pwc-archive/papers-with-abstracts huggingface dataset:
Analyzing and Exploiting NARX Recurrent Neural Networks for Long-Term Dependencies
Authors: Robert DiPietro, Christian Rupprecht, Nassir Navab, Gregory D. Hager
Date: 2017-02-24 00:00:00
Proceeding: ICLR 2018 1
Tasks: Activity Recognition, Machine Translation, Translation

Recurrent neural networks (RNNs) have achieved state-of-the-art performance
on many diverse tasks, from machine translation to surgical activity
recognition, yet training RNNs to capture long-term dependencies remains
difficult. To date, the vast majority of successful RNN architectures alleviate
this problem using nearly-additive connections between states, as introduced by
long short-term memory (LSTM). We take an orthogonal approach and introduce
MIST RNNs, a NARX RNN architecture that allows direct connections from the very
distant past. We show that MIST RNNs 1) exhibit superior vanishing-gradient
properties in comparison to LSTM and previously-proposed NARX RNNs; 2) are far
more efficient than previously-proposed NARX RNN architectures, requiring even
fewer computations than LSTM; and 3) improve performance substantially over
LSTM and Clockwork RNNs on tasks requiring very long-term dependencies.
A Mixed Hierarchical Attention based Encoder-Decoder Approach for Standard Table Summarization
Authors: Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Preksha Nema, Mitesh M. Khapra, Shreyas Shetty
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6
Tasks: Data Summarization, Decoder

Structured data summarization involves generation of natural language
summaries from structured input data. In this work, we consider summarizing
structured data occurring in the form of tables as they are prevalent across a
wide variety of domains. We formulate the standard table summarization problem,
which deals with tables conforming to a single predefined schema. To this end,
we propose a mixed hierarchical attention based encoder-decoder model which is
able to leverage the structure in addition to the content of the tables. Our
experiments on the publicly available WEATHERGOV dataset show around 18 BLEU (~
30%) improvement over the current state-of-the-art.
Generating Descriptions from Structured Data Using a Bifocal Attention Mechanism and Gated Orthogonalization
Authors: Preksha Nema, Shreyas Shetty, Parag Jain, Anirban Laha, Karthik Sankaranarayanan, Mitesh M. Khapra
Date: 2018-04-20 00:00:00
Proceeding: NAACL 2018 6

In this work, we focus on the task of generating natural language
descriptions from a structured table of facts containing fields (such as
nationality, occupation, etc) and values (such as Indian, actor, director,
etc). One simple choice is to treat the table as a sequence of fields and
values and then use a standard seq2seq model for this task. However, such a
model is too generic and does not exploit task-specific characteristics. For
example, while generating descriptions from a table, a human would attend to
information at two levels: (i) the fields (macro level) and (ii) the values
within the field (micro level). Further, a human would continue attending to a
field for a few timesteps till all the information from that field has been
rendered and then never return back to this field (because there is nothing
left to say about it). To capture this behavior we use (i) a fused bifocal
attention mechanism which exploits and combines this micro and macro level
information and (ii) a gated orthogonalization mechanism which tries to ensure
that a field is remembered for a few time steps and then forgotten. We
experiment with a recently released dataset which contains fact tables about
people and their corresponding one line biographical descriptions in English.
In addition, we also introduce two similar datasets for French and German. Our
experiments show that the proposed model gives 21% relative improvement over a
recently proposed state of the art method and 10% relative improvement over
basic seq2seq models. The code and the datasets developed as a part of this
work are publicly available.
Pathologies of Neural Models Make Interpretations Difficult
Authors: Shi Feng, Eric Wallace, Alvin Grissom II, Mohit Iyyer, Pedro Rodriguez, Jordan Boyd-Graber
Date: 2018-04-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence

One way to interpret neural model predictions is to highlight the most
important input features---for example, a heatmap visualization over the words
in an input sentence. In existing interpretation methods for NLP, a word's
importance is determined by either input perturbation---measuring the decrease
in model confidence when that word is removed---or by the gradient with respect
to that word. To understand the limitations of these methods, we use input
reduction, which iteratively removes the least important word from the input.
This exposes pathological behaviors of neural models: the remaining words
appear nonsensical to humans and are not the ones determined as important by
interpretation methods. As we confirm with human experiments, the reduced
examples lack information to support the prediction of any label, but models
still make the same predictions with high confidence. To explain these
counterintuitive results, we draw connections to adversarial examples and
confidence calibration: pathological behaviors reveal difficulties in
interpreting neural models trained with maximum likelihood. To mitigate their
deficiencies, we fine-tune the models by encouraging high entropy outputs on
reduced examples. Fine-tuned models become more interpretable under input
reduction without accuracy loss on regular examples.
QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites
Authors: Jiankai Sun, Sobhan Moosavi, Rajiv Ramnath, Srinivasan Parthasarathy
Date: 2018-03-31 00:00:00
Tasks: Community Question Answering, Question Answering

In this paper, we present a framework for Question Difficulty and Expertise
Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo!
Answers and Stack Overflow, which tackles a fundamental challenge in
crowdsourcing: how to appropriately route and assign questions to users with
the suitable expertise. This problem domain has been the subject of much
research and includes both language-agnostic as well as language conscious
solutions. We bring to bear a key language-agnostic insight: that users gain
expertise and therefore tend to ask as well as answer more difficult questions
over time. We use this insight within the popular competition (directed) graph
model to estimate question difficulty and user expertise by identifying key
hierarchical structure within said model. An important and novel contribution
here is the application of "social agony" to this problem domain. Difficulty
levels of newly posted questions (the cold-start problem) are estimated by
using our QDEE framework and additional textual features. We also propose a
model to route newly posted questions to appropriate users based on the
difficulty level of the question and the expertise of the user. Extensive
experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data
demonstrate the improved efficacy of our approach over contemporary
state-of-the-art models. The QDEE framework also allows us to characterize user
expertise in novel ways by identifying interesting patterns and roles played by
different users in such CQAs.
The Statistical Model for Ticker, an Adaptive Single-Switch Text-Entry Method for Visually Impaired Users
Authors: Emli-Mari Nel, Per Ola Kristensson, David J. C. MacKay
Date: 2018-04-20 00:00:00

This paper presents the statistical model for Ticker [1], a novel
probabilistic stereophonic single-switch text entry method for
visually-impaired users with motor disabilities who rely on single-switch
scanning systems to communicate. All terminology and notation are defined in
[1].
Sampling the Riemann-Theta Boltzmann Machine
Authors: Stefano Carrazza, Daniel Krefl
Date: 2018-04-20 00:00:00

We show that the visible sector probability density function of the Riemann-Theta Boltzmann machine corresponds to a gaussian mixture model consisting of an infinite number of component multi-variate gaussians. The weights of the mixture are given by a discrete multi-variate gaussian over the hidden state space. This allows us to sample the visible sector density function in a straight-forward manner. Furthermore, we show that the visible sector probability density function possesses an affine transform property, similar to the multi-variate gaussian density.
Phrase-Based & Neural Unsupervised Machine Translation
Authors: Guillaume Lample, Myle Ott, Alexis Conneau, Ludovic Denoyer, Marc'Aurelio Ranzato
Date: 2018-04-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, NMT, Sentence, Translation, Unsupervised Machine Translation

Machine translation systems achieve near human-level performance on some
languages, yet their effectiveness strongly relies on the availability of large
amounts of parallel sentences, which hinders their applicability to the
majority of language pairs. This work investigates how to learn to translate
when having access to only large monolingual corpora in each language. We
propose two model variants, a neural and a phrase-based model. Both versions
leverage a careful initialization of the parameters, the denoising effect of
language models and automatic generation of parallel data by iterative
back-translation. These models are significantly better than methods from the
literature, while being simpler and having fewer hyper-parameters. On the
widely used WMT'14 English-French and WMT'16 German-English benchmarks, our
models respectively obtain 28.1 and 25.2 BLEU points without using a single
parallel sentence, outperforming the state of the art by more than 11 BLEU
points. On low-resource languages like English-Urdu and English-Romanian, our
methods achieve even better results than semi-supervised and supervised
approaches leveraging the paucity of available bitexts. Our code for NMT and
PBSMT is publicly available.
Learning Semantic Textual Similarity from Conversations
Authors: Yinfei Yang, Steve Yuan, Daniel Cer, Sheng-yi Kong, Noah Constant, Petr Pilar, Heming Ge, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Date: 2018-04-20 00:00:00
Proceeding: WS 2018 7
Tasks: Community Question Answering, Natural Language Inference, Question Answering, Question Similarity, Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embeddings, STS, STS Benchmark

We present a novel approach to learn representations for sentence-level
semantic similarity using conversational data. Our method trains an
unsupervised model to predict conversational input-response pairs. The
resulting sentence embeddings perform well on the semantic textual similarity
(STS) benchmark and SemEval 2017's Community Question Answering (CQA) question
similarity subtask. Performance is further improved by introducing multitask
training combining the conversational input-response prediction task and a
natural language inference task. Extensive experiments show the proposed model
achieves the best performance among all neural models on the STS benchmark and
is competitive with the state-of-the-art feature engineered and mixed systems
in both tasks.
A New Approach to an Old Problem: The Reconstruction of a Go Game through a Series of Photographs
Authors: Mario Corsolini, Andrea Carta
Date: 2015-08-13 00:00:00

Given a series of photographs taken during a Go game, we describe the
techniques we successfully employ for pinpointing the grid lines of the Go
board and for tracking their small movements between consecutive photographs;
then we discuss how to approximate the location and orientation of the
observer's point of view, in order to compensate for projection effects.
Finally we describe the different criteria that jointly form the algorithm for
stones' detection, thus enabling us to automatically reconstruct the whole move
sequence.
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Authors: Armand Joulin, Piotr Bojanowski, Tomas Mikolov, Herve Jegou, Edouard Grave
Date: 2018-04-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: regression, Retrieval, Translation, Word Translation

Continuous word representations learned separately on distinct languages can
be aligned so that their words become comparable in a common space. Existing
works typically solve a least-square regression problem to learn a rotation
aligning a small bilingual lexicon, and use a retrieval criterion for
inference. In this paper, we propose an unified formulation that directly
optimizes a retrieval criterion in an end-to-end fashion. Our experiments on
standard benchmarks show that our approach outperforms the state of the art on
word translation, with the biggest improvements observed for distant language
pairs such as English-Chinese.
Synthesizing Images of Humans in Unseen Poses
Authors: Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Fredo Durand, John Guttag
Date: 2018-04-20 00:00:00
Proceeding: CVPR 2018 6
Tasks: Image Generation

We address the computational problem of novel human pose synthesis. Given an
image of a person and a desired pose, we produce a depiction of that person in
that pose, retaining the appearance of both the person and background. We
present a modular generative neural network that synthesizes unseen poses using
training pairs of images and poses taken from human action videos. Our network
separates a scene into different body part and background layers, moves body
parts to new locations and refines their appearances, and composites the new
foreground with a hole-filled background. These subtasks, implemented with
separate modules, are trained jointly using only a single target image as a
supervised label. We use an adversarial discriminator to force our network to
synthesize realistic details conditioned on pose. We demonstrate image
synthesis results on three action classes: golf, yoga/workouts and tennis, and
show that our method produces accurate results within action classes as well as
across action classes. Given a sequence of desired poses, we also produce
coherent videos of actions.
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
Authors: Guang-Neng Hu, Yu Zhang, Qiang Yang
Date: 2018-04-18 00:00:00
Tasks: Recommendation Systems, Transfer Learning

The cross-domain recommendation technique is an effective way of alleviating
the data sparse issue in recommender systems by leveraging the knowledge from
relevant domains. Transfer learning is a class of algorithms underlying these
techniques. In this paper, we propose a novel transfer learning approach for
cross-domain recommendation by using neural networks as the base model. In
contrast to the matrix factorization based cross-domain techniques, our method
is deep transfer learning, which can learn complex user-item interaction
relationships. We assume that hidden layers in two base networks are connected
by cross mappings, leading to the collaborative cross networks (CoNet). CoNet
enables dual knowledge transfer across domains by introducing cross connections
from one base network to another and vice versa. CoNet is achieved in
multi-layer feedforward networks by adding dual connections and joint loss
functions, which can be trained efficiently by back-propagation. The proposed
model is thoroughly evaluated on two large real-world datasets. It outperforms
baselines by relative improvements of 7.84\% in NDCG. We demonstrate the
necessity of adaptively selecting representations to transfer. Our model can
reduce tens of thousands training examples comparing with non-transfer methods
and still has the competitive performance with them.
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension
Authors: Minjoon Seo, Tom Kwiatkowski, Ankur P. Parikh, Ali Farhadi, Hannaneh Hajishirzi
Date: 2018-04-20 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Reading Comprehension, Retrieval

We formalize a new modular variant of current question answering tasks by
enforcing complete independence of the document encoder from the question
encoder. This formulation addresses a key challenge in machine comprehension by
requiring a standalone representation of the document discourse. It
additionally leads to a significant scalability advantage since the encoding of
the answer candidate phrases in the document can be pre-computed and indexed
offline for efficient retrieval. We experiment with baseline models for the new
task, which achieve a reasonable accuracy but significantly underperform
unconstrained QA models. We invite the QA research community to engage in
Phrase-Indexed Question Answering (PIQA, pika) for closing the gap. The
leaderboard is at: nlp.cs.washington.edu/piqa
Image Inpainting for Irregular Holes Using Partial Convolutions
Authors: Guilin Liu, Fitsum A. Reda, Kevin J. Shih, Ting-Chun Wang, Andrew Tao, Bryan Catanzaro
Date: 2018-04-20 00:00:00
Proceeding: ECCV 2018 9
Tasks: Image Inpainting, valid

Existing deep learning based image inpainting methods use a standard
convolutional network over the corrupted image, using convolutional filter
responses conditioned on both valid pixels as well as the substitute values in
the masked holes (typically the mean value). This often leads to artifacts such
as color discrepancy and blurriness. Post-processing is usually used to reduce
such artifacts, but are expensive and may fail. We propose the use of partial
convolutions, where the convolution is masked and renormalized to be
conditioned on only valid pixels. We further include a mechanism to
automatically generate an updated mask for the next layer as part of the
forward pass. Our model outperforms other methods for irregular masks. We show
qualitative and quantitative comparisons with other methods to validate our
approach.
A Study on Overfitting in Deep Reinforcement Learning
Authors: Chiyuan Zhang, Oriol Vinyals, Remi Munos, Samy Bengio
Date: 2018-04-18 00:00:00
Tasks: Deep Reinforcement Learning, Inductive Bias, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent years have witnessed significant progresses in deep Reinforcement
Learning (RL). Empowered with large scale neural networks, carefully designed
architectures, novel training algorithms and massively parallel computing
devices, researchers are able to attack many challenging RL problems. However,
in machine learning, more training power comes with a potential risk of more
overfitting. As deep RL techniques are being applied to critical problems such
as healthcare and finance, it is important to understand the generalization
behaviors of the trained agents. In this paper, we conduct a systematic study
of standard RL agents and find that they could overfit in various ways.
Moreover, overfitting could happen "robustly": commonly used techniques in RL
that add stochasticity do not necessarily prevent or detect overfitting. In
particular, the same agents and learning algorithms could have drastically
different test performance, even when all of them achieve optimal rewards
during training. The observations call for more principled and careful
evaluation protocols in RL. We conclude with a general discussion on
overfitting in RL and a study of the generalization behaviors from the
perspective of inductive bias.
Fast inference of deep neural networks in FPGAs for particle physics
Authors: Javier Duarte, Song Han, Philip Harris, Sergo Jindariani, Edward Kreinar, Benjamin Kreis, Jennifer Ngadiuba, Maurizio Pierini, Ryan Rivera, Nhan Tran, Zhenbin Wu
Date: 2018-04-16 00:00:00
Tasks: BIG-bench Machine Learning, High-Level Synthesis

Recent results at the Large Hadron Collider (LHC) have pointed to enhanced
physics capabilities through the improvement of the real-time event processing
techniques. Machine learning methods are ubiquitous and have proven to be very
powerful in LHC physics, and particle physics as a whole. However, exploration
of the use of such techniques in low-latency, low-power FPGA hardware has only
just begun. FPGA-based trigger and data acquisition (DAQ) systems have
extremely low, sub-microsecond latency requirements that are unique to particle
physics. We present a case study for neural network inference in FPGAs focusing
on a classifier for jet substructure which would enable, among many other
physics scenarios, searches for new dark sector particles and novel
measurements of the Higgs boson. While we focus on a specific example, the
lessons are far-reaching. We develop a package based on High-Level Synthesis
(HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS
increases accessibility across a broad user community and allows for a drastic
decrease in firmware development time. We map out FPGA resource usage and
latency versus neural network hyperparameters to identify the problems in
particle physics that would benefit from performing neural network inference
with FPGAs. For our example jet substructure model, we fit well within the
available resources of modern FPGAs with a latency on the scale of 100 ns.
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
Authors: John Wieting, Kevin Gimpel
Date: 2017-11-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Natural Language Understanding, Paraphrase Generation, Semantic Textual Similarity, Sentence, Sentence Embeddings, Translation

We describe PARANMT-50M, a dataset of more than 50 million English-English
sentential paraphrase pairs. We generated the pairs automatically by using
neural machine translation to translate the non-English side of a large
parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M
can be a valuable resource for paraphrase generation and can provide a rich
source of semantic knowledge to improve downstream natural language
understanding tasks. To show its utility, we use ParaNMT-50M to train
paraphrastic sentence embeddings that outperform all supervised systems on
every SemEval semantic textual similarity competition, in addition to showing
how it can be used for paraphrase generation.
Unsupervised Learning for Color Constancy
Authors: Nikola Banić, Karlo Koščević, Sven Lončarić
Date: 2017-12-01 00:00:00
Tasks: Color Constancy

Most digital camera pipelines use color constancy methods to reduce the
influence of illumination and camera sensor on the colors of scene objects. The
highest accuracy of color correction is obtained with learning-based color
constancy methods, but they require a significant amount of calibrated training
images with known ground-truth illumination. Such calibration is time
consuming, preferably done for each sensor individually, and therefore a major
bottleneck in acquiring high color constancy accuracy. Statistics-based methods
do not require calibrated training images, but they are less accurate. In this
paper an unsupervised learning-based method is proposed that learns its
parameter values after approximating the unknown ground-truth illumination of
the training images, thus avoiding calibration. In terms of accuracy the
proposed method outperforms all statistics-based and many learning-based
methods. An extension of the method is also proposed, which learns the needed
parameters from non-calibrated images taken with one sensor and which can then
be successfully applied to images taken with another sensor. This effectively
enables inter-camera unsupervised learning for color constancy. Additionally, a
new high quality color constancy benchmark dataset with 1707 calibrated images
is created, used for testing, and made publicly available. The results are
presented and discussed. The source code and the dataset are available at
http://www.fer.unizg.hr/ipg/resources/color_constancy/.