pwc-archive/papers-with-abstracts huggingface dataset:
Probabilistic Sparse Subspace Clustering Using Delayed Association
Authors: Maryam Jaberi, Marianna Pensky, Hassan Foroosh
Date: 2018-08-28 00:00:00
Tasks: Clustering

Discovering and clustering subspaces in high-dimensional data is a
fundamental problem of machine learning with a wide range of applications in
data mining, computer vision, and pattern recognition. Earlier methods divided
the problem into two separate stages of finding the similarity matrix and
finding clusters. Similar to some recent works, we integrate these two steps
using a joint optimization approach. We make the following contributions: (i)
we estimate the reliability of the cluster assignment for each point before
assigning a point to a subspace. We group the data points into two groups of
"certain" and "uncertain", with the assignment of latter group delayed until
their subspace association certainty improves. (ii) We demonstrate that delayed
association is better suited for clustering subspaces that have ambiguities,
i.e. when subspaces intersect or data are contaminated with outliers/noise.
(iii) We demonstrate experimentally that such delayed probabilistic association
leads to a more accurate self-representation and final clusters. The proposed
method has higher accuracy both for points that exclusively lie in one
subspace, and those that are on the intersection of subspaces. (iv) We show
that delayed association leads to huge reduction of computational cost, since
it allows for incremental spectral clustering.
Cycle-of-Learning for Autonomous Systems from Human Interaction
Authors: Nicholas R. Waytowich, Vinicius G. Goecks, Vernon J. Lawhern
Date: 2018-08-28 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We discuss different types of human-robot interaction paradigms in the
context of training end-to-end reinforcement learning algorithms. We provide a
taxonomy to categorize the types of human interaction and present our
Cycle-of-Learning framework for autonomous systems that combines different
human-interaction modalities with reinforcement learning. Two key concepts
provided by our Cycle-of-Learning framework are how it handles the integration
of the different human-interaction modalities (demonstration, intervention, and
evaluation) and how to define the switching criteria between them.
ARBEE: Towards Automated Recognition of Bodily Expression of Emotion In the Wild
Authors: Yu Luo, Jianbo Ye, Reginald B. Adams, Jr., Jia Li, Michelle G. Newman, James Z. Wang
Date: 2018-08-28 00:00:00
Tasks: Action Recognition

Humans are arguably innately prepared to comprehend others' emotional expressions from subtle body movements. If robots or computers can be empowered with this capability, a number of robotic applications become possible. Automatically recognizing human bodily expression in unconstrained situations, however, is daunting given the incomplete understanding of the relationship between emotional expressions and body movements. The current research, as a multidisciplinary effort among computer and information sciences, psychology, and statistics, proposes a scalable and reliable crowdsourcing approach for collecting in-the-wild perceived emotion data for computers to learn to recognize body languages of humans. To accomplish this task, a large and growing annotated dataset with 9,876 video clips of body movements and 13,239 human characters, named BoLD (Body Language Dataset), has been created. Comprehensive statistical analysis of the dataset revealed many interesting insights. A system to model the emotional expressions based on bodily movements, named ARBEE (Automated Recognition of Bodily Expression of Emotion), has also been developed and evaluated. Our analysis shows the effectiveness of Laban Movement Analysis (LMA) features in characterizing arousal, and our experiments using LMA features further demonstrate computability of bodily expression. We report and compare results of several other baseline methods which were developed for action recognition based on two different modalities, body skeleton, and raw image. The dataset and findings presented in this work will likely serve as a launchpad for future discoveries in body language understanding that will enable future robots to interact and collaborate more effectively with humans.
Multi-Reference Training with Pseudo-References for Neural Translation and Text Generation
Authors: Renjie Zheng, Mingbo Ma, Liang Huang
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Image Captioning, Machine Translation, Text Generation, Translation

Neural text generation, including neural machine translation, image
captioning, and summarization, has been quite successful recently. However,
during training time, typically only one reference is considered for each
example, even though there are often multiple references available, e.g., 4
references in NIST MT evaluations, and 5 references in image captioning data.
We first investigate several different ways of utilizing multiple human
references during training. But more importantly, we then propose an algorithm
to generate exponentially many pseudo-references by first compressing existing
human references into lattices and then traversing them to generate new
pseudo-references. These approaches lead to substantial improvements over
strong baselines in both machine translation (+1.5 BLEU) and image captioning
(+3.1 BLEU / +11.7 CIDEr).
Autonomous drone cinematographer: Using artistic principles to create smooth, safe, occlusion-free trajectories for aerial filming
Authors: Rogerio Bonatti, yanfu Zhang, Sanjiban Choudhury, Wenshan Wang, Sebastian Scherer
Date: 2018-08-28 00:00:00

Autonomous aerial cinematography has the potential to enable automatic
capture of aesthetically pleasing videos without requiring human intervention,
empowering individuals with the capability of high-end film studios. Current
approaches either only handle off-line trajectory generation, or offer
strategies that reason over short time horizons and simplistic representations
for obstacles, which result in jerky movement and low real-life applicability.
In this work we develop a method for aerial filming that is able to trade off
shot smoothness, occlusion, and cinematography guidelines in a principled
manner, even under noisy actor predictions. We present a novel algorithm for
real-time covariant gradient descent that we use to efficiently find the
desired trajectories by optimizing a set of cost functions. Experimental
results show that our approach creates attractive shots, avoiding obstacles and
occlusion 65 times over 1.25 hours of flight time, re-planning at 5 Hz with a
10 s time horizon. We robustly film human actors, cars and bicycles performing
different motion among obstacles, using various shot types.
On Learning 3D Face Morphable Model from In-the-wild Images
Authors: Luan Tran, Xiaoming Liu
Date: 2018-08-28 00:00:00
Tasks: 3D Reconstruction, Face Alignment, Image Generation

As a classic statistical model of 3D facial shape and albedo, 3D Morphable Model (3DMM) is widely used in facial analysis, e.g., model fitting, image synthesis. Conventional 3DMM is learned from a set of 3D face scans with associated well-controlled 2D face images, and represented by two sets of PCA basis functions. Due to the type and amount of training data, as well as, the linear bases, the representation power of 3DMM can be limited. To address these problems, this paper proposes an innovative framework to learn a nonlinear 3DMM model from a large set of in-the-wild face images, without collecting 3D face scans. Specifically, given a face image as input, a network encoder estimates the projection, lighting, shape and albedo parameters. Two decoders serve as the nonlinear 3DMM to map from the shape and albedo parameters to the 3D shape and albedo, respectively. With the projection parameter, lighting, 3D shape, and albedo, a novel analytically-differentiable rendering layer is designed to reconstruct the original input face. The entire network is end-to-end trainable with only weak supervision. We demonstrate the superior representation power of our nonlinear 3DMM over its linear counterpart, and its contribution to face alignment, 3D reconstruction, and face editing.
Temporal Saliency Adaptation in Egocentric Videos
Authors: Panagiotis Linardos, Eva Mohedano, Monica Cherto, Cathal Gurrin, Xavier Giro-i-Nieto
Date: 2018-08-28 00:00:00
Tasks: Saliency Prediction, Video Saliency Prediction

This work adapts a deep neural model for image saliency prediction to the
temporal domain of egocentric video. We compute the saliency map for each video
frame, firstly with an off-the-shelf model trained from static images, secondly
by adding a a convolutional or conv-LSTM layers trained with a dataset for
video saliency prediction. We study each configuration on EgoMon, a new dataset
made of seven egocentric videos recorded by three subjects in both free-viewing
and task-driven set ups. Our results indicate that the temporal adaptation is
beneficial when the viewer is not moving and observing the scene from a narrow
field of view. Encouraged by this observation, we compute and publish the
saliency maps for the EPIC Kitchens dataset, in which viewers are cooking.
Source code and models available at
https://imatge-upc.github.io/saliency-2018-videosalgan/
Explaining Character-Aware Neural Networks for Word-Level Prediction: Do They Discover Linguistic Rules?
Authors: Fréderic Godin, Kris Demuynck, Joni Dambre, Wesley De Neve, Thomas Demeester
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Morphological Tagging

Character-level features are currently used in different neural network-based
natural language processing algorithms. However, little is known about the
character-level patterns those models learn. Moreover, models are often
compared only quantitatively while a qualitative analysis is missing. In this
paper, we investigate which character-level patterns neural networks learn and
if those patterns coincide with manually-defined word segmentations and
annotations. To that end, we extend the contextual decomposition technique
(Murdoch et al. 2018) to convolutional neural networks which allows us to
compare convolutional neural networks and bidirectional long short-term memory
networks. We evaluate and compare these models for the task of morphological
tagging on three morphologically different languages and show that these models
implicitly discover understandable linguistic rules. Our implementation can be
found at https://github.com/FredericGodin/ContextualDecomposition-NLP .
Towards Semi-Supervised Learning for Deep Semantic Role Labeling
Authors: Sanket Vaibhav Mehta, Jay Yoon Lee, Jaime Carbonell
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Semantic Role Labeling

Neural models have shown several state-of-the-art performances on Semantic
Role Labeling (SRL). However, the neural models require an immense amount of
semantic-role corpora and are thus not well suited for low-resource languages
or domains. The paper proposes a semi-supervised semantic role labeling method
that outperforms the state-of-the-art in limited SRL training corpora. The
method is based on explicitly enforcing syntactic constraints by augmenting the
training objective with a syntactic-inconsistency loss component and uses
SRL-unlabeled instances to train a joint-objective LSTM. On CoNLL-2012 English
section, the proposed semi-supervised training with 1%, 10% SRL-labeled data
and varying amounts of SRL-unlabeled data achieves +1.58, +0.78 F1,
respectively, over the pre-trained models that were trained on SOTA
architecture with ELMo on the same SRL-labeled data. Additionally, by using the
syntactic-inconsistency loss on inference time, the proposed model achieves
+3.67, +2.1 F1 over pre-trained model on 1%, 10% SRL-labeled data,
respectively.
Hierarchical Quantized Representations for Script Generation
Authors: Noah Weber, Leena Shekhar, Niranjan Balasubramanian, Nathanael Chambers
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Language Modeling, Language Modelling, Quantization, Script Generation

Scripts define knowledge about how everyday scenarios (such as going to a
restaurant) are expected to unfold. One of the challenges to learning scripts
is the hierarchical nature of the knowledge. For example, a suspect arrested
might plead innocent or guilty, and a very different track of events is then
expected to happen. To capture this type of information, we propose an
autoencoder model with a latent space defined by a hierarchy of categorical
variables. We utilize a recently proposed vector quantization based approach,
which allows continuous embeddings to be associated with each latent variable
value. This permits the decoder to softly decide what portions of the latent
hierarchy to condition on by attending over the value embeddings for a given
setting. Our model effectively encodes and generates scripts, outperforming a
recent language modeling-based method on several standard tasks, and allowing
the autoencoder model to achieve substantially lower perplexity scores compared
to the previous language modeling-based method.
Lipschitz regularized Deep Neural Networks generalize and are adversarially robust
Authors: Chris Finlay, Jeff Calder, Bilal Abbasi, Adam Oberman
Date: 2018-08-28 00:00:00
Tasks: Adversarial Robustness

In this work we study input gradient regularization of deep neural networks, and demonstrate that such regularization leads to generalization proofs and improved adversarial robustness. The proof of generalization does not overcome the curse of dimensionality, but it is independent of the number of layers in the networks. The adversarial robustness regularization combines adversarial training, which we show to be equivalent to Total Variation regularization, with Lipschitz regularization. We demonstrate empirically that the regularized models are more robust, and that gradient norms of images can be used for attack detection.
Deep Lidar CNN to Understand the Dynamics of Moving Vehicles
Authors: Victor Vaquero, Alberto Sanfeliu, Francesc Moreno-Noguer
Date: 2018-08-28 00:00:00
Tasks: Autonomous Driving, Optical Flow Estimation

Perception technologies in Autonomous Driving are experiencing their golden
age due to the advances in Deep Learning. Yet, most of these systems rely on
the semantically rich information of RGB images. Deep Learning solutions
applied to the data of other sensors typically mounted on autonomous cars (e.g.
lidars or radars) are not explored much. In this paper we propose a novel
solution to understand the dynamics of moving vehicles of the scene from only
lidar information. The main challenge of this problem stems from the fact that
we need to disambiguate the proprio-motion of the 'observer' vehicle from that
of the external 'observed' vehicles. For this purpose, we devise a CNN
architecture which at testing time is fed with pairs of consecutive lidar
scans. However, in order to properly learn the parameters of this network,
during training we introduce a series of so-called pretext tasks which also
leverage on image data. These tasks include semantic information about
vehicleness and a novel lidar-flow feature which combines standard image-based
optical flow with lidar scans. We obtain very promising results and show that
including distilled image information only during training, allows improving
the inference results of the network at test time, even when image data is no
longer used.
Layer Trajectory LSTM
Authors: Jinyu Li, Changliang Liu, Yifan Gong
Date: 2018-08-28 00:00:00

It is popular to stack LSTM layers to get better modeling power, especially
when large amount of training data is available. However, an LSTM-RNN with too
many vanilla LSTM layers is very hard to train and there still exists the
gradient vanishing issue if the network goes too deep. This issue can be
partially solved by adding skip connections between layers, such as residual
LSTM. In this paper, we propose a layer trajectory LSTM (ltLSTM) which builds a
layer-LSTM using all the layer outputs from a standard multi-layer time-LSTM.
This layer-LSTM scans the outputs from time-LSTMs, and uses the summarized
layer trajectory information for final senone classification. The
forward-propagation of time-LSTM and layer-LSTM can be handled in two separate
threads in parallel so that the network computation time is the same as the
standard time-LSTM. With a layer-LSTM running through layers, a gated path is
provided from the output layer to the bottom layer, alleviating the gradient
vanishing issue. Trained with 30 thousand hours of EN-US Microsoft internal
data, the proposed ltLSTM performed significantly better than the standard
multi-layer LSTM and residual LSTM, with up to 9.0% relative word error rate
reduction across different tasks.
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder
Authors: Basma Abdulaimma, Paul Fergus, Carl Chalmers
Date: 2018-08-28 00:00:00
Tasks: regression

2 Diabetes is a leading worldwide public health concern, and its increasing
prevalence has significant health and economic importance in all nations. The
condition is a multifactorial disorder with a complex aetiology. The genetic
determinants remain largely elusive, with only a handful of identified
candidate genes. Genome wide association studies (GWAS) promised to
significantly enhance our understanding of genetic based determinants of common
complex diseases. To date, 83 single nucleotide polymorphisms (SNPs) for type 2
diabetes have been identified using GWAS. Standard statistical tests for single
and multi-locus analysis such as logistic regression, have demonstrated little
effect in understanding the genetic architecture of complex human diseases.
Logistic regression is modelled to capture linear interactions but neglects the
non-linear epistatic interactions present within genetic data. There is an
urgent need to detect epistatic interactions in complex diseases as this may
explain the remaining missing heritability in such diseases. In this paper, we
present a novel framework based on deep learning algorithms that deal with
non-linear epistatic interactions that exist in genome wide association data.
Logistic association analysis under an additive genetic model, adjusted for
genomic control inflation factor, is conducted to remove statistically
improbable SNPs to minimize computational overheads.
Semantic Matching Against a Corpus: New Applications and Methods
Authors: Lucy H. Lin, Scott Miles, Noah A. Smith
Date: 2018-08-28 00:00:00

We consider the case of a domain expert who wishes to explore the extent to
which a particular idea is expressed in a text collection. We propose the task
of semantically matching the idea, expressed as a natural language proposition,
against a corpus. We create two preliminary tasks derived from existing
datasets, and then introduce a more realistic one on disaster recovery designed
for emergency managers, whom we engaged in a user study. On the latter, we find
that a new model built from natural language entailment data produces
higher-quality matches than simple word-vector averaging, both on
expert-crafted queries and on ones produced by the subjects themselves. This
work provides a proof-of-concept for such applications of semantic matching and
illustrates key challenges.
Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget
Authors: Jaewoo Lee, Daniel Kifer
Date: 2018-08-28 00:00:00

Iterative algorithms, like gradient descent, are common tools for solving a
variety of problems, such as model fitting. For this reason, there is interest
in creating differentially private versions of them. However, their conversion
to differentially private algorithms is often naive. For instance, a fixed
number of iterations are chosen, the privacy budget is split evenly among them,
and at each iteration, parameters are updated with a noisy gradient. In this
paper, we show that gradient-based algorithms can be improved by a more careful
allocation of privacy budget per iteration. Intuitively, at the beginning of
the optimization, gradients are expected to be large, so that they do not need
to be measured as accurately. However, as the parameters approach their optimal
values, the gradients decrease and hence need to be measured more accurately.
We add a basic line-search capability that helps the algorithm decide when more
accurate gradient measurements are necessary. Our gradient descent algorithm
works with the recently introduced zCDP version of differential privacy. It
outperforms prior algorithms for model fitting and is competitive with the
state-of-the-art for $(\epsilon,\delta)$-differential privacy, a strictly
weaker definition than zCDP.
Adapting Word Embeddings to New Languages with Morphological and Phonological Subword Representations
Authors: Aditi Chaudhary, Chunting Zhou, Lori Levin, Graham Neubig, David R. Mortensen, Jaime G. Carbonell
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Avg, Machine Translation, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Transfer Learning, Translation, Word Embeddings

Much work in Natural Language Processing (NLP) has been for resource-rich
languages, making generalization to new, less-resourced languages challenging.
We present two approaches for improving generalization to low-resourced
languages by adapting continuous word representations using linguistically
motivated subword units: phonemes, morphemes and graphemes. Our method requires
neither parallel corpora nor bilingual dictionaries and provides a significant
gain in performance over previous methods relying on these resources. We
demonstrate the effectiveness of our approaches on Named Entity Recognition for
four languages, namely Uyghur, Turkish, Bengali and Hindi, of which Uyghur and
Bengali are low resource languages, and also perform experiments on Machine
Translation. Exploiting subwords with transfer learning gives us a boost of
+15.2 NER F1 for Uyghur and +9.7 F1 for Bengali. We also show improvements in
the monolingual setting where we achieve (avg.) +3 F1 and (avg.) +1.35 BLEU.
Learning to Attend On Essential Terms: An Enhanced Retriever-Reader Model for Open-domain Question Answering
Authors: Jianmo Ni, Chenguang Zhu, Weizhu Chen, Julian McAuley
Date: 2018-08-28 00:00:00
Proceeding: NAACL 2019 6
Tasks: AI2 Reasoning Challenge, ARC, Multiple-choice, Multiple Choice Question Answering (MCQA), Open-Domain Question Answering, Question Answering, Reading Comprehension

Open-domain question answering remains a challenging task as it requires models that are capable of understanding questions and answers, collecting useful information, and reasoning over evidence. Previous work typically formulates this task as a reading comprehension or entailment problem given evidence retrieved from search engines. However, existing techniques struggle to retrieve indirectly related evidence when no directly related evidence is provided, especially for complex questions where it is hard to parse precisely what the question asks. In this paper we propose a retriever-reader model that learns to attend on essential terms during the question answering process. We build (1) an essential term selector which first identifies the most important words in a question, then reformulates the query and searches for related evidence; and (2) an enhanced reader that distinguishes between essential terms and distracting words to predict the answer. We evaluate our model on multiple open-domain multiple-choice QA datasets, notably performing at the level of the state-of-the-art on the AI2 Reasoning Challenge (ARC) dataset.