pwc-archive/papers-with-abstracts huggingface dataset:
BFGAN: Backward and Forward Generative Adversarial Networks for Lexically Constrained Sentence Generation
Authors: Dayiheng Liu, Jie Fu, Qian Qu, Jiancheng Lv
Date: 2018-06-21 00:00:00
Tasks: Image Captioning, Machine Translation, Sentence, Translation

Incorporating prior knowledge like lexical constraints into the model's
output to generate meaningful and coherent sentences has many applications in
dialogue system, machine translation, image captioning, etc. However, existing
RNN-based models incrementally generate sentences from left to right via beam
search, which makes it difficult to directly introduce lexical constraints into
the generated sentences. In this paper, we propose a new algorithmic framework,
dubbed BFGAN, to address this challenge. Specifically, we employ a backward
generator and a forward generator to generate lexically constrained sentences
together, and use a discriminator to guide the joint training of two generators
by assigning them reward signals. Due to the difficulty of BFGAN training, we
propose several training techniques to make the training process more stable
and efficient. Our extensive experiments on two large-scale datasets with human
evaluation demonstrate that BFGAN has significant improvements over previous
methods.
Deep Reinforcement Learning for Surgical Gesture Segmentation and Classification
Authors: Daochang Liu, Tingting Jiang
Date: 2018-06-21 00:00:00
Tasks: Action Segmentation, Classification, Decision Making, Deep Reinforcement Learning, General Classification, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Segmentation, Sequential Decision Making, Surgical Gesture Recognition

Recognition of surgical gesture is crucial for surgical skill assessment and
efficient surgery training. Prior works on this task are based on either
variant graphical models such as HMMs and CRFs, or deep learning models such as
Recurrent Neural Networks and Temporal Convolutional Networks. Most of the
current approaches usually suffer from over-segmentation and therefore low
segment-level edit scores. In contrast, we present an essentially different
methodology by modeling the task as a sequential decision-making process. An
intelligent agent is trained using reinforcement learning with hierarchical
features from a deep model. Temporal consistency is integrated into our action
design and reward mechanism to reduce over-segmentation errors. Experiments on
JIGSAWS dataset demonstrate that the proposed method performs better than
state-of-the-art methods in terms of the edit score and on par in frame-wise
accuracy. Our code will be released later.
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices
Authors: Thomas B. Preußer, Giulio Gambardella, Nicholas Fraser, Michaela Blott
Date: 2018-06-21 00:00:00
Tasks: All, CPU, Decision Making, image-classification, Image Classification, Object, object-detection, Object Detection, Quantization, Real-Time Object Detection

Neural networks have established as a generic and powerful means to approach
challenging problems such as image classification, object detection or decision
making. Their successful employment foots on an enormous demand of compute. The
quantization of network parameters and the processed data has proven a valuable
measure to reduce the challenges of network inference so effectively that the
feasible scope of applications is expanded even into the embedded domain. This
paper describes the making of a real-time object detection in a live video
stream processed on an embedded all-programmable device. The presented case
illustrates how the required processing is tamed and parallelized across both
the CPU cores and the programmable logic and how the most suitable resources
and powerful extensions, such as NEON vectorization, are leveraged for the
individual processing steps. The crafted result is an extended Darknet
framework implementing a fully integrated, end-to-end solution from video
capture over object annotation to video output applying neural network
inference at different quantization levels running at 16~frames per second on
an embedded Zynq UltraScale+ (XCZU3EG) platform.
Expanding the Active Inference Landscape: More Intrinsic Motivations in the Perception-Action Loop
Authors: Martin Biehl, Christian Guckelsberger, Christoph Salge, Simón C. Smith, Daniel Polani
Date: 2018-06-21 00:00:00
Tasks: Reinforcement Learning

Active inference is an ambitious theory that treats perception, inference and
action selection of autonomous agents under the heading of a single principle.
It suggests biologically plausible explanations for many cognitive phenomena,
including consciousness. In active inference, action selection is driven by an
objective function that evaluates possible future actions with respect to
current, inferred beliefs about the world. Active inference at its core is
independent from extrinsic rewards, resulting in a high level of robustness
across e.g.\ different environments or agent morphologies. In the literature,
paradigms that share this independence have been summarised under the notion of
intrinsic motivations. In general and in contrast to active inference, these
models of motivation come without a commitment to particular inference and
action selection mechanisms. In this article, we study if the inference and
action selection machinery of active inference can also be used by alternatives
to the originally included intrinsic motivation. The perception-action loop
explicitly relates inference and action selection to the environment and agent
memory, and is consequently used as foundation for our analysis. We reconstruct
the active inference approach, locate the original formulation within, and show
how alternative intrinsic motivations can be used while keeping many of the
original features intact. Furthermore, we illustrate the connection to
universal reinforcement learning by means of our formalism. Active inference
research may profit from comparisons of the dynamics induced by alternative
intrinsic motivations. Research on intrinsic motivations may profit from an
additional way to implement intrinsically motivated agents that also share the
biological plausibility of active inference.
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
Authors: Manqing Dong, Lina Yao, Xianzhi Wang, Boualem Benatallah, Shuai Zhang
Date: 2018-06-21 00:00:00

Random forest and deep neural network are two schools of effective
classification methods in machine learning. While the random forest is robust
irrespective of the data domain, the deep neural network has advantages in
handling high dimensional data. In view that a differentiable neural decision
forest can be added to the neural network to fully exploit the benefits of both
models, in our work, we further combine convolutional autoencoder with neural
decision forest, where autoencoder has its advantages in finding the hidden
representations of the input data. We develop a gradient boost module and embed
it into the proposed convolutional autoencoder with neural decision forest to
improve the performance. The idea of gradient boost is to learn and use the
residual in the prediction. In addition, we design a structure to learn the
parameters of the neural decision forest and gradient boost module at
contiguous steps. The extensive experiments on several public datasets
demonstrate that our proposed model achieves good efficiency and prediction
performance compared with a series of baseline methods.
Finding Original Image Of A Sub Image Using CNNs
Authors: Raja Asim
Date: 2018-06-21 00:00:00
Tasks: image-classification, Image Classification, object-detection, Object Detection

Convolututional Neural Networks have achieved state of the art in image
classification, object detection and other image related tasks. In this paper I
present another use of CNNs i.e. if given a set of images and then giving a
single test image the network identifies that the test image is part of which
image from the images given before. This is a task somehow similar to measuring
image similarity and can be done using a simple CNN. Doing this task manually
by looping can be quite a time consuming problem and won't be a generalizable
solution. The task is quite similar to doing object detection but for that lots
training data should be given or in the case of sliding window it takes lot of
time and my algorithm can work with much fewer examples, is totally
unsupervised and works much efficiently. Also, I explain that how unsupervised
algorithm like K-Means or supervised algorithm like K-NN are not good enough to
perform this task. The basic idea is that image encodings are collected for
each image from a CNN, when a test image comes it is replaced by a part of
original image, the encoding is generated using the same network, the frobenius
norm is calculated and if it comes under a tolerance level then the test image
is said to be the part of the original image.
Dictionary-Guided Editing Networks for Paraphrase Generation
Authors: Shaohan Huang, Yu Wu, Furu Wei, Ming Zhou
Date: 2018-06-21 00:00:00
Tasks: Paraphrase Generation, Sentence

An intuitive way for a human to write paraphrase sentences is to replace
words or phrases in the original sentence with their corresponding synonyms and
make necessary changes to ensure the new sentences are fluent and grammatically
correct. We propose a novel approach to modeling the process with
dictionary-guided editing networks which effectively conduct rewriting on the
source sentence to generate paraphrase sentences. It jointly learns the
selection of the appropriate word level and phrase level paraphrase pairs in
the context of the original sentence from an off-the-shelf dictionary as well
as the generation of fluent natural language sentences. Specifically, the
system retrieves a set of word level and phrase level araphrased pairs derived
from the Paraphrase Database (PPDB) for the original sentence, which is used to
guide the decision of which the words might be deleted or inserted with the
soft attention mechanism under the sequence-to-sequence framework. We conduct
experiments on two benchmark datasets for paraphrase generation, namely the
MSCOCO and Quora dataset. The evaluation results demonstrate that our
dictionary-guided editing networks outperforms the baseline methods.
Learning Cognitive Models using Neural Networks
Authors: Devendra Singh Chaplot, Christopher MacLellan, Ruslan Salakhutdinov, Kenneth Koedinger
Date: 2018-06-21 00:00:00
Tasks: Model Discovery

A cognitive model of human learning provides information about skills a
learner must acquire to perform accurately in a task domain. Cognitive models
of learning are not only of scientific interest, but are also valuable in
adaptive online tutoring systems. A more accurate model yields more effective
tutoring through better instructional decisions. Prior methods of automated
cognitive model discovery have typically focused on well-structured domains,
relied on student performance data or involved substantial human knowledge
engineering. In this paper, we propose Cognitive Representation Learner
(CogRL), a novel framework to learn accurate cognitive models in ill-structured
domains with no data and little to no human knowledge engineering. Our
contribution is two-fold: firstly, we show that representations learnt using
CogRL can be used for accurate automatic cognitive model discovery without
using any student performance data in several ill-structured domains: Rumble
Blocks, Chinese Character, and Article Selection. This is especially effective
and useful in domains where an accurate human-authored cognitive model is
unavailable or authoring a cognitive model is difficult. Secondly, for domains
where a cognitive model is available, we show that representations learned
through CogRL can be used to get accurate estimates of skill difficulty and
learning rate parameters without using any student performance data. These
estimates are shown to highly correlate with estimates using student
performance data on an Article Selection dataset.
Towards a Grounded Dialog Model for Explainable Artificial Intelligence
Authors: Prashan Madumal, Tim Miller, Frank Vetere, Liz Sonenberg
Date: 2018-06-21 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

To generate trust with their users, Explainable Artificial Intelligence (XAI)
systems need to include an explanation model that can communicate the internal
decisions, behaviours and actions to the interacting humans. Successful
explanation involves both cognitive and social processes. In this paper we
focus on the challenge of meaningful interaction between an explainer and an
explainee and investigate the structural aspects of an explanation in order to
propose a human explanation dialog model. We follow a bottom-up approach to
derive the model by analysing transcripts of 398 different explanation dialog
types. We use grounded theory to code and identify key components of which an
explanation dialog consists. We carry out further analysis to identify the
relationships between components and sequences and cycles that occur in a
dialog. We present a generalized state model obtained by the analysis and
compare it with an existing conceptual dialog model of explanation.
Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
Authors: Jiaxiang Wu, Weidong Huang, Junzhou Huang, Tong Zhang
Date: 2018-06-21 00:00:00
Proceeding: ICML 2018 7
Tasks: Distributed Optimization, Quantization

Large-scale distributed optimization is of great importance in various
applications. For data-parallel based distributed learning, the inter-node
gradient communication often becomes the performance bottleneck. In this paper,
we propose the error compensated quantized stochastic gradient descent
algorithm to improve the training efficiency. Local gradients are quantized to
reduce the communication overhead, and accumulated quantization error is
utilized to speed up the convergence. Furthermore, we present theoretical
analysis on the convergence behaviour, and demonstrate its advantage over
competitors. Extensive experiments indicate that our algorithm can compress
gradients by a factor of up to two magnitudes without performance degradation.
On the Robustness of Interpretability Methods
Authors: David Alvarez-Melis, Tommi S. Jaakkola
Date: 2018-06-21 00:00:00

We argue that robustness of explanations---i.e., that similar inputs should
give rise to similar explanations---is a key desideratum for interpretability.
We introduce metrics to quantify robustness and demonstrate that current
methods do not perform well according to these metrics. Finally, we propose
ways that robustness can be enforced on existing interpretability approaches.
Flexible Neural Representation for Physics Prediction
Authors: Damian Mrowca, Chengxu Zhuang, Elias Wang, Nick Haber, Li Fei-Fei, Joshua B. Tenenbaum, Daniel L. K. Yamins
Date: 2018-06-21 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Prediction, Relation Network

Humans have a remarkable capacity to understand the physical dynamics of
objects in their environment, flexibly capturing complex structures and
interactions at multiple levels of detail. Inspired by this ability, we propose
a hierarchical particle-based object representation that covers a wide variety
of types of three-dimensional objects, including both arbitrary rigid
geometrical shapes and deformable materials. We then describe the Hierarchical
Relation Network (HRN), an end-to-end differentiable neural network based on
hierarchical graph convolution, that learns to predict physical dynamics in
this representation. Compared to other neural network baselines, the HRN
accurately handles complex collisions and nonrigid deformations, generating
plausible dynamics predictions at long time scales in novel settings, and
scaling to large scene configurations. These results demonstrate an
architecture with the potential to form the basis of next-generation physics
predictors for use in computer vision, robotics, and quantitative cognitive
science.
Coherence Models for Dialogue
Authors: Alessandra Cervone, Evgeny Stepanov, Giuseppe Riccardi
Date: 2018-06-21 00:00:00

Coherence across multiple turns is a major challenge for state-of-the-art
dialogue models. Arguably the most successful approach to automatically
learning text coherence is the entity grid, which relies on modelling patterns
of distribution of entities across multiple sentences of a text. Originally
applied to the evaluation of automatic summaries and the news genre, among its
many extensions, this model has also been successfully used to assess dialogue
coherence. Nevertheless, both the original grid and its extensions do not model
intents, a crucial aspect that has been studied widely in the literature in
connection to dialogue structure. We propose to augment the original grid
document representation for dialogue with the intentional structure of the
conversation. Our models outperform the original grid representation on both
text discrimination and insertion, the two main standard tasks for coherence
assessment across three different dialogue datasets, confirming that intents
play a key role in modelling dialogue coherence.
An empirical study on the names of points of interest and their changes with geographic distance
Authors: Yingjie Hu, Krzysztof Janowicz
Date: 2018-06-21 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Information Retrieval, Retrieval

While Points Of Interest (POIs), such as restaurants, hotels, and barber
shops, are part of urban areas irrespective of their specific locations, the
names of these POIs often reveal valuable information related to local culture,
landmarks, influential families, figures, events, and so on. Place names have
long been studied by geographers, e.g., to understand their origins and
relations to family names. However, there is a lack of large-scale empirical
studies that examine the localness of place names and their changes with
geographic distance. In addition to enhancing our understanding of the
coherence of geographic regions, such empirical studies are also significant
for geographic information retrieval where they can inform computational models
and improve the accuracy of place name disambiguation. In this work, we conduct
an empirical study based on 112,071 POIs in seven US metropolitan areas
extracted from an open Yelp dataset. We propose to adopt term frequency and
inverse document frequency in geographic contexts to identify local terms used
in POI names and to analyze their usages across different POI types. Our
results show an uneven usage of local terms across POI types, which is highly
consistent among different geographic regions. We also examine the decaying
effect of POI name similarity with the increase of distance among POIs. While
our analysis focuses on urban POI names, the presented methods can be
generalized to other place types as well, such as mountain peaks and streets.
Pixel-level Reconstruction and Classification for Noisy Handwritten Bangla Characters
Authors: Manohar Karki, Qun Liu, Robert DiBiano, Saikat Basu, Supratik Mukhopadhyay
Date: 2018-06-21 00:00:00
Tasks: Classification, Document Image Classification, General Classification, Image Classification

Classification techniques for images of handwritten characters are
susceptible to noise. Quadtrees can be an efficient representation for learning
from sparse features. In this paper, we improve the effectiveness of
probabilistic quadtrees by using a pixel level classifier to extract the
character pixels and remove noise from handwritten character images. The pixel
level denoiser (a deep belief network) uses the map responses obtained from a
pretrained CNN as features for reconstructing the characters eliminating noise.
We experimentally demonstrate the effectiveness of our approach by
reconstructing and classifying a noisy version of handwritten Bangla Numeral
and Basic Character datasets.
Gradient Adversarial Training of Neural Networks
Authors: Ayan Sinha, Zhao Chen, Vijay Badrinarayanan, Andrew Rabinovich
Date: 2018-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification, Knowledge Distillation, Multi-Task Learning

We propose gradient adversarial training, an auxiliary deep learning
framework applicable to different machine learning problems. In gradient
adversarial training, we leverage a prior belief that in many contexts,
simultaneous gradient updates should be statistically indistinguishable from
each other. We enforce this consistency using an auxiliary network that
classifies the origin of the gradient tensor, and the main network serves as an
adversary to the auxiliary network in addition to performing standard
task-based training. We demonstrate gradient adversarial training for three
different scenarios: (1) as a defense to adversarial examples we classify
gradient tensors and tune them to be agnostic to the class of their
corresponding example, (2) for knowledge distillation, we do binary
classification of gradient tensors derived from the student or teacher network
and tune the student gradient tensor to mimic the teacher's gradient tensor;
and (3) for multi-task learning we classify the gradient tensors derived from
different task loss functions and tune them to be statistically
indistinguishable. For each of the three scenarios we show the potential of
gradient adversarial training procedure. Specifically, gradient adversarial
training increases the robustness of a network to adversarial attacks, is able
to better distill the knowledge from a teacher network to a student network
compared to soft targets, and boosts multi-task learning by aligning the
gradient tensors derived from the task specific loss functions. Overall, our
experiments demonstrate that gradient tensors contain latent information about
whatever tasks are being trained, and can support diverse machine learning
problems when intelligently guided through adversarialization using a auxiliary
network.
Stability of Scattering Decoder For Nonlinear Diffractive Imaging
Authors: Yu Sun, Ulugbek S. Kamilov
Date: 2018-06-20 00:00:00
Tasks: Decoder, Image Reconstruction

The problem of image reconstruction under multiple light scattering is
usually formulated as a regularized non-convex optimization. A deep learning
architecture, Scattering Decoder (ScaDec), was recently proposed to solve this
problem in a purely data-driven fashion. The proposed method was shown to
substantially outperform optimization-based baselines and achieve
state-of-the-art results. In this paper, we thoroughly test the robustness of
ScaDec to different permittivity contrasts, number of transmissions, and input
signal-to-noise ratios. The results on high-fidelity simulated datasets show
that the performance of ScaDec is stable in different settings.
Fairness Without Demographics in Repeated Loss Minimization
Authors: Tatsunori B. Hashimoto, Megha Srivastava, Hongseok Namkoong, Percy Liang
Date: 2018-06-20 00:00:00
Proceeding: ICML 2018 7
Tasks: Fairness

Machine learning models (e.g., speech recognizers) are usually trained to
minimize average loss, which results in representation disparity---minority
groups (e.g., non-native speakers) contribute less to the training objective
and thus tend to suffer higher loss. Worse, as model accuracy affects user
retention, a minority group can shrink over time. In this paper, we first show
that the status quo of empirical risk minimization (ERM) amplifies
representation disparity over time, which can even make initially fair models
unfair. To mitigate this, we develop an approach based on distributionally
robust optimization (DRO), which minimizes the worst case risk over all
distributions close to the empirical distribution. We prove that this approach
controls the risk of the minority group at each time step, in the spirit of
Rawlsian distributive justice, while remaining oblivious to the identity of the
groups. We demonstrate that DRO prevents disparity amplification on examples
where ERM fails, and show improvements in minority group user satisfaction in a
real-world text autocomplete task.