pwc-archive/papers-with-abstracts huggingface dataset:
Neural Fine-Grained Entity Type Classification with Hierarchy-Aware Loss
Authors: Peng Xu, Denilson Barbosa
Date: 2018-03-09 00:00:00
Proceeding: NAACL 2018 6
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentence, Vocal Bursts Type Prediction, Word Embeddings

The task of Fine-grained Entity Type Classification (FETC) consists of
assigning types from a hierarchy to entity mentions in text. Existing methods
rely on distant supervision and are thus susceptible to noisy labels that can
be out-of-context or overly-specific for the training sentence. Previous
methods that attempt to address these issues do so with heuristics or with the
help of hand-crafted features. Instead, we propose an end-to-end solution with
a neural network model that uses a variant of cross- entropy loss function to
handle out-of-context labels, and hierarchical loss normalization to cope with
overly-specific ones. Also, previous work solve FETC a multi-label
classification followed by ad-hoc post-processing. In contrast, our solution is
more elegant: we use public word embeddings to train a single-label that
jointly learns representations for entity mentions and their context. We show
experimentally that our approach is robust against noise and consistently
outperforms the state-of-the-art on established benchmarks for the task.
Real-Time 6DOF Pose Relocalization for Event Cameras with Stacked Spatial LSTM Networks
Authors: Anh Nguyen, Thanh-Toan Do, Darwin G. Caldwell, Nikos G. Tsagarakis
Date: 2017-08-22 00:00:00

We present a new method to relocalize the 6DOF pose of an event camera solely
based on the event stream. Our method first creates the event image from a list
of events that occurs in a very short time interval, then a Stacked Spatial
LSTM Network (SP-LSTM) is used to learn the camera pose. Our SP-LSTM is
composed of a CNN to learn deep features from the event images and a stack of
LSTM to learn spatial dependencies in the image feature space. We show that the
spatial dependency plays an important role in the relocalization task and the
SP-LSTM can effectively learn this information. The experimental results on a
publicly available dataset show that our approach generalizes well and
outperforms recent methods by a substantial margin. Overall, our proposed
method reduces by approx. 6 times the position error and 3 times the
orientation error compared to the current state of the art. The source code and
trained models will be released.
TBD: Benchmarking and Analyzing Deep Neural Network Training
Authors: Hongyu Zhu, Mohamed Akrout, Bojian Zheng, Andrew Pelegris, Amar Phanishayee, Bianca Schroeder, Gennady Pekhimenko
Date: 2018-03-16 00:00:00
Tasks: Benchmarking, General Classification, GPU, image-classification, Image Classification, Machine Translation, object-detection, Object Detection, Reinforcement Learning, speech-recognition, Speech Recognition

The recent popularity of deep neural networks (DNNs) has generated a lot of
research interest in performing DNN-related computation efficiently. However,
the primary focus is usually very narrow and limited to (i) inference -- i.e.
how to efficiently execute already trained models and (ii) image classification
networks as the primary benchmark for evaluation.
  Our primary goal in this work is to break this myopic view by (i) proposing a
new benchmark for DNN training, called TBD (TBD is short for Training Benchmark
for DNNs), that uses a representative set of DNN models that cover a wide range
of machine learning applications: image classification, machine translation,
speech recognition, object detection, adversarial networks, reinforcement
learning, and (ii) by performing an extensive performance analysis of training
these different applications on three major deep learning frameworks
(TensorFlow, MXNet, CNTK) across different hardware configurations (single-GPU,
multi-GPU, and multi-machine). TBD currently covers six major application
domains and eight different state-of-the-art models.
  We present a new toolchain for performance analysis for these models that
combines the targeted usage of existing performance analysis tools, careful
selection of new and existing metrics and methodologies to analyze the results,
and utilization of domain specific characteristics of DNN training. We also
build a new set of tools for memory profiling in all three major frameworks;
much needed tools that can finally shed some light on precisely how much memory
is consumed by different data structures (weights, activations, gradients,
workspace) in DNN training. By using our tools and methodologies, we make
several important observations and recommendations on where the future research
and optimization of DNN training should be focused.
HyperFusion-Net: Densely Reflective Fusion for Salient Object Detection
Authors: Pingping Zhang, Huchuan Lu, Chunhua Shen
Date: 2018-04-14 00:00:00
Tasks: object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection

Salient object detection (SOD), which aims to find the most important region
of interest and segment the relevant object/item in that area, is an important
yet challenging vision task. This problem is inspired by the fact that human
seems to perceive main scene elements with high priorities. Thus, accurate
detection of salient objects in complex scenes is critical for human-computer
interaction. In this paper, we present a novel feature learning framework for
SOD, in which we cast the SOD as a pixel-wise classification problem. The
proposed framework utilizes a densely hierarchical feature fusion network,
named HyperFusion-Net, automatically predicts the most important area and
segments the associated objects in an end-to-end manner. Specifically, inspired
by the human perception system and image reflection separation, we first
decompose input images into reflective image pairs by content-preserving
transforms. Then, the complementary information of reflective image pairs is
jointly extracted by an interweaved convolutional neural network (ICNN) and
hierarchically combined with a hyper-dense fusion mechanism. Based on the fused
multi-scale features, our method finally achieves a promising way of predicting
SOD. As shown in our extensive experiments, the proposed method consistently
outperforms other state-of-the-art methods on seven public datasets with a
large margin.
Iterative Refinement for Machine Translation
Authors: Roman Novak, Michael Auli, David Grangier
Date: 2016-10-20 00:00:00
Tasks: Machine Translation, Sentence, Translation

Existing machine translation decoding algorithms generate translations in a
strictly monotonic fashion and never revisit previous decisions. As a result,
earlier mistakes cannot be corrected at a later stage. In this paper, we
present a translation scheme that starts from an initial guess and then makes
iterative improvements that may revisit previous decisions. We parameterize our
model as a convolutional neural network that predicts discrete substitutions to
an existing translation based on an attention mechanism over both the source
sentence as well as the current translation output. By making less than one
modification per sentence, we improve the output of a phrase-based translation
system by up to 0.4 BLEU on WMT15 German-English translation.
Sensitivity and Generalization in Neural Networks: an Empirical Study
Authors: Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein
Date: 2018-02-23 00:00:00
Proceeding: ICLR 2018 1
Tasks: Data Augmentation, image-classification, Image Classification, Sensitivity

In practice it is often found that large over-parameterized neural networks
generalize better than their smaller counterparts, an observation that appears
to conflict with classical notions of function complexity, which typically
favor smaller models. In this work, we investigate this tension between
complexity and generalization through an extensive empirical exploration of two
natural metrics of complexity related to sensitivity to input perturbations.
Our experiments survey thousands of models with various fully-connected
architectures, optimizers, and other hyper-parameters, as well as four
different image classification datasets.
  We find that trained neural networks are more robust to input perturbations
in the vicinity of the training data manifold, as measured by the norm of the
input-output Jacobian of the network, and that it correlates well with
generalization. We further establish that factors associated with poor
generalization $-$ such as full-batch training or using random labels $-$
correspond to lower robustness, while factors associated with good
generalization $-$ such as data augmentation and ReLU non-linearities $-$ give
rise to more robust functions. Finally, we demonstrate how the input-output
Jacobian norm can be predictive of generalization at the level of individual
test points.
Joint Event Detection and Description in Continuous Video Streams
Authors: Huijuan Xu, Boyang Li, Vasili Ramanishka, Leonid Sigal, Kate Saenko
Date: 2018-02-28 00:00:00
Tasks: Dense Captioning, Dense Video Captioning, Event Detection, Video Captioning, Video Understanding

Dense video captioning is a fine-grained video understanding task that
involves two sub-problems: localizing distinct events in a long video stream,
and generating captions for the localized events. We propose the Joint Event
Detection and Description Network (JEDDi-Net), which solves the dense video
captioning task in an end-to-end fashion. Our model continuously encodes the
input video stream with three-dimensional convolutional layers, proposes
variable-length temporal events based on pooled features, and generates their
captions. Proposal features are extracted within each proposal segment through
3D Segment-of-Interest pooling from shared video feature encoding. In order to
explicitly model temporal relationships between visual events and their
captions in a single video, we also propose a two-level hierarchical captioning
module that keeps track of context. On the large-scale ActivityNet Captions
dataset, JEDDi-Net demonstrates improved results as measured by standard
metrics. We also present the first dense captioning results on the
TACoS-MultiLevel dataset.
A Latent Gaussian Mixture Model for Clustering Longitudinal Data
Authors: Vanessa S. E. Bierling, Paul D. McNicholas
Date: 2018-04-13 00:00:00
Tasks: Clustering, Model Selection, parameter estimation

Finite mixture models have become a popular tool for clustering. Amongst
other uses, they have been applied for clustering longitudinal data and
clustering high-dimensional data. In the latter case, a latent Gaussian mixture
model is sometimes used. Although there has been much work on clustering using
latent variables and on clustering longitudinal data, respectively, there has
been a paucity of work that combines these features. An approach is developed
for clustering longitudinal data with many time points based on an extension of
the mixture of common factor analyzers model. A variation of the
expectation-maximization algorithm is used for parameter estimation and the
Bayesian information criterion is used for model selection. The approach is
illustrated using real and simulated data.
Towards Safe Autonomous Driving: Capture Uncertainty in the Deep Neural Network For Lidar 3D Vehicle Detection
Authors: Di Feng, Lars Rosenbaum, Klaus Dietmayer
Date: 2018-04-13 00:00:00
Tasks: Autonomous Driving, General Classification, Object, object-detection, Object Detection, vehicle detection

To assure that an autonomous car is driving safely on public roads, its
object detection module should not only work correctly, but show its prediction
confidence as well. Previous object detectors driven by deep learning do not
explicitly model uncertainties in the neural network. We tackle with this
problem by presenting practical methods to capture uncertainties in a 3D
vehicle detector for Lidar point clouds. The proposed probabilistic detector
represents reliable epistemic uncertainty and aleatoric uncertainty in
classification and localization tasks. Experimental results show that the
epistemic uncertainty is related to the detection accuracy, whereas the
aleatoric uncertainty is influenced by vehicle distance and occlusion. The
results also show that we can improve the detection performance by 1%-5% by
modeling the aleatoric uncertainty.
Sampling Requirements and Accelerated Schemes for Sparse Linear Regression with Orthogonal Least-Squares
Authors: Abolfazl Hashemi, Haris Vikalo
Date: 2016-08-08 00:00:00
Tasks: Clustering, regression

We study the problem of inferring a sparse vector from random linear
combinations of its components. We propose the Accelerated Orthogonal
Least-Squares (AOLS) algorithm that improves performance of the well-known
Orthogonal Least-Squares (OLS) algorithm while requiring significantly lower
computational costs. While OLS greedily selects columns of the coefficient
matrix that correspond to non-zero components of the sparse vector, AOLS
employs a novel computationally efficient procedure that speeds up the search
by anticipating future selections via choosing $L$ columns in each step, where
$L$ is an adjustable hyper-parameter. We analyze the performance of AOLS and
establish lower bounds on the probability of exact recovery for both noiseless
and noisy random linear measurements. In the noiseless scenario, it is shown
that when the coefficients are samples from a Gaussian distribution, AOLS with
high probability recovers a $k$-sparse $m$-dimensional sparse vector using
${\cal O}(k\log \frac{m}{k+L-1})$ measurements. Similar result is established
for the bounded-noise scenario where an additional condition on the smallest
nonzero element of the unknown vector is required. The asymptotic sampling
complexity of AOLS is lower than the asymptotic sampling complexity of the
existing sparse reconstruction algorithms. In simulations, AOLS is compared to
state-of-the-art sparse recovery techniques and shown to provide better
performance in terms of accuracy, running time, or both. Finally, we consider
an application of AOLS to clustering high-dimensional data lying on the union
of low-dimensional subspaces and demonstrate its superiority over existing
methods.
Multilevel Language and Vision Integration for Text-to-Clip Retrieval
Authors: Huijuan Xu, Kun He, Bryan A. Plummer, Leonid Sigal, Stan Sclaroff, Kate Saenko
Date: 2018-04-13 00:00:00
Tasks: Retrieval, Sentence

We address the problem of text-based activity retrieval in video. Given a
sentence describing an activity, our task is to retrieve matching clips from an
untrimmed video. To capture the inherent structures present in both text and
video, we introduce a multilevel model that integrates vision and language
features earlier and more tightly than prior work. First, we inject text
features early on when generating clip proposals, to help eliminate unlikely
clips and thus speed up processing and boost performance. Second, to learn a
fine-grained similarity metric for retrieval, we use visual features to
modulate the processing of query sentences at the word level in a recurrent
neural network. A multi-task loss is also employed by adding query
re-generation as an auxiliary task. Our approach significantly outperforms
prior work on two challenging benchmarks: Charades-STA and ActivityNet
Captions.
O-HAZE: a dehazing benchmark with real hazy and haze-free outdoor images
Authors: Codruta O. Ancuti, Cosmin Ancuti, Radu Timofte, Christophe De Vleeschouwer
Date: 2018-04-13 00:00:00
Tasks: SSIM

Haze removal or dehazing is a challenging ill-posed problem that has drawn a
significant attention in the last few years. Despite this growing interest, the
scientific community is still lacking a reference dataset to evaluate
objectively and quantitatively the performance of proposed dehazing methods.
The few datasets that are currently considered, both for assessment and
training of learning-based dehazing techniques, exclusively rely on synthetic
hazy images. To address this limitation, we introduce the first outdoor scenes
database (named O-HAZE) composed of pairs of real hazy and corresponding
haze-free images. In practice, hazy images have been captured in presence of
real haze, generated by professional haze machines, and OHAZE contains 45
different outdoor scenes depicting the same visual content recorded in
haze-free and hazy conditions, under the same illumination parameters. To
illustrate its usefulness, O-HAZE is used to compare a representative set of
state-of-the-art dehazing techniques, using traditional image quality metrics
such as PSNR, SSIM and CIEDE2000. This reveals the limitations of current
techniques, and questions some of their underlying assumptions.
Demo of Sanskrit-Hindi SMT System
Authors: Rajneesh Pandey, Atul Kr. Ojha, Girish Nath Jha
Date: 2018-04-13 00:00:00
Tasks: Machine Translation, Translation

The demo proposal presents a Phrase-based Sanskrit-Hindi (SaHiT) Statistical
Machine Translation system. The system has been developed on Moses. 43k
sentences of Sanskrit-Hindi parallel corpus and 56k sentences of a monolingual
corpus in the target language (Hindi) have been used. This system gives 57 BLEU
score.
Automatic Language Identification System for Hindi and Magahi
Authors: Priya Rani, Atul Kr. Ojha, Girish Nath Jha
Date: 2018-04-13 00:00:00
Tasks: Language Identification

Language identification has become a prerequisite for all kinds of automated
text processing systems. In this paper, we present a rule-based language
identifier tool for two closely related Indo-Aryan languages: Hindi and Magahi.
This system has currently achieved an accuracy of approx 86.34%. We hope to
improve this in the future. Automatic identification of languages will be
significant in the accuracy of output of Web Crawlers.
Heterogeneous Multilayer Generalized Operational Perceptron
Authors: Dat Thanh Tran, Serkan Kiranyaz, Moncef Gabbouj, Alexandros Iosifidis
Date: 2018-04-13 00:00:00

The traditional Multilayer Perceptron (MLP) using McCulloch-Pitts neuron
model is inherently limited to a set of neuronal activities, i.e., linear
weighted sum followed by nonlinear thresholding step. Previously, Generalized
Operational Perceptron (GOP) was proposed to extend conventional perceptron
model by defining a diverse set of neuronal activities to imitate a generalized
model of biological neurons. Together with GOP, Progressive Operational
Perceptron (POP) algorithm was proposed to optimize a pre-defined template of
multiple homogeneous layers in a layerwise manner. In this paper, we propose an
efficient algorithm to learn a compact, fully heterogeneous multilayer network
that allows each individual neuron, regardless of the layer, to have distinct
characteristics. Based on the complexity of the problem, the proposed algorithm
operates in a progressive manner on a neuronal level, searching for a compact
topology, not only in terms of depth but also width, i.e., the number of
neurons in each layer. The proposed algorithm is shown to outperform other
related learning methods in extensive experiments on several classification
problems.
Challenges and Characteristics of Intelligent Autonomy for Internet of Battle Things in Highly Adversarial Environments
Authors: Alexander Kott
Date: 2018-03-20 00:00:00
Tasks: BIG-bench Machine Learning

Numerous, artificially intelligent, networked things will populate the
battlefield of the future, operating in close collaboration with human
warfighters, and fighting as teams in highly adversarial environments. This
paper explores the characteristics, capabilities and intelligence required of
such a network of intelligent things and humans - Internet of Battle Things
(IOBT). It will experience unique challenges that are not yet well addressed by
the current generation of AI and machine learning.
A new robust feature selection method using variance-based sensitivity analysis
Authors: Saman Sadeghyan
Date: 2018-04-13 00:00:00
Tasks: BIG-bench Machine Learning, Computational Efficiency, Data Visualization, feature selection, Sensitivity

Excluding irrelevant features in a pattern recognition task plays an
important role in maintaining a simpler machine learning model and optimizing
the computational efficiency. Nowadays with the rise of large scale datasets,
feature selection is in great demand as it becomes a central issue when facing
high-dimensional datasets. The present study provides a new measure of saliency
for features by employing a Sensitivity Analysis (SA) technique called the
extended Fourier amplitude sensitivity test, and a well-trained Feedforward
Neural Network (FNN) model, which ultimately leads to the selection of a
promising optimal feature subset. Ideas of the paper are mainly demonstrated
based on adopting FNN model for feature selection in classification problems.
But in the end, a generalization framework is discussed in order to give
insights into the usage in regression problems as well as expressing how other
function approximate models can be deployed. Effectiveness of the proposed
method is verified by result analysis and data visualization for a series of
experiments over several well-known datasets drawn from UCI machine learning
repository.
Expected Policy Gradients
Authors: Kamil Ciosek, Shimon Whiteson
Date: 2017-06-15 00:00:00
Tasks: MuJoCo, Reinforcement Learning

We propose expected policy gradients (EPG), which unify stochastic policy
gradients (SPG) and deterministic policy gradients (DPG) for reinforcement
learning. Inspired by expected sarsa, EPG integrates across the action when
estimating the gradient, instead of relying only on the action in the sampled
trajectory. We establish a new general policy gradient theorem, of which the
stochastic and deterministic policy gradient theorems are special cases. We
also prove that EPG reduces the variance of the gradient estimates without
requiring deterministic policies and, for the Gaussian case, with no
computational overhead. Finally, we show that it is optimal in a certain sense
to explore with a Gaussian policy such that the covariance is proportional to
the exponential of the scaled Hessian of the critic with respect to the
actions. We present empirical results confirming that this new form of
exploration substantially outperforms DPG with the Ornstein-Uhlenbeck heuristic
in four challenging MuJoCo domains.
Functional Map of the World
Authors: Gordon Christie, Neil Fendley, James Wilson, Ryan Mukherjee
Date: 2017-11-21 00:00:00
Proceeding: CVPR 2018 6
Tasks: Temporal Sequences

We present a new dataset, Functional Map of the World (fMoW), which aims to
inspire the development of machine learning models capable of predicting the
functional purpose of buildings and land use from temporal sequences of
satellite images and a rich set of metadata features. The metadata provided
with each image enables reasoning about location, time, sun angles, physical
sizes, and other features when making predictions about objects in the image.
Our dataset consists of over 1 million images from over 200 countries. For each
image, we provide at least one bounding box annotation containing one of 63
categories, including a "false detection" category. We present an analysis of
the dataset along with baseline approaches that reason about metadata and
temporal views. Our data, code, and pretrained models have been made publicly
available.