pwc-archive/papers-with-abstracts huggingface dataset:
Evaluating Visual Conversational Agents via Cooperative Human-AI Games
Authors: Prithvijit Chattopadhyay, Deshraj Yadav, Viraj Prabhu, Arjun Chandrasekaran, Abhishek Das, Stefan Lee, Dhruv Batra, Devi Parikh
Date: 2017-08-17 00:00:00
Tasks: Benchmarking

As AI continues to advance, human-AI teams are inevitable. However, progress
in AI is routinely measured in isolation, without a human in the loop. It is
crucial to benchmark progress in AI, not just in isolation, but also in terms
of how it translates to helping humans perform certain tasks, i.e., the
performance of human-AI teams.
  In this work, we design a cooperative game - GuessWhich - to measure human-AI
team performance in the specific context of the AI being a visual
conversational agent. GuessWhich involves live interaction between the human
and the AI. The AI, which we call ALICE, is provided an image which is unseen
by the human. Following a brief description of the image, the human questions
ALICE about this secret image to identify it from a fixed pool of images.
  We measure performance of the human-ALICE team by the number of guesses it
takes the human to correctly identify the secret image after a fixed number of
dialog rounds with ALICE. We compare performance of the human-ALICE teams for
two versions of ALICE. Our human studies suggest a counterintuitive trend -
that while AI literature shows that one version outperforms the other when
paired with an AI questioner bot, we find that this improvement in AI-AI
performance does not translate to improved human-AI performance. This suggests
a mismatch between benchmarking of AI in isolation and in the context of
human-AI teams.
Multi-space Variational Encoder-Decoders for Semi-supervised Labeled Sequence Transduction
Authors: Chunting Zhou, Graham Neubig
Date: 2017-04-06 00:00:00
Proceeding: ACL 2017 7
Tasks: Morphological Inflection

Labeled sequence transduction is a task of transforming one sequence into
another sequence that satisfies desiderata specified by a set of labels. In
this paper we propose multi-space variational encoder-decoders, a new model for
labeled sequence transduction with semi-supervised learning. The generative
model can use neural networks to handle both discrete and continuous latent
variables to exploit various features of data. Experiments show that our model
provides not only a powerful supervised framework but also can effectively take
advantage of the unlabeled data. On the SIGMORPHON morphological inflection
benchmark, our model outperforms single-model state-of-art results by a large
margin for the majority of languages.
Hand2Face: Automatic Synthesis and Recognition of Hand Over Face Occlusions
Authors: Behnaz Nojavanasghari, Charles. E. Hughes, Tadas Baltrusaitis, Louis-Philippe Morency
Date: 2017-08-01 00:00:00

A person's face discloses important information about their affective state.
Although there has been extensive research on recognition of facial
expressions, the performance of existing approaches is challenged by facial
occlusions. Facial occlusions are often treated as noise and discarded in
recognition of affective states. However, hand over face occlusions can provide
additional information for recognition of some affective states such as
curiosity, frustration and boredom. One of the reasons that this problem has
not gained attention is the lack of naturalistic occluded faces that contain
hand over face occlusions as well as other types of occlusions. Traditional
approaches for obtaining affective data are time demanding and expensive, which
limits researchers in affective computing to work on small datasets. This
limitation affects the generalizability of models and deprives researchers from
taking advantage of recent advances in deep learning that have shown great
success in many fields but require large volumes of data. In this paper, we
first introduce a novel framework for synthesizing naturalistic facial
occlusions from an initial dataset of non-occluded faces and separate images of
hands, reducing the costly process of data collection and annotation. We then
propose a model for facial occlusion type recognition to differentiate between
hand over face occlusions and other types of occlusions such as scarves, hair,
glasses and objects. Finally, we present a model to localize hand over face
occlusions and identify the occluded regions of the face.
An Improved Residual LSTM Architecture for Acoustic Modeling
Authors: Lu Huang, Jiasong Sun, Ji Xu, Yi Yang
Date: 2017-08-17 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

Long Short-Term Memory (LSTM) is the primary recurrent neural networks
architecture for acoustic modeling in automatic speech recognition systems.
Residual learning is an efficient method to help neural networks converge
easier and faster. In this paper, we propose several types of residual LSTM
methods for our acoustic modeling. Our experiments indicate that, compared with
classic LSTM, our architecture shows more than 8% relative reduction in Phone
Error Rate (PER) on TIMIT tasks. At the same time, our residual fast LSTM
approach shows 4% relative reduction in PER on the same task. Besides, we find
that all this architecture could have good results on THCHS-30, Librispeech and
Switchboard corpora.
Which Encoding is the Best for Text Classification in Chinese, English, Japanese and Korean?
Authors: Xiang Zhang, Yann Lecun
Date: 2017-08-08 00:00:00
Tasks: General Classification, Text Classification

This article offers an empirical study on the different ways of encoding
Chinese, Japanese, Korean (CJK) and English languages for text classification.
Different encoding levels are studied, including UTF-8 bytes, characters,
words, romanized characters and romanized words. For all encoding levels,
whenever applicable, we provide comparisons with linear models, fastText and
convolutional networks. For convolutional networks, we compare between encoding
mechanisms using character glyph images, one-hot (or one-of-n) encoding, and
embedding. In total there are 473 models, using 14 large-scale text
classification datasets in 4 languages including Chinese, English, Japanese and
Korean. Some conclusions from these results include that byte-level one-hot
encoding based on UTF-8 consistently produces competitive results for
convolutional networks, that word-level n-grams linear models are competitive
even without perfect word segmentation, and that fastText provides the best
result using character-level n-gram encoding but can overfit when the features
are overly rich.
Convolutional Neural Networks for Non-iterative Reconstruction of Compressively Sensed Images
Authors: Suhas Lohit, Kuldeep Kulkarni, Ronan Kerviche, Pavan Turaga, Amit Ashok
Date: 2017-08-15 00:00:00
Tasks: Compressive Sensing, Object Tracking

Traditional algorithms for compressive sensing recovery are computationally
expensive and are ineffective at low measurement rates. In this work, we
propose a data driven non-iterative algorithm to overcome the shortcomings of
earlier iterative algorithms. Our solution, ReconNet, is a deep neural network,
whose parameters are learned end-to-end to map block-wise compressive
measurements of the scene to the desired image blocks. Reconstruction of an
image becomes a simple forward pass through the network and can be done in
real-time. We show empirically that our algorithm yields reconstructions with
higher PSNRs compared to iterative algorithms at low measurement rates and in
presence of measurement noise. We also propose a variant of ReconNet which uses
adversarial loss in order to further improve reconstruction quality. We discuss
how adding a fully connected layer to the existing ReconNet architecture allows
for jointly learning the measurement matrix and the reconstruction algorithm in
a single network. Experiments on real data obtained from a block compressive
imager show that our networks are robust to unseen sensor noise. Finally,
through an experiment in object tracking, we show that even at very low
measurement rates, reconstructions using our algorithm possess rich semantic
content that can be used for high level inference.
An Ensemble Quadratic Echo State Network for Nonlinear Spatio-Temporal Forecasting
Authors: Patrick L. McDermott, Christopher K. Wikle
Date: 2017-08-16 00:00:00
Tasks: Management, Spatio-Temporal Forecasting, Uncertainty Quantification

Spatio-temporal data and processes are prevalent across a wide variety of
scientific disciplines. These processes are often characterized by nonlinear
time dynamics that include interactions across multiple scales of spatial and
temporal variability. The data sets associated with many of these processes are
increasing in size due to advances in automated data measurement, management,
and numerical simulator output. Non- linear spatio-temporal models have only
recently seen interest in statistics, but there are many classes of such models
in the engineering and geophysical sciences. Tradi- tionally, these models are
more heuristic than those that have been presented in the statistics
literature, but are often intuitive and quite efficient computationally. We
show here that with fairly simple, but important, enhancements, the echo state
net- work (ESN) machine learning approach can be used to generate long-lead
forecasts of nonlinear spatio-temporal processes, with reasonable uncertainty
quantification, and at only a fraction of the computational expense of a
traditional parametric nonlinear spatio-temporal models.
CAD Priors for Accurate and Flexible Instance Reconstruction
Authors: Tolga Birdal, Slobodan Ilic
Date: 2017-05-08 00:00:00
Proceeding: ICCV 2017 10
Tasks: 3D Reconstruction, Novel Object Detection, Object, object-detection, Object Detection

We present an efficient and automatic approach for accurate reconstruction of
instances of big 3D objects from multiple, unorganized and unstructured point
clouds, in presence of dynamic clutter and occlusions. In contrast to
conventional scanning, where the background is assumed to be rather static, we
aim at handling dynamic clutter where background drastically changes during the
object scanning. Currently, it is tedious to solve this with available methods
unless the object of interest is first segmented out from the rest of the
scene. We address the problem by assuming the availability of a prior CAD
model, roughly resembling the object to be reconstructed. This assumption
almost always holds in applications such as industrial inspection or reverse
engineering. With aid of this prior acting as a proxy, we propose a fully
enhanced pipeline, capable of automatically detecting and segmenting the object
of interest from scenes and creating a pose graph, online, with linear
complexity. This allows initial scan alignment to the CAD model space, which is
then refined without the CAD constraint to fully recover a high fidelity 3D
reconstruction, accurate up to the sensor noise level. We also contribute a
novel object detection method, local implicit shape models (LISM) and give a
fast verification scheme. We evaluate our method on multiple datasets,
demonstrating the ability to accurately reconstruct objects from small sizes up
to $125m^3$.
ConvNet Architecture Search for Spatiotemporal Feature Learning
Authors: Du Tran, Jamie Ray, Zheng Shou, Shih-Fu Chang, Manohar Paluri
Date: 2017-08-16 00:00:00
Tasks: Action Classification, Action Recognition, Image Captioning, Neural Architecture Search, object-detection, Object Detection, Semantic Segmentation

Learning image representations with ConvNets by pre-training on ImageNet has
proven useful across many visual understanding tasks including object
detection, semantic segmentation, and image captioning. Although any image
representation can be applied to video frames, a dedicated spatiotemporal
representation is still vital in order to incorporate motion patterns that
cannot be captured by appearance based models alone. This paper presents an
empirical ConvNet architecture search for spatiotemporal feature learning,
culminating in a deep 3-dimensional (3D) Residual ConvNet. Our proposed
architecture outperforms C3D by a good margin on Sports-1M, UCF101, HMDB51,
THUMOS14, and ASLAN while being 2 times faster at inference time, 2 times
smaller in model size, and having a more compact representation.
Neural Factorization Machines for Sparse Predictive Analytics
Authors: Xiangnan He, Tat-Seng Chua
Date: 2017-08-16 00:00:00
Tasks: Link Prediction

Many predictive tasks of web applications need to model categorical
variables, such as user IDs and demographics like genders and occupations. To
apply standard machine learning techniques, these categorical predictors are
always converted to a set of binary features via one-hot encoding, making the
resultant feature vector highly sparse. To learn from such sparse data
effectively, it is crucial to account for the interactions between features.
  Factorization Machines (FMs) are a popular solution for efficiently using the
second-order feature interactions. However, FM models feature interactions in a
linear way, which can be insufficient for capturing the non-linear and complex
inherent structure of real-world data. While deep neural networks have recently
been applied to learn non-linear feature interactions in industry, such as the
Wide&Deep by Google and DeepCross by Microsoft, the deep structure meanwhile
makes them difficult to train.
  In this paper, we propose a novel model Neural Factorization Machine (NFM)
for prediction under sparse settings. NFM seamlessly combines the linearity of
FM in modelling second-order feature interactions and the non-linearity of
neural network in modelling higher-order feature interactions. Conceptually,
NFM is more expressive than FM since FM can be seen as a special case of NFM
without hidden layers. Empirical results on two regression tasks show that with
one hidden layer only, NFM significantly outperforms FM with a 7.3% relative
improvement. Compared to the recent deep learning methods Wide&Deep and
DeepCross, our NFM uses a shallower structure but offers better performance,
being much easier to train and tune in practice.
Identifying Growth-Patterns in Children by Applying Cluster analysis to Electronic Medical Records
Authors: Moumita Bhattacharya, Deborah Ehrenthal, Hagit Shatkay
Date: 2017-08-16 00:00:00
Tasks: Clustering

Obesity is one of the leading health concerns in the United States.
Researchers and health care providers are interested in understanding factors
affecting obesity and detecting the likelihood of obesity as early as possible.
In this paper, we set out to recognize children who have higher risk of obesity
by identifying distinct growth patterns in them. This is done by using
clustering methods, which group together children who share similar body
measurements over a period of time. The measurements characterizing children
within the same cluster are plotted as a function of age. We refer to these
plots as growthpattern curves. We show that distinct growth-pattern curves are
associated with different clusters and thus can be used to separate children
into the topmost (heaviest), middle, or bottom-most cluster based on early
growth measurements.
Salt-n-pepper noise filtering using Cellular Automata
Authors: Dimitrios Tourtounis, Nikolaos Mitianoudis, Georgios Ch. Sirakoulis
Date: 2017-08-16 00:00:00

Cellular Automata (CA) have been considered one of the most pronounced
parallel computational tools in the recent era of nature and bio-inspired
computing. Taking advantage of their local connectivity, the simplicity of
their design and their inherent parallelism, CA can be effectively applied to
many image processing tasks. In this paper, a CA approach for efficient
salt-n-pepper noise filtering in grayscale images is presented. Using a 2D
Moore neighborhood, the classified "noisy" cells are corrected by averaging the
non-noisy neighboring cells. While keeping the computational burden really low,
the proposed approach succeeds in removing high-noise levels from various
images and yields promising qualitative and quantitative results, compared to
state-of-the-art techniques.
Two-Phase Learning for Weakly Supervised Object Localization
Authors: Dahun Kim, Donghyeon Cho, Donggeun Yoo, In So Kweon
Date: 2017-08-07 00:00:00
Proceeding: ICCV 2017 10
Tasks: Object, Object Localization, Segmentation, Semantic Segmentation, TAR, Vocal Bursts Valence Prediction, Weakly-Supervised Object Localization, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

Weakly supervised semantic segmentation and localiza- tion have a problem of
focusing only on the most important parts of an image since they use only
image-level annota- tions. In this paper, we solve this problem fundamentally
via two-phase learning. Our networks are trained in two steps. In the first
step, a conventional fully convolutional network (FCN) is trained to find the
most discriminative parts of an image. In the second step, the activations on
the most salient parts are suppressed by inference conditional feedback, and
then the second learning is performed to find the area of the next most
important parts. By combining the activations of both phases, the entire
portion of the tar- get object can be captured. Our proposed training scheme is
novel and can be utilized in well-designed techniques for weakly supervised
semantic segmentation, salient region detection, and object location
prediction. Detailed experi- ments demonstrate the effectiveness of our
two-phase learn- ing in each task.
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Authors: Saber Moradi, Ning Qiao, Fabio Stefanini, Giacomo Indiveri
Date: 2017-08-14 00:00:00

Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.
Free Space Estimation using Occupancy Grids and Dynamic Object Detection
Authors: Raghavender Sahdev
Date: 2017-08-16 00:00:00
Tasks: Autonomous Driving, object-detection, Object Detection

In this paper we present an approach to estimate Free Space from a Stereo
image pair using stochastic occupancy grids. We do this in the domain of
autonomous driving on the famous benchmark dataset KITTI. Later based on the
generated occupancy grid we match 2 image sequences to compute the top view
representation of the map. We do this to map the environment. We compute a
transformation between the occupancy grids of two successive images and use it
to compute the top view map. Two issues need to be addressed for mapping are
discussed - computing a map and dealing with dynamic objects for computing the
map. Dynamic Objects are detected in successive images based on an idea similar
to tracking of foreground objects from the background objects based on motion
flow. A novel RANSAC based segmentation approach has been proposed here to
address this issue.
Warp: a method for neural network interpretability applied to gene expression profiles
Authors: Trofimov Assya, Lemieux Sebastien, Perreault Claude
Date: 2017-08-16 00:00:00

We show a proof of principle for warping, a method to interpret the inner
working of neural networks in the context of gene expression analysis. Warping
is an efficient way to gain insight to the inner workings of neural nets and
make them more interpretable. We demonstrate the ability of warping to recover
meaningful information for a given class on a samplespecific individual basis.
We found warping works well in both linearly and nonlinearly separable
datasets. These encouraging results show that warping has a potential to be the
answer to neural networks interpretability in computational biology.
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE
Authors: Andrey Boytsov, Francois Fouquet, Thomas Hartmann, Yves LeTraon
Date: 2017-08-16 00:00:00
Tasks: Dimensionality Reduction, Outlier Detection, Vocal Bursts Intensity Prediction

T-distributed stochastic neighbor embedding (tSNE) is a popular and
prize-winning approach for dimensionality reduction and visualizing
high-dimensional data. However, tSNE is non-parametric: once visualization is
built, tSNE is not designed to incorporate additional data into existing
representation. It highly limits the applicability of tSNE to the scenarios
where data are added or updated over time (like dashboards or series of data
snapshots).
  In this paper we propose, analyze and evaluate LION-tSNE (Local Interpolation
with Outlier coNtrol) - a novel approach for incorporating new data into tSNE
representation. LION-tSNE is based on local interpolation in the vicinity of
training data, outlier detection and a special outlier mapping algorithm. We
show that LION-tSNE method is robust both to outliers and to new samples from
existing clusters. We also discuss multiple possible improvements for special
cases.
  We compare LION-tSNE to a comprehensive list of possible benchmark approaches
that include multiple interpolation techniques, gradient descent for new data,
and neural network approximation.