine translation is going through a radical revolution, driven by the
explosive development of deep learning techniques using Convolutional Neural
Network (CNN) and Recurrent Neural Network (RNN). In this paper, we consider a
special case in machine translation problems, targeting to convert natural
language into Structured Query Language (SQL) for data retrieval over
relational database. Although generic CNN and RNN learn the grammar structure
of SQL when trained with sufficient samples, the accuracy and training
efficiency of the model could be dramatically improved, when the translation
model is deeply integrated with the grammar rules of SQL. We present a new
encoder-decoder framework, with a suite of new approaches, including new
semantic features fed into the encoder, grammar-aware states injected into the
memory of decoder, as well as recursive state management for sub-queries. These
techniques help the neural network better focus on understanding semantics of
operations in natural language and save the efforts on SQL grammar learning.
The empirical evaluation on real world database and queries show that our
approach outperform state-of-the-art solution by a significant margin.
Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech
Authors: Yu-An Chung, James Glass
Date: 2018-03-23 00:00:00
Tasks: Decoder, Learning Word Embeddings, Word Embeddings, Word Similarity

In this paper, we propose a novel deep neural network architecture,
Speech2Vec, for learning fixed-length vector representations of audio segments
excised from a speech corpus, where the vectors contain semantic information
pertaining to the underlying spoken words, and are close to other vectors in
the embedding space if their corresponding underlying spoken words are
semantically similar. The proposed model can be viewed as a speech version of
Word2Vec. Its design is based on a RNN Encoder-Decoder framework, and borrows
the methodology of skipgrams or continuous bag-of-words for training. Learning
word embeddings directly from speech enables Speech2Vec to make use of the
semantic information carried by speech that does not exist in plain text. The
learned word embeddings are evaluated and analyzed on 13 widely used word
similarity benchmarks, and outperform word embeddings learned by Word2Vec from
the transcriptions.
Efficient Optimization Algorithms for Robust Principal Component Analysis and Its Variants
Authors: Shiqian Ma, Necdet Serhat Aybat
Date: 2018-06-09 00:00:00

Robust PCA has drawn significant attention in the last decade due to its
success in numerous application domains, ranging from bio-informatics,
statistics, and machine learning to image and video processing in computer
vision. Robust PCA and its variants such as sparse PCA and stable PCA can be
formulated as optimization problems with exploitable special structures. Many
specialized efficient optimization methods have been proposed to solve robust
PCA and related problems. In this paper we review existing optimization methods
for solving convex and nonconvex relaxations/variants of robust PCA, discuss
their advantages and disadvantages, and elaborate on their convergence
behaviors. We also provide some insights for possible future research
directions including new algorithmic frameworks that might be suitable for
implementing on multi-processor setting to handle large-scale problems.
Efficient and Accurate MRI Super-Resolution using a Generative Adversarial Network and 3D Multi-Level Densely Connected Network
Authors: Yuhua Chen, Feng Shi, Anthony G. Christodoulou, Zhengwei Zhou, Yibin Xie, Debiao Li
Date: 2018-03-04 00:00:00
Tasks: Generative Adversarial Network, Image Super-Resolution, Super-Resolution

High-resolution (HR) magnetic resonance images (MRI) provide detailed
anatomical information important for clinical application and quantitative
image analysis. However, HR MRI conventionally comes at the cost of longer scan
time, smaller spatial coverage, and lower signal-to-noise ratio (SNR). Recent
studies have shown that single image super-resolution (SISR), a technique to
recover HR details from one single low-resolution (LR) input image, could
provide high-quality image details with the help of advanced deep convolutional
neural networks (CNN). However, deep neural networks consume memory heavily and
run slowly, especially in 3D settings. In this paper, we propose a novel 3D
neural network design, namely a multi-level densely connected super-resolution
network (mDCSRN) with generative adversarial network (GAN)-guided training. The
mDCSRN quickly trains and inferences and the GAN promotes realistic output
hardly distinguishable from original HR images. Our results from experiments on
a dataset with 1,113 subjects show that our new architecture beats other
popular deep learning methods in recovering 4x resolution-downgraded im-ages
and runs 6x faster.
Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry
Authors: Maximilian Nickel, Douwe Kiela
Date: 2018-06-09 00:00:00
Proceeding: ICML 2018 7

We are concerned with the discovery of hierarchical relationships from
large-scale unstructured similarity scores. For this purpose, we study
different models of hyperbolic space and find that learning embeddings in the
Lorentz model is substantially more efficient than in the Poincar\'e-ball
model. We show that the proposed approach allows us to learn high-quality
embeddings of large taxonomies which yield improvements over Poincar\'e
embeddings, especially in low dimensions. Lastly, we apply our model to
discover hierarchies in two real-world datasets: we show that an embedding in
hyperbolic space can reveal important aspects of a company's organizational
structure as well as reveal historical relationships between language families.
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Authors: Hideaki Imamura, Issei Sato, Masashi Sugiyama
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Clustering

While crowdsourcing has become an important means to label data, there is
great interest in estimating the ground truth from unreliable labels produced
by crowdworkers. The Dawid and Skene (DS) model is one of the most well-known
models in the study of crowdsourcing. Despite its practical popularity,
theoretical error analysis for the DS model has been conducted only under
restrictive assumptions on class priors, confusion matrices, or the number of
labels each worker provides. In this paper, we derive a minimax error rate
under more practical setting for a broader class of crowdsourcing models
including the DS model as a special case. We further propose the worker
clustering model, which is more practical than the DS model under real
crowdsourcing settings. The wide applicability of our theoretical analysis
allows us to immediately investigate the behavior of this proposed model, which
can not be analyzed by existing studies. Experimental results showed that there
is a strong similarity between the lower bound of the minimax error rate
derived by our theoretical analysis and the empirical error of the estimated
value.
Joint Stem Detection and Crop-Weed Classification for Plant-specific Treatment in Precision Farming
Authors: Philipp Lottes, Jens Behley, Nived Chebrolu, Andres Milioto, Cyrill Stachniss
Date: 2018-06-09 00:00:00
Tasks: General Classification, Semantic Segmentation

Applying agrochemicals is the default procedure for conventional weed control
in crop production, but has negative impacts on the environment. Robots have
the potential to treat every plant in the field individually and thus can
reduce the required use of such chemicals. To achieve that, robots need the
ability to identify crops and weeds in the field and must additionally select
effective treatments. While certain types of weed can be treated mechanically,
other types need to be treated by (selective) spraying. In this paper, we
present an approach that provides the necessary information for effective
plant-specific treatment. It outputs the stem location for weeds, which allows
for mechanical treatments, and the covered area of the weed for selective
spraying. Our approach uses an end-to-end trainable fully convolutional network
that simultaneously estimates stem positions as well as the covered area of
crops and weeds. It jointly learns the class-wise stem detection and the
pixel-wise semantic segmentation. Experimental evaluations on different
real-world datasets show that our approach is able to reliably solve this
problem. Compared to state-of-the-art approaches, our approach not only
substantially improves the stem detection accuracy, i.e., distinguishing crop
and weed stems, but also provides an improvement in the semantic segmentation
performance.
Fully Convolutional Networks with Sequential Information for Robust Crop and Weed Detection in Precision Farming
Authors: Philipp Lottes, Jens Behley, Andres Milioto, Cyrill Stachniss
Date: 2018-06-09 00:00:00
Tasks: Classification, General Classification, Semantic Segmentation

Reducing the use of agrochemicals is an important component towards
sustainable agriculture. Robots that can perform targeted weed control offer
the potential to contribute to this goal, for example, through specialized
weeding actions such as selective spraying or mechanical weed removal. A
prerequisite of such systems is a reliable and robust plant classification
system that is able to distinguish crop and weed in the field. A major
challenge in this context is the fact that different fields show a large
variability. Thus, classification systems have to robustly cope with
substantial environmental changes with respect to weed pressure and weed types,
growth stages of the crop, visual appearance, and soil conditions. In this
paper, we propose a novel crop-weed classification system that relies on a
fully convolutional network with an encoder-decoder structure and incorporates
spatial information by considering image sequences. Exploiting the crop
arrangement information that is observable from the image sequences enables our
system to robustly estimate a pixel-wise labeling of the images into crop and
weed, i.e., a semantic segmentation. We provide a thorough experimental
evaluation, which shows that our system generalizes well to previously unseen
fields under varying environmental conditions --- a key capability to actually
use such systems in precision framing. We provide comparisons to other
state-of-the-art approaches and show that our system substantially improves the
accuracy of crop-weed classification without requiring a retraining of the
model.
Learning Scene Gist with Convolutional Neural Networks to Improve Object Recognition
Authors: Kevin Wu, Eric Wu, Gabriel Kreiman
Date: 2018-03-06 00:00:00
Tasks: Object, Object Recognition, Scene Understanding

Advancements in convolutional neural networks (CNNs) have made significant
strides toward achieving high performance levels on multiple object recognition
tasks. While some approaches utilize information from the entire scene to
propose regions of interest, the task of interpreting a particular region or
object is still performed independently of other objects and features in the
image. Here we demonstrate that a scene's 'gist' can significantly contribute
to how well humans can recognize objects. These findings are consistent with
the notion that humans foveate on an object and incorporate information from
the periphery to aid in recognition. We use a biologically inspired two-part
convolutional neural network ('GistNet') that models the fovea and periphery to
provide a proof-of-principle demonstration that computational object
recognition can significantly benefit from the gist of the scene as contextual
information. Our model yields accuracy improvements of up to 50% in certain
object categories when incorporating contextual gist, while only increasing the
original model size by 5%. This proposed model mirrors our intuition about how
the human visual system recognizes objects, suggesting specific biologically
plausible constraints to improve machine vision and building initial steps
towards the challenge of scene understanding.
Deterministic Stretchy Regression
Authors: Kar-Ann Toh, Lei Sun, Zhiping Lin
Date: 2018-06-09 00:00:00
Tasks: regression

An extension of the regularized least-squares in which the estimation
parameters are stretchable is introduced and studied in this paper. The
solution of this ridge regression with stretchable parameters is given in
primal and dual spaces and in closed-form. Essentially, the proposed solution
stretches the covariance computation by a power term, thereby compressing or
amplifying the estimation parameters. To maintain the computation of power root
terms within the real space, an input transformation is proposed. The results
of an empirical evaluation in both synthetic and real-world data illustrate
that the proposed method is effective for compressive learning with
high-dimensional data.
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Authors: Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
Date: 2018-02-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Graph Embedding

A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is
proposed for multi-view feature learning with many-to-many associations so that
it generalizes various existing multi-view methods. PMvGE is a probabilistic
model for predicting new associations via graph embedding of the nodes of data
vectors with links of their associations. Multi-view data vectors with
many-to-many associations are transformed by neural networks to feature vectors
in a shared space, and the probability of new association between two data
vectors is modeled by the inner product of their feature vectors. While
existing multi-view feature learning techniques can treat only either of
many-to-many association or non-linear transformation, PMvGE can treat both
simultaneously. By combining Mercer's theorem and the universal approximation
theorem, we prove that PMvGE learns a wide class of similarity measures across
views. Our likelihood-based estimator enables efficient computation of
non-linear transformations of data vectors in large-scale datasets by minibatch
SGD, and numerical experiments illustrate that PMvGE outperforms existing
multi-view methods.
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Authors: Reza Mahjourian, Martin Wicke, Anelia Angelova
Date: 2018-02-15 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D geometry, Depth And Camera Motion, Depth Estimation, Monocular Depth Estimation, Simultaneous Localization and Mapping

We present a novel approach for unsupervised learning of depth and ego-motion
from monocular video. Unsupervised learning removes the need for separate
supervisory signals (depth or ego-motion ground truth, or multi-view video).
Prior work in unsupervised depth learning uses pixel-wise or gradient-based
losses, which only consider pixels in small local neighborhoods. Our main
contribution is to explicitly consider the inferred 3D geometry of the scene,
enforcing consistency of the estimated 3D point clouds and ego-motion across
consecutive frames. This is a challenging task and is solved by a novel
(approximate) backpropagation algorithm for aligning 3D structures.
  We combine this novel 3D-based loss with 2D losses based on photometric
quality of frame reconstructions using estimated depth and ego-motion from
adjacent frames. We also incorporate validity masks to avoid penalizing areas
in which no useful information exists.
  We test our algorithm on the KITTI dataset and on a video dataset captured on
an uncalibrated mobile phone camera. Our proposed approach consistently
improves depth estimates on both datasets, and outperforms the state-of-the-art
for both depth and ego-motion. Because we only require a simple video, learning
depth and ego-motion on large and varied datasets becomes possible. We
demonstrate this by training on the low quality uncalibrated video dataset and
evaluating on KITTI, ranking among top performing prior methods which are
trained on KITTI itself.
Going Deeper in Spiking Neural Networks: VGG and Residual Architectures
Authors: Abhronil Sengupta, Yuting Ye, Robert Wang, Chiao Liu, Kaushik Roy
Date: 2018-02-07 00:00:00

Over the past few years, Spiking Neural Networks (SNNs) have become popular
as a possible pathway to enable low-power event-driven neuromorphic hardware.
However, their application in machine learning have largely been limited to
very shallow neural network architectures for simple problems. In this paper,
we propose a novel algorithmic technique for generating an SNN with a deep
architecture, and demonstrate its effectiveness on complex visual recognition
problems such as CIFAR-10 and ImageNet. Our technique applies to both VGG and
Residual network architectures, with significantly better accuracy than the
state-of-the-art. Finally, we present analysis of the sparse event-driven
computations to demonstrate reduced hardware overhead when operating in the
spiking domain.
CS-VQA: Visual Question Answering with Compressively Sensed Images
Authors: Li-Chi Huang, Kuldeep Kulkarni, Anik Jha, Suhas Lohit, Suren Jayasuriya, Pavan Turaga
Date: 2018-06-08 00:00:00
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

Visual Question Answering (VQA) is a complex semantic task requiring both
natural language processing and visual recognition. In this paper, we explore
whether VQA is solvable when images are captured in a sub-Nyquist compressive
paradigm. We develop a series of deep-network architectures that exploit
available compressive data to increasing degrees of accuracy, and show that VQA
is indeed solvable in the compressed domain. Our results show that there is
nominal degradation in VQA performance when using compressive measurements, but
that accuracy can be recovered when VQA pipelines are used in conjunction with
state-of-the-art deep neural networks for CS reconstruction. The results
presented yield important implications for resource-constrained VQA
applications.
Self-supervisory Signals for Object Discovery and Detection
Authors: Etienne Pot, Alexander Toshev, Jana Kosecka
Date: 2018-06-08 00:00:00
Tasks: Clustering, Object, Object Discovery

In robotic applications, we often face the challenge of discovering new
objects while having very little or no labelled training data. In this paper we
explore the use of self-supervision provided by a robot traversing an
environment to learn representations of encountered objects. Knowledge of
ego-motion and depth perception enables the agent to effectively associate
multiple object proposals, which serve as training data for learning object
representations from unlabelled images. We demonstrate the utility of this
representation in two ways. First, we can automatically discover objects by
performing clustering in the learned embedding space. Each resulting cluster
contains examples of one instance seen from various viewpoints and scales.
Second, given a small number of labeled images, we can efficiently learn
detectors for these labels. In the few-shot regime, these detectors have a
substantially higher mAP of 0.22 compared to 0.12 of off-the-shelf standard
detectors trained on this limited data. Thus, the proposed self-supervision
results in effective environment specific object discovery and detection at no
or very small human labeling cost.
#SarcasmDetection is soooo general! Towards a Domain-Independent Approach for Detecting Sarcasm
Authors: Natalie Parde, Rodney D. Nielsen
Date: 2018-06-08 00:00:00
Tasks: Domain Adaptation, Sarcasm Detection

Automatic sarcasm detection methods have traditionally been designed for
maximum performance on a specific domain. This poses challenges for those
wishing to transfer those approaches to other existing or novel domains, which
may be typified by very different language characteristics. We develop a
general set of features and evaluate it under different training scenarios
utilizing in-domain and/or out-of-domain training data. The best-performing
scenario, training on both while employing a domain adaptation step, achieves
an F1 of 0.780, which is well above baseline F1-measures of 0.515 and 0.345. We
also show that the approach outperforms the best results from prior work on the
same target domain.
Deep Models of Interactions Across Sets
Authors: Jason Hartford, Devon R Graham, Kevin Leyton-Brown, Siamak Ravanbakhsh
Date: 2018-03-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Collaborative Filtering, Matrix Completion, Recommendation Systems, TAG

We use deep learning to model interactions across two or more sets of
objects, such as user-movie ratings, protein-drug bindings, or ternary
user-item-tag interactions. The canonical representation of such interactions
is a matrix (or a higher-dimensional tensor) with an exchangeability property:
the encoding's meaning is not changed by permuting rows or columns. We argue
that models should hence be Permutation Equivariant (PE): constrained to make
the same predictions across such permutations. We present a parameter-sharing
scheme and prove that it could not be made any more expressive without
violating PE. This scheme yields three benefits. First, we demonstrate
state-of-the-art performance on multiple matrix completion benchmarks. Second,
our models require a number of parameters independent of the numbers of
objects, and thus scale well to large datasets. Third, models can be queried
about new objects that were not available at training time, but for which
interactions have since been observed. In experiments, our models achieved
surprisingly good generalization performance on this matrix extrapolation task,
both within domains (e.g., new users and new movies drawn from the same
distribution used for training) and even across domains (e.g., predicting music
ratings after training on movies).
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices
Authors: Jie Zhang, Xiaolong Wang, Dawei Li, Yalin Wang
Date: 2018-06-04 00:00:00
Tasks: Dictionary Learning, Language Modeling, Language Modelling, Model Compression

Recurrent neural networks (RNNs) achieve cutting-edge performance on a
variety of problems. However, due to their high computational and memory
demands, deploying RNNs on resource constrained mobile devices is a challenging
task. To guarantee minimum accuracy loss with higher compression rate and
driven by the mobile resource requirement, we introduce a novel model
compression approach DirNet based on an optimized fast dictionary learning
algorithm, which 1) dynamically mines the dictionary atoms of the projection
dictionary matrix within layer to adjust the compression rate 2) adaptively
changes the sparsity of sparse codes cross the hierarchical layers.
Experimental results on language model and an ASR model trained with a 1000h
speech dataset demonstrate that our method significantly outperforms prior
approaches. Evaluated on off-the-shelf mobile devices, we are able to reduce
the size of original model by eight times with real-time model inference and
negligible accuracy loss.