pwc-archive/papers-with-abstracts huggingface dataset:
Learning the PE Header, Malware Detection with Minimal Domain Knowledge
Authors: Edward Raff, Jared Sylvester, Charles Nicholas
Date: 2017-09-05 00:00:00
Tasks: Malware Detection

Many efforts have been made to use various forms of domain knowledge in
malware detection. Currently there exist two common approaches to malware
detection without domain knowledge, namely byte n-grams and strings. In this
work we explore the feasibility of applying neural networks to malware
detection and feature learning. We do this by restricting ourselves to a
minimal amount of domain knowledge in order to extract a portion of the
Portable Executable (PE) header. By doing this we show that neural networks can
learn from raw bytes without explicit feature construction, and perform even
better than a domain knowledge approach that parses the PE header into explicit
features.
Fine Grained Knowledge Transfer for Personalized Task-oriented Dialogue Systems
Authors: Kaixiang Mo, Yu Zhang, Qiang Yang, Pascale Fung
Date: 2017-11-11 00:00:00
Tasks: Decoder, Reinforcement Learning, Sentence, Task-Oriented Dialogue Systems, Transfer Learning

Training a personalized dialogue system requires a lot of data, and the data
collected for a single user is usually insufficient. One common practice for
this problem is to share training dialogues between different users and train
multiple sequence-to-sequence dialogue models together with transfer learning.
However, current sequence-to-sequence transfer learning models operate on the
entire sentence, which might cause negative transfer if different personal
information from different users is mixed up. We propose a personalized decoder
model to transfer finer granularity phrase-level knowledge between different
users while keeping personal preferences of each user intact. A novel personal
control gate is introduced, enabling the personalized decoder to switch between
generating personalized phrases and shared phrases. The proposed personalized
decoder model can be easily combined with various deep models and can be
trained with reinforcement learning. Real-world experimental results
demonstrate that the phrase-level personalized decoder improves the BLEU over
multiple sentence-level transfer baseline models by as much as 7.5%.
Parkinson's Disease Digital Biomarker Discovery with Optimized Transitions and Inferred Markov Emissions
Authors: Avinash Bukkittu, Baihan Lin, Trung Vu, Itsik Pe'er
Date: 2017-11-11 00:00:00

We search for digital biomarkers from Parkinson's Disease by observing
approximate repetitive patterns matching hypothesized step and stride periodic
cycles. These observations were modeled as a cycle of hidden states with
randomness allowing deviation from a canonical pattern of transitions and
emissions, under the hypothesis that the averaged features of hidden states
would serve to informatively characterize classes of patients/controls. We
propose a Hidden Semi-Markov Model (HSMM), a latent-state model, emitting
3D-acceleration vectors. Transitions and emissions are inferred from data. We
fit separate models per unique device and training label. Hidden Markov Models
(HMM) force geometric distributions of the duration spent at each state before
transition to a new state. Instead, our HSMM allows us to specify the
distribution of state duration. This modified version is more effective because
we are interested more in each state's duration than the sequence of distinct
states, allowing inclusion of these durations the feature vector.
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
Authors: Khanh Nguyen, Hal Daumé III, Jordan Boyd-Graber
Date: 2017-07-24 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Decoder, Machine Translation, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Translation

Machine translation is a natural candidate problem for reinforcement learning
from human feedback: users provide quick, dirty ratings on candidate
translations to guide a system to improve. Yet, current neural machine
translation training focuses on expensive human-generated reference
translations. We describe a reinforcement learning algorithm that improves
neural machine translation systems from simulated human feedback. Our algorithm
combines the advantage actor-critic algorithm (Mnih et al., 2016) with the
attention-based neural encoder-decoder architecture (Luong et al., 2015). This
algorithm (a) is well-designed for problems with a large action space and
delayed rewards, (b) effectively optimizes traditional corpus-level machine
translation metrics, and (c) is robust to skewed, high-variance, granular
feedback modeled after actual human behaviors.
Learning Neural Representations of Human Cognition across Many fMRI Studies
Authors: Arthur Mensch, Julien Mairal, Danilo Bzdok, Bertrand Thirion, Gaël Varoquaux
Date: 2017-10-31 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Dimensionality Reduction, Multi-Task Learning

Cognitive neuroscience is enjoying rapid increase in extensive public
brain-imaging datasets. It opens the door to large-scale statistical models.
Finding a unified perspective for all available data calls for scalable and
automated solutions to an old challenge: how to aggregate heterogeneous
information on brain function into a universal cognitive system that relates
mental operations/cognitive processes/psychological tasks to brain networks? We
cast this challenge in a machine-learning approach to predict conditions from
statistical brain maps across different studies. For this, we leverage
multi-task learning and multi-scale dimension reduction to learn
low-dimensional representations of brain images that carry cognitive
information and can be robustly associated with psychological stimuli. Our
multi-dataset classification model achieves the best prediction performance on
several large reference datasets, compared to models without cognitive-aware
low-dimension representations, it brings a substantial performance boost to the
analysis of small datasets, and can be introspected to identify universal
template cognitive concepts.
Towards ECDSA key derivation from deep embeddings for novel Blockchain applications
Authors: Christian S. Perone
Date: 2017-11-11 00:00:00
Tasks: Deep Learning, Metric Learning

In this work, we propose a straightforward method to derive Elliptic Curve
Digital Signature Algorithm (ECDSA) key pairs from embeddings created using
Deep Learning and Metric Learning approaches. We also show that these keys
allows the derivation of cryptocurrencies (such as Bitcoin) addresses that can
be used to transfer and receive funds, allowing novel Blockchain-based
applications that can be used to transfer funds or data directly to domains
such as image, text, sound or any other domain where Deep Learning can extract
high-quality embeddings; providing thus a novel integration between the
properties of the Blockchain-based technologies such as trust minimization and
decentralization together with the high-quality learned representations from
Deep Learning techniques.
Going Further with Point Pair Features
Authors: Stefan Hinterstoisser, Vincent Lepetit, Naresh Rajkumar, Kurt Konolige
Date: 2017-11-11 00:00:00
Tasks: 6D Pose Estimation using RGB

Point Pair Features is a widely used method to detect 3D objects in point
clouds, however they are prone to fail in presence of sensor noise and
background clutter. We introduce novel sampling and voting schemes that
significantly reduces the influence of clutter and sensor noise. Our
experiments show that with our improvements, PPFs become competitive against
state-of-the-art methods as it outperforms them on several objects from
challenging benchmarks, at a low computational cost.
Representation Learning for Grounded Spatial Reasoning
Authors: Michael Janner, Karthik Narasimhan, Regina Barzilay
Date: 2017-07-13 00:00:00
Proceeding: TACL 2018 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning, Spatial Reasoning

The interpretation of spatial references is highly contextual, requiring
joint inference over both language and the environment. We consider the task of
spatial reasoning in a simulated environment, where an agent can act and
receive rewards. The proposed model learns a representation of the world
steered by instruction text. This design allows for precise alignment of local
neighborhoods with corresponding verbalizations, while also handling global
references in the instructions. We train our model with reinforcement learning
using a variant of generalized value iteration. The model outperforms
state-of-the-art approaches on several metrics, yielding a 45% reduction in
goal localization error.
A Motion Planning Strategy for the Active Vision-Based Mapping of Ground-Level Structures
Authors: Manikandasriram Srinivasan Ramanagopal, André Phu-Van Nguyen, Jerome Le Ny
Date: 2016-02-22 00:00:00
Tasks: Motion Planning

This paper presents a strategy to guide a mobile ground robot equipped with a
camera or depth sensor, in order to autonomously map the visible part of a
bounded three-dimensional structure. We describe motion planning algorithms
that determine appropriate successive viewpoints and attempt to fill holes
automatically in a point cloud produced by the sensing and perception layer.
The emphasis is on accurately reconstructing a 3D model of a structure of
moderate size rather than mapping large open environments, with applications
for example in architecture, construction and inspection. The proposed
algorithms do not require any initialization in the form of a mesh model or a
bounding box, and the paths generated are well adapted to situations where the
vision sensor is used simultaneously for mapping and for localizing the robot,
in the absence of additional absolute positioning system. We analyze the
coverage properties of our policy, and compare its performance to the classic
frontier based exploration algorithm. We illustrate its efficacy for different
structure sizes, levels of localization accuracy and range of the depth sensor,
and validate our design on a real-world experiment.
CT-SRCNN: Cascade Trained and Trimmed Deep Convolutional Neural Networks for Image Super Resolution
Authors: Haoyu Ren, Mostafa El-Khamy, Jungwon Lee
Date: 2017-11-11 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

We propose methodologies to train highly accurate and efficient deep
convolutional neural networks (CNNs) for image super resolution (SR). A cascade
training approach to deep learning is proposed to improve the accuracy of the
neural networks while gradually increasing the number of network layers. Next,
we explore how to improve the SR efficiency by making the network slimmer. Two
methodologies, the one-shot trimming and the cascade trimming, are proposed.
With the cascade trimming, the network's size is gradually reduced layer by
layer, without significant loss on its discriminative ability. Experiments on
benchmark image datasets show that our proposed SR network achieves the
state-of-the-art super resolution accuracy, while being more than 4 times
faster compared to existing deep super resolution networks.
DeepKSPD: Learning Kernel-matrix-based SPD Representation for Fine-grained Image Recognition
Authors: Melih Engin, Lei Wang, Luping Zhou, Xinwang Liu
Date: 2017-11-11 00:00:00
Proceeding: ECCV 2018 9
Tasks: Fine-Grained Image Recognition

Being symmetric positive-definite (SPD), covariance matrix has traditionally
been used to represent a set of local descriptors in visual recognition. Recent
study shows that kernel matrix can give considerably better representation by
modelling the nonlinearity in the local descriptor set. Nevertheless, neither
the descriptors nor the kernel matrix is deeply learned. Worse, they are
considered separately, hindering the pursuit of an optimal SPD representation.
This work proposes a deep network that jointly learns local descriptors,
kernel-matrix-based SPD representation, and the classifier via an end-to-end
training process. We derive the derivatives for the mapping from a local
descriptor set to the SPD representation to carry out backpropagation. Also, we
exploit the Daleckii-Krein formula in operator theory to give a concise and
unified result on differentiating SPD matrix functions, including the matrix
logarithm to handle the Riemannian geometry of kernel matrix. Experiments not
only show the superiority of kernel-matrix-based SPD representation with deep
local descriptors, but also verify the advantage of the proposed deep network
in pursuing better SPD representations for fine-grained image recognition
tasks.
Adversarial Generation of Training Examples: Applications to Moving Vehicle License Plate Recognition
Authors: Xinlong Wang, Zhipeng Man, Mingyu You, Chunhua Shen
Date: 2017-07-11 00:00:00
Tasks: CPU, Image Generation, License Plate Recognition

Generative Adversarial Networks (GAN) have attracted much research attention
recently, leading to impressive results for natural image generation. However,
to date little success was observed in using GAN generated images for improving
classification tasks. Here we attempt to explore, in the context of car license
plate recognition, whether it is possible to generate synthetic training data
using GAN to improve recognition accuracy. With a carefully-designed pipeline,
we show that the answer is affirmative. First, a large-scale image set is
generated using the generator of GAN, without manual annotation. Then, these
images are fed to a deep convolutional neural network (DCNN) followed by a
bidirectional recurrent neural network (BRNN) with long short-term memory
(LSTM), which performs the feature learning and sequence labelling. Finally,
the pre-trained model is fine-tuned on real images. Our experimental results on
a few data sets demonstrate the effectiveness of using GAN images: an
improvement of 7.5% over a strong baseline with moderate-sized real data being
available. We show that the proposed framework achieves competitive recognition
accuracy on challenging test datasets. We also leverage the depthwise separate
convolution to construct a lightweight convolutional RNN, which is about half
size and 2x faster on CPU. Combining this framework and the proposed pipeline,
we make progress in performing accurate recognition on mobile and embedded
devices.
Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning
Authors: Daniel Lopez-Martinez, Ognjen Rudovic, Rosalind Picard
Date: 2017-11-10 00:00:00
Tasks: BIG-bench Machine Learning

Pain is a subjective experience commonly measured through patient's self
report. While there exist numerous situations in which automatic pain
estimation methods may be preferred, inter-subject variability in physiological
and behavioral pain responses has hindered the development of such methods. In
this work, we address this problem by introducing a novel personalized
multitask machine learning method for pain estimation based on individual
physiological and behavioral pain response profiles, and show its advantages in
a dataset containing multimodal responses to nociceptive heat pain.
Deep Within-Class Covariance Analysis for Robust Audio Representation Learning
Authors: Hamid Eghbal-zadeh, Matthias Dorfer, Gerhard Widmer
Date: 2017-11-10 00:00:00
Tasks: Acoustic Scene Classification, Classification, General Classification, Representation Learning, Scene Classification

Convolutional Neural Networks (CNNs) can learn effective features, though
have been shown to suffer from a performance drop when the distribution of the
data changes from training to test data. In this paper we analyze the internal
representations of CNNs and observe that the representations of unseen data in
each class, spread more (with higher variance) in the embedding space of the
CNN compared to representations of the training data. More importantly, this
difference is more extreme if the unseen data comes from a shifted
distribution. Based on this observation, we objectively evaluate the degree of
representation's variance in each class via eigenvalue decomposition on the
within-class covariance of the internal representations of CNNs and observe the
same behaviour. This can be problematic as larger variances might lead to
mis-classification if the sample crosses the decision boundary of its class. We
apply nearest neighbor classification on the representations and empirically
show that the embeddings with the high variance actually have significantly
worse KNN classification performances, although this could not be foreseen from
their end-to-end classification results. To tackle this problem, we propose
Deep Within-Class Covariance Analysis (DWCCA), a deep neural network layer that
significantly reduces the within-class covariance of a DNN's representation,
improving performance on unseen test data from a shifted distribution. We
empirically evaluate DWCCA on two datasets for Acoustic Scene Classification
(DCASE2016 and DCASE2017). We demonstrate that not only does DWCCA
significantly improve the network's internal representation, it also increases
the end-to-end classification accuracy, especially when the test set exhibits a
distribution shift. By adding DWCCA to a VGG network, we achieve around 6
percentage points improvement in the case of a distribution mismatch.
A Batch Learning Framework for Scalable Personalized Ranking
Authors: Kuan Liu, Prem Natarajan
Date: 2017-11-10 00:00:00

In designing personalized ranking algorithms, it is desirable to encourage a
high precision at the top of the ranked list. Existing methods either seek a
smooth convex surrogate for a non-smooth ranking metric or directly modify
updating procedures to encourage top accuracy. In this work we point out that
these methods do not scale well to a large-scale setting, and this is partly
due to the inaccurate pointwise or pairwise rank estimation. We propose a new
framework for personalized ranking. It uses batch-based rank estimators and
smooth rank-sensitive loss functions. This new batch learning framework leads
to more stable and accurate rank approximations compared to previous work.
Moreover, it enables explicit use of parallel computation to speed up training.
We conduct empirical evaluation on three item recommendation tasks. Our method
shows consistent accuracy improvements over state-of-the-art methods.
Additionally, we observe time efficiency advantages when data scale increases.
WMRB: Learning to Rank in a Scalable Batch Training Approach
Authors: Kuan Liu, Prem Natarajan
Date: 2017-11-10 00:00:00
Tasks: Learning-To-Rank

We propose a new learning to rank algorithm, named Weighted Margin-Rank Batch
loss (WMRB), to extend the popular Weighted Approximate-Rank Pairwise loss
(WARP). WMRB uses a new rank estimator and an efficient batch training
algorithm. The approach allows more accurate item rank approximation and
explicit utilization of parallel computation to accelerate training. In three
item recommendation tasks, WMRB consistently outperforms WARP and other
baselines. Moreover, WMRB shows clear time efficiency advantages as data scale
increases.
Stream Reasoning in Temporal Datalog
Authors: Alessandro Ronca, Mark Kaminski, Bernardo Cuenca Grau, Boris Motik, Ian Horrocks
Date: 2017-11-10 00:00:00

In recent years, there has been an increasing interest in extending
traditional stream processing engines with logical, rule-based, reasoning
capabilities. This poses significant theoretical and practical challenges since
rules can derive new information and propagate it both towards past and future
time points; as a result, streamed query answers can depend on data that has
not yet been received, as well as on data that arrived far in the past. Stream
reasoning algorithms, however, must be able to stream out query answers as soon
as possible, and can only keep a limited number of previous input facts in
memory. In this paper, we propose novel reasoning problems to deal with these
challenges, and study their computational properties on Datalog extended with a
temporal sort and the successor function (a core rule-based language for stream
reasoning applications).
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
Authors: Caiming Xiong, Victor Zhong, Richard Socher
Date: 2017-10-31 00:00:00
Proceeding: ICLR 2018 1
Tasks: Question Answering

Traditional models for question answering optimize using cross entropy loss,
which encourages exact answers at the cost of penalizing nearby or overlapping
answers that are sometimes equally accurate. We propose a mixed objective that
combines cross entropy loss with self-critical policy learning. The objective
uses rewards derived from word overlap to solve the misalignment between
evaluation metric and optimization objective. In addition to the mixed
objective, we improve dynamic coattention networks (DCN) with a deep residual
coattention encoder that is inspired by recent work in deep self-attention and
residual networks. Our proposals improve model performance across question
types and input lengths, especially for long questions that requires the
ability to capture long-term dependencies. On the Stanford Question Answering
Dataset, our model achieves state-of-the-art results with 75.1% exact match
accuracy and 83.1% F1, while the ensemble obtains 78.9% exact match accuracy
and 86.0% F1.
Improved Twitter Sentiment Analysis Using Naive Bayes and Custom Language Model
Authors: Angela Lin
Date: 2017-11-10 00:00:00
Tasks: BIG-bench Machine Learning, Language Modeling, Language Modelling, Sentiment Analysis, Twitter Sentiment Analysis

In the last couple decades, social network services like Twitter have
generated large volumes of data about users and their interests, providing
meaningful business intelligence so organizations can better understand and
engage their customers. All businesses want to know who is promoting their
products, who is complaining about them, and how are these opinions bringing or
diminishing value to a company. Companies want to be able to identify their
high-value customers and quantify the value each user brings. Many businesses
use social media metrics to calculate the user contribution score, which
enables them to quantify the value that influential users bring on social
media, so the businesses can offer them more differentiated services. However,
the score calculation can be refined to provide a better illustration of a
user's contribution. Using Microsoft Azure as a case study, we conducted
Twitter sentiment analysis to develop a machine learning classification model
that identifies tweet contents and sentiments most illustrative of
positive-value user contribution. Using data mining and AI-powered cognitive
tools, we analyzed factors of social influence and specifically, promotional
language in the developer community. Our predictive model was a combination of
a traditional supervised machine learning algorithm and a custom-developed
natural language model for identifying promotional tweets, that identifies a
product-specific promotion on Twitter with a 90% accuracy rate.