pwc-archive/papers-with-abstracts huggingface dataset:
Regularizing Activation Distribution for Training Binarized Deep Networks
Authors: Ruizhou Ding, Ting-Wu Chin, Zeye Liu, Diana Marculescu
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6

Binarized Neural Networks (BNNs) can significantly reduce the inference
latency and energy consumption in resource-constrained devices due to their
pure-logical computation and fewer memory accesses. However, training BNNs is
difficult since the activation flow encounters degeneration, saturation, and
gradient mismatch problems. Prior work alleviates these issues by increasing
activation bits and adding floating-point scaling factors, thereby sacrificing
BNN's energy efficiency. In this paper, we propose to use distribution loss to
explicitly regularize the activation flow, and develop a framework to
systematically formulate the loss. Our experiments show that the distribution
loss can consistently improve the accuracy of BNNs without losing their energy
benefits. Moreover, equipped with the proposed regularization, BNN training is
shown to be robust to the selection of hyper-parameters including optimizer and
learning rate.
Neural Networks for Modeling Source Code Edits
Authors: Rui Zhao, David Bieber, Kevin Swersky, Daniel Tarlow
Date: 2019-04-04 00:00:00

Programming languages are emerging as a challenging and interesting domain
for machine learning. A core task, which has received significant attention in
recent years, is building generative models of source code. However, to our
knowledge, previous generative models have always been framed in terms of
generating static snapshots of code. In this work, we instead treat source code
as a dynamic object and tackle the problem of modeling the edits that software
developers make to source code files. This requires extracting intent from
previous edits and leveraging it to generate subsequent edits. We develop
several neural networks and use synthetic data to test their ability to learn
challenging edit patterns that require strong generalization. We then collect
and train our models on a large-scale dataset of Google source code, consisting
of millions of fine-grained edits from thousands of Python developers. From the
modeling perspective, our main conclusion is that a new composition of
attentional and pointer network components provides the best overall
performance and scalability. From the application perspective, our results
provide preliminary evidence of the feasibility of developing tools that learn
to predict future edits.
Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
Authors: Xiaochuang Han, Jacob Eisenstein
Date: 2019-04-04 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Domain Adaptation, Language Modeling, Language Modelling, Masked Language Modeling, Part-Of-Speech Tagging, Unsupervised Domain Adaptation, Word Embeddings

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled text. However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on text from the target domain. We test this approach on sequence labeling in two challenging domains: Early Modern English and Twitter. Both domains differ substantially from existing pretraining corpora, and domain-adaptive fine-tuning yields substantial improvements over strong BERT baselines, with particularly impressive results on out-of-vocabulary words. We conclude that domain-adaptive fine-tuning offers a simple and effective approach for the unsupervised adaptation of sequence labeling to difficult new domains.
Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning
Authors: Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler
Date: 2019-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Computed Tomography (CT), Image Reconstruction

The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as sparsity or low-rank. A fourth type of methods replaces mathematically designed models of signals and systems with data-driven or adaptive models inspired by the field of machine learning. This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.
Topic Spotting using Hierarchical Networks with Self Attention
Authors: Pooja Chitkara, Ashutosh Modi, Pravalika Avvaru, Sepehr Janghorbani, Mubbasir Kapadia
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: text-classification, Text Classification

Success of deep learning techniques have renewed the interest in development
of dialogue systems. However, current systems struggle to have consistent long
term conversations with the users and fail to build rapport. Topic spotting,
the task of automatically inferring the topic of a conversation, has been shown
to be helpful in making a dialog system more engaging and efficient. We propose
a hierarchical model with self attention for topic spotting. Experiments on the
Switchboard corpus show the superior performance of our model over previously
proposed techniques for topic spotting and deep models for text classification.
Additionally, in contrast to offline processing of dialog, we also analyze the
performance of our model in a more realistic setting i.e. in an online setting
where the topic is identified in real time as the dialog progresses. Results
show that our model is able to generalize even with limited information in the
online setting.
Video Classification with Channel-Separated Convolutional Networks
Authors: Du Tran, Heng Wang, Lorenzo Torresani, Matt Feiszli
Date: 2019-04-04 00:00:00
Proceeding: ICCV 2019 10
Tasks: Action Classification, Action Recognition, General Classification, image-classification, Image Classification, Video Classification

Group convolution has been shown to offer great computational savings in various 2D convolutional architectures for image classification. It is natural to ask: 1) if group convolution can help to alleviate the high computational cost of video classification networks; 2) what factors matter the most in 3D group convolutional networks; and 3) what are good computation/accuracy trade-offs with 3D group convolutional networks. This paper studies the effects of different design choices in 3D group convolutional networks for video classification. We empirically demonstrate that the amount of channel interactions plays an important role in the accuracy of 3D group convolutional networks. Our experiments suggest two main findings. First, it is a good practice to factorize 3D convolutions by separating channel interactions and spatiotemporal interactions as this leads to improved accuracy and lower computational cost. Second, 3D channel-separated convolutions provide a form of regularization, yielding lower training accuracy but higher test accuracy compared to 3D convolutions. These two empirical findings lead us to design an architecture -- Channel-Separated Convolutional Network (CSN) -- which is simple, efficient, yet accurate. On Sports1M, Kinetics, and Something-Something, our CSNs are comparable with or better than the state-of-the-art while being 2-3 times more efficient.
Fluxonic Processing of Photonic Synapse Events
Authors: Jeffrey M. Shainline
Date: 2019-04-04 00:00:00

Much of the information processing performed by a neuron occurs in the
dendritic tree. For neural systems using light for communication, it is
advantageous to convert signals to the electronic domain at synaptic terminals
so dendritic computation can be performed with electrical circuits. Here we
present circuits based on Josephson junctions and mutual inductors that act as
dendrites, processing signals from synapses receiving single-photon
communication events with superconducting detectors. We show simulations of
circuits performing basic temporal filtering, logical operations, and nonlinear
transfer functions. We further show how the synaptic signal from a
single-photon can fan out locally in the electronic domain to enable the
dendrites of the receiving neuron to process a photonic synapse event or pulse
train in multiple different ways simultaneously. Such a technique makes
efficient use of photons, energy, space, and information.
Assessment of Faster R-CNN in Man-Machine collaborative search
Authors: Arturo Deza, Amit Surana, Miguel P. Eckstein
Date: 2019-04-04 00:00:00
Proceeding: CVPR 2019 6
Tasks: Deep Learning, Experimental Design, Sensitivity

With the advent of modern expert systems driven by deep learning that
supplement human experts (e.g. radiologists, dermatologists, surveillance
scanners), we analyze how and when do such expert systems enhance human
performance in a fine-grained small target visual search task. We set up a 2
session factorial experimental design in which humans visually search for a
target with and without a Deep Learning (DL) expert system. We evaluate human
changes of target detection performance and eye-movements in the presence of
the DL system. We find that performance improvements with the DL system
(computed via a Faster R-CNN with a VGG16) interacts with observer's perceptual
abilities (e.g., sensitivity). The main results include: 1) The DL system
reduces the False Alarm rate per Image on average across observer groups of
both high/low sensitivity; 2) Only human observers with high sensitivity
perform better than the DL system, while the low sensitivity group does not
surpass individual DL system performance, even when aided with the DL system
itself; 3) Increases in number of trials and decrease in viewing time were
mainly driven by the DL system only for the low sensitivity group. 4) The DL
system aids the human observer to fixate at a target by the 3rd fixation. These
results provide insights of the benefits and limitations of deep learning
systems that are collaborative or competitive with humans.
Improving Dialogue State Tracking by Discerning the Relevant Context
Authors: Sanuj Sharma, Prafulla Kumar Choubey, Ruihong Huang
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Dialogue State Tracking

A typical conversation comprises of multiple turns between participants where
they go back-and-forth between different topics. At each user turn, dialogue
state tracking (DST) aims to estimate user's goal by processing the current
utterance. However, in many turns, users implicitly refer to the previous goal,
necessitating the use of relevant dialogue history. Nonetheless, distinguishing
relevant history is challenging and a popular method of using dialogue recency
for that is inefficient. We, therefore, propose a novel framework for DST that
identifies relevant historical context by referring to the past utterances
where a particular slot-value changes and uses that together with weighted
system utterance to identify the relevant context. Specifically, we use the
current user utterance and the most recent system utterance to determine the
relevance of a system utterance. Empirical analyses show that our method
improves joint goal accuracy by 2.75% and 2.36% on WoZ 2.0 and MultiWoZ 2.0
restaurant domain datasets respectively over the previous state-of-the-art GLAD
model.
VQD: Visual Query Detection in Natural Scenes
Authors: Manoj Acharya, Karan Jariwala, Christopher Kanan
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Referring Expression, Referring Expression Comprehension, Visual Grounding

We propose Visual Query Detection (VQD), a new visual grounding task. In VQD,
a system is guided by natural language to localize a variable number of objects
in an image. VQD is related to visual referring expression recognition, where
the task is to localize only one object. We describe the first dataset for VQD
and we propose baseline algorithms that demonstrate the difficulty of the task
compared to referring expression recognition.
Affect-Driven Dialog Generation
Authors: Pierre Colombo, Wojciech Witon, Ashutosh Modi, James Kennedy, Mubbasir Kapadia
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Diversity, Reranking

The majority of current systems for end-to-end dialog generation focus on
response quality without an explicit control over the affective content of the
responses. In this paper, we present an affect-driven dialog system, which
generates emotional responses in a controlled manner using a continuous
representation of emotions. The system achieves this by modeling emotions at a
word and sequence level using: (1) a vector representation of the desired
emotion, (2) an affect regularizer, which penalizes neutral words, and (3) an
affect sampling method, which forces the neural network to generate diverse
words that are emotionally relevant. During inference, we use a reranking
procedure that aims to extract the most emotionally relevant responses using a
human-in-the-loop optimization process. We study the performance of our system
in terms of both quantitative (BLEU score and response diversity), and
qualitative (emotional appropriateness) measures.
Unifying Human and Statistical Evaluation for Natural Language Generation
Authors: Tatsunori B. Hashimoto, Hugh Zhang, Percy Liang
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Diversity, Sentence, Text Generation

How can we measure whether a natural language generation system produces both
high quality and diverse outputs? Human evaluation captures quality but not
diversity, as it does not catch models that simply plagiarize from the training
set. On the other hand, statistical evaluation (i.e., perplexity) captures
diversity but not quality, as models that occasionally emit low quality samples
would be insufficiently penalized. In this paper, we propose a unified
framework which evaluates both diversity and quality, based on the optimal
error rate of predicting whether a sentence is human- or machine-generated. We
demonstrate that this error rate can be efficiently estimated by combining
human and statistical evaluation, using an evaluation metric which we call
HUSE. On summarization and chit-chat dialogue, we show that (i) HUSE detects
diversity defects which fool pure human evaluation and that (ii) techniques
such as annealing for improving quality actually decrease HUSE due to decreased
diversity.
In Other News: A Bi-style Text-to-speech Model for Synthesizing Newscaster Voice with Limited Data
Authors: Nishant Prateek, Mateusz Łajszczak, Roberto Barra-Chicote, Thomas Drugman, Jaime Lorenzo-Trueba, Thomas Merritt, Srikanth Ronanki, Trevor Wood
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Word Embeddings

Neural text-to-speech synthesis (NTTS) models have shown significant progress
in generating high-quality speech, however they require a large quantity of
training data. This makes creating models for multiple styles expensive and
time-consuming. In this paper different styles of speech are analysed based on
prosodic variations, from this a model is proposed to synthesise speech in the
style of a newscaster, with just a few hours of supplementary data. We pose the
problem of synthesising in a target style using limited data as that of
creating a bi-style model that can synthesise both neutral-style and
newscaster-style speech via a one-hot vector which factorises the two styles.
We also propose conditioning the model on contextual word embeddings, and
extensively evaluate it against neutral NTTS, and neutral concatenative-based
synthesis. This model closes the gap in perceived style-appropriateness between
natural recordings for newscaster-style of speech, and neutral speech synthesis
by approximately two-thirds.
Crowd Transformer Network
Authors: Viresh Ranjan, Mubarak Shah, Minh Hoai Nguyen
Date: 2019-04-04 00:00:00
Tasks: Crowd Counting, Density Estimation

In this paper, we tackle the problem of Crowd Counting, and present a crowd
density estimation based approach for obtaining the crowd count. Most of the
existing crowd counting approaches rely on local features for estimating the
crowd density map. In this work, we investigate the usefulness of combining
local with non-local features for crowd counting. We use convolution layers for
extracting local features, and a type of self-attention mechanism for
extracting non-local features. We combine the local and the non-local features,
and use it for estimating crowd density map. We conduct experiments on three
publicly available Crowd Counting datasets, and achieve significant improvement
over the previous approaches.
Adaptive Sequential Machine Learning
Authors: Craig Wilson, Yuheng Bu, Venugopal Veeravalli
Date: 2019-04-04 00:00:00
Tasks: BIG-bench Machine Learning, Model Selection, Stochastic Optimization

A framework previously introduced in [3] for solving a sequence of stochastic
optimization problems with bounded changes in the minimizers is extended and
applied to machine learning problems such as regression and classification. The
stochastic optimization problems arising in these machine learning problems is
solved using algorithms such as stochastic gradient descent (SGD). A method
based on estimates of the change in the minimizers and properties of the
optimization algorithm is introduced for adaptively selecting the number of
samples at each time step to ensure that the excess risk, i.e., the expected
gap between the loss achieved by the approximate minimizer produced by the
optimization algorithm and the exact minimizer, does not exceed a target level.
A bound is developed to show that the estimate of the change in the minimizers
is non-trivial provided that the excess risk is small enough. Extensions
relevant to the machine learning setting are considered, including a cost-based
approach to select the number of samples with a cost budget over a fixed
horizon, and an approach to applying cross-validation for model selection.
Finally, experiments with synthetic and real data are used to validate the
algorithms.
Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation
Authors: Erlend Olsvik, Christian M. D. Trinh, Kristian Muri Knausgård, Arne Wiklund, Tonje Knutsen Sørdalen, Alf Ring Kleiven, Lei Jiao, Morten Goodwin
Date: 2019-04-04 00:00:00
Tasks: General Classification, Image Augmentation, Transfer Learning

Our understanding and ability to effectively monitor and manage coastal
ecosystems are severely limited by observation methods. Automatic recognition
of species in natural environment is a promising tool which would revolutionize
video and image analysis for a wide range of applications in marine ecology.
However, classifying fish from images captured by underwater cameras is in
general very challenging due to noise and illumination variations in water.
Previous classification methods in the literature relies on filtering the
images to separate the fish from the background or sharpening the images by
removing background noise. This pre-filtering process may negatively impact the
classification accuracy. In this work, we propose a Convolutional Neural
Network (CNN) using the Squeeze-and-Excitation (SE) architecture for
classifying images of fish without pre-filtering. Different from conventional
schemes, this scheme is divided into two steps. The first step is to train the
fish classifier via a public data set, i.e., Fish4Knowledge, without using
image augmentation, named as pre-training. The second step is to train the
classifier based on a new data set consisting of species that we are interested
in for classification, named as post-training. The weights obtained from
pre-training are applied to post-training as a priori. This is also known as
transfer learning. Our solution achieves the state-of-the-art accuracy of
99.27% accuracy on the pre-training. The accuracy on the post-training is
83.68%. Experiments on the post-training with image augmentation yields an
accuracy of 87.74%, indicating that the solution is viable with a larger data
set.
Complexity-Weighted Loss and Diverse Reranking for Sentence Simplification
Authors: Reno Kriz, João Sedoc, Marianna Apidianaki, Carolina Zheng, Gaurav Kumar, Eleni Miltsakaki, Chris Callison-Burch
Date: 2019-04-04 00:00:00
Proceeding: NAACL 2019 6
Tasks: Reinforcement Learning, Reranking, Sentence, Text Simplification

Sentence simplification is the task of rewriting texts so they are easier to
understand. Recent research has applied sequence-to-sequence (Seq2Seq) models
to this task, focusing largely on training-time improvements via reinforcement
learning and memory augmentation. One of the main problems with applying
generic Seq2Seq models for simplification is that these models tend to copy
directly from the original sentence, resulting in outputs that are relatively
long and complex. We aim to alleviate this issue through the use of two main
techniques. First, we incorporate content word complexities, as predicted with
a leveled word complexity model, into our loss function during training.
Second, we generate a large set of diverse candidate simplifications at test
time, and rerank these to promote fluency, adequacy, and simplicity. Here, we
measure simplicity through a novel sentence complexity model. These extensions
allow our models to perform competitively with state-of-the-art systems while
generating simpler sentences. We report standard automatic and human evaluation
metrics.