pwc-archive/papers-with-abstracts huggingface dataset:
Commonsense mining as knowledge base completion? A study on the impact of novelty
Authors: Stanisław Jastrzębski, Dzmitry Bahdanau, Seyedarian Hosseini, Michael Noukhovitch, Yoshua Bengio, Jackie Chi Kit Cheung
Date: 2018-04-24 00:00:00
Proceeding: WS 2018 6
Tasks: Knowledge Base Completion

Commonsense knowledge bases such as ConceptNet represent knowledge in the
form of relational triples. Inspired by the recent work by Li et al., we
analyse if knowledge base completion models can be used to mine commonsense
knowledge from raw text. We propose novelty of predicted triples with respect
to the training set as an important factor in interpreting results. We
critically analyse the difficulty of mining novel commonsense knowledge, and
show that a simple baseline method outperforms the previous state of the art on
predicting more novel.
Bayesian Hypernetworks
Authors: David Krueger, Chin-wei Huang, Riashat Islam, Ryan Turner, Alexandre Lacoste, Aaron Courville
Date: 2017-10-13 00:00:00
Proceeding: ICLR 2018 1
Tasks: Active Learning, Anomaly Detection, Bayesian Inference, Variational Inference

We study Bayesian hypernetworks: a framework for approximate Bayesian
inference in neural networks. A Bayesian hypernetwork $\h$ is a neural network
which learns to transform a simple noise distribution, $p(\vec\epsilon) =
\N(\vec 0,\mat I)$, to a distribution $q(\pp) := q(h(\vec\epsilon))$ over the
parameters $\pp$ of another neural network (the "primary network")\@. We train
$q$ with variational inference, using an invertible $\h$ to enable efficient
estimation of the variational lower bound on the posterior $p(\pp | \D)$ via
sampling. In contrast to most methods for Bayesian deep learning, Bayesian
hypernets can represent a complex multimodal approximate posterior with
correlations between parameters, while enabling cheap iid sampling of~$q(\pp)$.
In practice, Bayesian hypernets can provide a better defense against
adversarial examples than dropout, and also exhibit competitive performance on
a suite of tasks which evaluate model uncertainty, including regularization,
active learning, and anomaly detection.
A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors
Authors: Germain Haessig, Ryad Benosman
Date: 2018-04-24 00:00:00
Tasks: BIG-bench Machine Learning, Clustering

This paper introduces an unsupervised compact architecture that can extract
features and classify the contents of dynamic scenes from the temporal output
of a neuromorphic asynchronous event-based camera. Event-based cameras are
clock-less sensors where each pixel asynchronously reports intensity changes
encoded in time at the microsecond precision. While this technology is gaining
more attention, there is still a lack of methodology and understanding of their
temporal properties. This paper introduces an unsupervised time-oriented
event-based machine learning algorithm building on the concept of hierarchy of
temporal descriptors called time surfaces. In this work we show that the use of
sparse coding allows for a very compact yet efficient time-based machine
learning that lowers both the computational cost and memory need. We show that
we can represent visual scene temporal dynamics with a finite set of elementary
time surfaces while providing similar recognition rates as an uncompressed
version by storing the most representative time surfaces using clustering
techniques. Experiments will illustrate the main optimizations and trade-offs
to consider when implementing the method for online continuous vs. offline
learning. We report results on the same previously published 36 class character
recognition task and a 4 class canonical dynamic card pip task, achieving 100%
accuracy on each.
On the effectiveness of task granularity for transfer learning
Authors: Farzaneh Mahdisoltani, Guillaume Berger, Waseem Gharbieh, David Fleet, Roland Memisevic
Date: 2018-04-24 00:00:00
Tasks: Classification, Diversity, General Classification, Transfer Learning, Video Classification

We describe a DNN for video classification and captioning, trained
end-to-end, with shared features, to solve tasks at different levels of
granularity, exploring the link between granularity in a source task and the
quality of learned features for transfer learning. For solving the new task
domain in transfer learning, we freeze the trained encoder and fine-tune a
neural net on the target domain. We train on the Something-Something dataset
with over 220, 000 videos, and multiple levels of target granularity, including
50 action groups, 174 fine-grained action categories and captions.
Classification and captioning with Something-Something are challenging because
of the subtle differences between actions, applied to thousands of different
object classes, and the diversity of captions penned by crowd actors. Our model
performs better than existing classification baselines for SomethingSomething,
with impressive fine-grained results. And it yields a strong baseline on the
new Something-Something captioning task. Experiments reveal that training with
more fine-grained tasks tends to produce better features for transfer learning.
Interactive Grounded Language Acquisition and Generalization in a 2D World
Authors: Haonan Yu, Haichao Zhang, Wei Xu
Date: 2018-01-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Language Acquisition, Question Answering, Sentence

We build a virtual agent for learning language in a 2D maze-like world. The
agent sees images of the surrounding environment, listens to a virtual teacher,
and takes actions to receive rewards. It interactively learns the teacher's
language from scratch based on two language use cases: sentence-directed
navigation and question answering. It learns simultaneously the visual
representations of the world, the language, and the action control. By
disentangling language grounding from other computational routines and sharing
a concept detection function between language grounding and prediction, the
agent reliably interpolates and extrapolates to interpret sentences that
contain new word combinations or new words missing from training sentences. The
new words are transferred from the answers of language prediction. Such a
language ability is trained and evaluated on a population of over 1.6 million
distinct sentences consisting of 119 object words, 8 color words, 9
spatial-relation words, and 50 grammatical words. The proposed model
significantly outperforms five comparison methods for interpreting zero-shot
sentences. In addition, we demonstrate human-interpretable intermediate outputs
of the model in the appendix.
On Learning Sparsely Used Dictionaries from Incomplete Samples
Authors: Thanh V. Nguyen, Akshay Soni, Chinmay Hegde
Date: 2018-04-24 00:00:00
Proceeding: ICML 2018 7
Tasks: Dictionary Learning

Most existing algorithms for dictionary learning assume that all entries of
the (high-dimensional) input data are fully observed. However, in several
practical applications (such as hyper-spectral imaging or blood glucose
monitoring), only an incomplete fraction of the data entries may be available.
For incomplete settings, no provably correct and polynomial-time algorithm has
been reported in the dictionary learning literature. In this paper, we provide
provable approaches for learning - from incomplete samples - a family of
dictionaries whose atoms have sufficiently "spread-out" mass. First, we propose
a descent-style iterative algorithm that linearly converges to the true
dictionary when provided a sufficiently coarse initial estimate. Second, we
propose an initialization algorithm that utilizes a small number of extra fully
observed samples to produce such a coarse initial estimate. Finally, we
theoretically analyze their performance and provide asymptotic statistical and
computational guarantees.
Automated Mouse Organ Segmentation: A Deep Learning Based Solution
Authors: Naveen Ashish, Mi-Youn Brusniak
Date: 2018-04-24 00:00:00
Tasks: Deep Learning, Organ Segmentation, Segmentation

The analysis of animal cross section images, such as cross sections of
laboratory mice, is critical in assessing the effect of experimental drugs such
as the biodistribution of candidate compounds in preclinical drug development
stage. Tissue distribution of radiolabeled candidate therapeutic compounds can
be quantified using techniques like Quantitative Whole-Body Autoradiography
(QWBA).QWBA relies, among other aspects, on the accurate segmentation or
identification of key organs of interest in the animal cross section image such
as the brain, spine, heart, liver and others. We present a deep learning based
organ segmentation solution to this problem, using which we can achieve
automated organ segmentation with high precision (dice coefficient in the
0.83-0.95 range depending on organ) for the key organs of interest.
MaskFusion: Real-Time Recognition, Tracking and Reconstruction of Multiple Moving Objects
Authors: Martin Rünz, Maud Buffier, Lourdes Agapito
Date: 2018-04-24 00:00:00
Tasks: Object, Object Recognition, Object SLAM, Segmentation, Semantic Segmentation, Semantic SLAM, Simultaneous Localization and Mapping

We present MaskFusion, a real-time, object-aware, semantic and dynamic RGB-D
SLAM system that goes beyond traditional systems which output a purely
geometric map of a static scene. MaskFusion recognizes, segments and assigns
semantic class labels to different objects in the scene, while tracking and
reconstructing them even when they move independently from the camera.
  As an RGB-D camera scans a cluttered scene, image-based instance-level
semantic segmentation creates semantic object masks that enable real-time
object recognition and the creation of an object-level representation for the
world map. Unlike previous recognition-based SLAM systems, MaskFusion does not
require known models of the objects it can recognize, and can deal with
multiple independent motions. MaskFusion takes full advantage of using
instance-level semantic segmentation to enable semantic labels to be fused into
an object-aware map, unlike recent semantics enabled SLAM systems that perform
voxel-level semantic segmentation. We show augmented-reality applications that
demonstrate the unique features of the map output by MaskFusion:
instance-aware, semantic and dynamic.
A disciplined approach to neural network hyper-parameters: Part 1 -- learning rate, batch size, momentum, and weight decay
Authors: Leslie N. Smith
Date: 2018-03-26 00:00:00

Although deep learning has produced dazzling successes for applications of
image, speech, and video processing in the past few years, most trainings are
with suboptimal hyper-parameters, requiring unnecessarily long training times.
Setting the hyper-parameters remains a black art that requires years of
experience to acquire. This report proposes several efficient ways to set the
hyper-parameters that significantly reduce training time and improves
performance. Specifically, this report shows how to examine the training
validation/test loss function for subtle clues of underfitting and overfitting
and suggests guidelines for moving toward the optimal balance point. Then it
discusses how to increase/decrease the learning rate/momentum to speed up
training. Our experiments show that it is crucial to balance every manner of
regularization for each dataset and architecture. Weight decay is used as a
sample regularizer to show how its optimal value is tightly coupled with the
learning rates and momentums. Files to help replicate the results reported here
are available.
No Metrics Are Perfect: Adversarial Reward Learning for Visual Storytelling
Authors: Xin Wang, Wenhu Chen, Yuan-Fang Wang, William Yang Wang
Date: 2018-04-24 00:00:00
Proceeding: ACL 2018 7
Tasks: Image Captioning, Reinforcement Learning, Visual Storytelling

Though impressive results have been achieved in visual captioning, the task
of generating abstract stories from photo streams is still a little-tapped
problem. Different from captions, stories have more expressive language styles
and contain many imaginary concepts that do not appear in the images. Thus it
poses challenges to behavioral cloning algorithms. Furthermore, due to the
limitations of automatic metrics on evaluating story quality, reinforcement
learning methods with hand-crafted rewards also face difficulties in gaining an
overall performance boost. Therefore, we propose an Adversarial REward Learning
(AREL) framework to learn an implicit reward function from human
demonstrations, and then optimize policy search with the learned reward
function. Though automatic eval- uation indicates slight performance boost over
state-of-the-art (SOTA) methods in cloning expert behaviors, human evaluation
shows that our approach achieves significant improvement in generating more
human-like stories than SOTA systems.
Factoring Shape, Pose, and Layout from the 2D Image of a 3D Scene
Authors: Shubham Tulsiani, Saurabh Gupta, David Fouhey, Alexei A. Efros, Jitendra Malik
Date: 2017-12-05 00:00:00
Proceeding: CVPR 2018 6

The goal of this paper is to take a single 2D image of a scene and recover
the 3D structure in terms of a small set of factors: a layout representing the
enclosing surfaces as well as a set of objects represented in terms of shape
and pose. We propose a convolutional neural network-based approach to predict
this representation and benchmark it on a large dataset of indoor scenes. Our
experiments evaluate a number of practical design questions, demonstrate that
we can infer this representation, and quantitatively and qualitatively
demonstrate its merits compared to alternate representations.
Multi-view Consistency as Supervisory Signal for Learning Shape and Pose Prediction
Authors: Shubham Tulsiani, Alexei A. Efros, Jitendra Malik
Date: 2018-01-11 00:00:00
Proceeding: CVPR 2018 6
Tasks: Pose Prediction

We present a framework for learning single-view shape and pose prediction
without using direct supervision for either. Our approach allows leveraging
multi-view observations from unknown poses as supervisory signal during
training. Our proposed training setup enforces geometric consistency between
the independently predicted shape and pose from two views of the same instance.
We consequently learn to predict shape in an emergent canonical (view-agnostic)
frame along with a corresponding pose predictor. We show empirical and
qualitative results using the ShapeNet dataset and observe encouragingly
competitive performance to previous techniques which rely on stronger forms of
supervision. We also demonstrate the applicability of our framework in a
realistic setting which is beyond the scope of existing techniques: using a
training dataset comprised of online product images where the underlying shape
and pose are unknown.
DOOM Level Generation using Generative Adversarial Networks
Authors: Edoardo Giacomello, Pier Luca Lanzi, Daniele Loiacono
Date: 2018-04-24 00:00:00
Tasks: Position

We applied Generative Adversarial Networks (GANs) to learn a model of DOOM
levels from human-designed content. Initially, we analysed the levels and
extracted several topological features. Then, for each level, we extracted a
set of images identifying the occupied area, the height map, the walls, and the
position of game objects. We trained two GANs: one using plain level images,
one using both the images and some of the features extracted during the
preliminary analysis. We used the two networks to generate new levels and
compared the results to assess whether the network trained using also the
topological features could generate levels more similar to human-designed ones.
Our results show that GANs can capture intrinsic structure of DOOM levels and
appears to be a promising approach to level generation in first person shooter
games.
An Integrated Framework for AI Assisted Level Design in 2D Platformers
Authors: Antonio Umberto Aramini, Pier Luca Lanzi, Daniele Loiacono
Date: 2018-04-24 00:00:00

The design of video game levels is a complex and critical task. Levels need
to elicit fun and challenge while avoiding frustration at all costs. In this
paper, we present a framework to assist designers in the creation of levels for
2D platformers. Our framework provides designers with a toolbox (i) to create
2D platformer levels, (ii) to estimate the difficulty and probability of
success of single jump actions (the main mechanics of platformer games), and
(iii) a set of metrics to evaluate the difficulty and probability of completion
of entire levels. At the end, we present the results of a set of experiments we
carried out with human players to validate the metrics included in our
framework.
Automated Detection of Adverse Drug Reactions in the Biomedical Literature Using Convolutional Neural Networks and Biomedical Word Embeddings
Authors: Diego Saldana Miranda
Date: 2018-04-24 00:00:00
Tasks: Pharmacovigilance, Word Embeddings

Monitoring the biomedical literature for cases of Adverse Drug Reactions
(ADRs) is a critically important and time consuming task in pharmacovigilance.
The development of computer assisted approaches to aid this process in
different forms has been the subject of many recent works. One particular area
that has shown promise is the use of Deep Neural Networks, in particular,
Convolutional Neural Networks (CNNs), for the detection of ADR relevant
sentences. Using token-level convolutions and general purpose word embeddings,
this architecture has shown good performance relative to more traditional
models as well as Long Short Term Memory (LSTM) models. In this work, we
evaluate and compare two different CNN architectures using the ADE corpus. In
addition, we show that by de-duplicating the ADR relevant sentences, we can
greatly reduce overoptimism in the classification results. Finally, we evaluate
the use of word embeddings specifically developed for biomedical text and show
that they lead to a better performance in this task.
Fixed-sized representation learning from Offline Handwritten Signatures of different sizes
Authors: Luiz G. Hafemann, Robert Sabourin, Luiz S. Oliveira
Date: 2018-04-02 00:00:00
Tasks: Representation Learning

Methods for learning feature representations for Offline Handwritten
Signature Verification have been successfully proposed in recent literature,
using Deep Convolutional Neural Networks to learn representations from
signature pixels. Such methods reported large performance improvements compared
to handcrafted feature extractors. However, they also introduced an important
constraint: the inputs to the neural networks must have a fixed size, while
signatures vary significantly in size between different users. In this paper we
propose addressing this issue by learning a fixed-sized representation from
variable-sized signatures by modifying the network architecture, using Spatial
Pyramid Pooling. We also investigate the impact of the resolution of the images
used for training, and the impact of adapting (fine-tuning) the representations
to new operating conditions (different acquisition protocols, such as writing
instruments and scan resolution). On the GPDS dataset, we achieve results
comparable with the state-of-the-art, while removing the constraint of having a
maximum size for the signatures to be processed. We also show that using higher
resolutions (300 or 600dpi) can improve performance when skilled forgeries from
a subset of users are available for feature learning, but lower resolutions
(around 100dpi) can be used if only genuine signatures are used. Lastly, we
show that fine-tuning can improve performance when the operating conditions
change.
Universal Dependency Parsing for Hindi-English Code-switching
Authors: Irshad Ahmad Bhat, Riyaz Ahmad Bhat, Manish Shrivastava, Dipti Misra Sharma
Date: 2018-04-16 00:00:00
Proceeding: NAACL 2018 6
Tasks: Dependency Parsing, Language Identification, TAG, Transliteration

Code-switching is a phenomenon of mixing grammatical structures of two or
more languages under varied social constraints. The code-switching data differ
so radically from the benchmark corpora used in NLP community that the
application of standard technologies to these data degrades their performance
sharply. Unlike standard corpora, these data often need to go through
additional processes such as language identification, normalization and/or
back-transliteration for their efficient processing. In this paper, we
investigate these indispensable processes and other problems associated with
syntactic parsing of code-switching data and propose methods to mitigate their
effects. In particular, we study dependency parsing of code-switching data of
Hindi and English multilingual speakers from Twitter. We present a treebank of
Hindi-English code-switching tweets under Universal Dependencies scheme and
propose a neural stacking model for parsing that efficiently leverages
part-of-speech tag and syntactic tree annotations in the code-switching
treebank and the preexisting Hindi and English treebanks. We also present
normalization and back-transliteration models with a decoding process tailored
for code-switching data. Results show that our neural stacking parser is 1.5%
LAS points better than the augmented parsing model and our decoding process
improves results by 3.8% LAS points over the first-best normalization and/or
back-transliteration.
Improving Native Ads CTR Prediction by Large Scale Event Embedding and Recurrent Networks
Authors: Mehul Parsana, Krishna Poola, Yajun Wang, Zhiguang Wang
Date: 2018-04-24 00:00:00
Tasks: Click-Through Rate Prediction, Prediction

Click through rate (CTR) prediction is very important for Native advertisement but also hard as there is no direct query intent. In this paper we propose a large-scale event embedding scheme to encode the each user browsing event by training a Siamese network with weak supervision on the users' consecutive events. The CTR prediction problem is modeled as a supervised recurrent neural network, which naturally model the user history as a sequence of events. Our proposed recurrent models utilizing pretrained event embedding vectors and an attention layer to model the user history. Our experiments demonstrate that our model significantly outperforms the baseline and some variants.
A Report on the Complex Word Identification Shared Task 2018
Authors: Seid Muhie Yimam, Chris Biemann, Shervin Malmasi, Gustavo H. Paetzold, Lucia Specia, Sanja Štajner, Anaïs Tack, Marcos Zampieri
Date: 2018-04-24 00:00:00
Proceeding: WS 2018 6
Tasks: Binary Classification, Classification, Complex Word Identification, General Classification

We report the findings of the second Complex Word Identification (CWI) shared
task organized as part of the BEA workshop co-located with NAACL-HLT'2018. The
second CWI shared task featured multilingual and multi-genre datasets divided
into four tracks: English monolingual, German monolingual, Spanish monolingual,
and a multilingual track with a French test set, and two tasks: binary
classification and probabilistic classification. A total of 12 teams submitted
their results in different task/track combinations and 11 of them wrote system
description papers that are referred to in this report and appear in the BEA
workshop proceedings.