pwc-archive/papers-with-abstracts huggingface dataset:
Entity-Aware Language Model as an Unsupervised Reranker
Authors: Mohammad Sadegh Rasooli, Sarangarajan Parthasarathy
Date: 2018-03-12 00:00:00
Tasks: Language Modeling, Language Modelling

In language modeling, it is difficult to incorporate entity relationships
from a knowledge-base. One solution is to use a reranker trained with global
features, in which global features are derived from n-best lists. However,
training such a reranker requires manually annotated n-best lists, which is
expensive to obtain. We propose a method based on the contrastive estimation
method that alleviates the need for such data. Experiments in the music domain
demonstrate that global features, as well as features extracted from an
external knowledge-base, can be incorporated into our reranker. Our final
model, a simple ensemble of a language model and reranker, achieves a 0.44\%
absolute word error rate improvement over an LSTM language model on the blind
test data.
Co-training Embeddings of Knowledge Graphs and Entity Descriptions for Cross-lingual Entity Alignment
Authors: Muhao Chen, Yingtao Tian, Kai-Wei Chang, Steven Skiena, Carlo Zaniolo
Date: 2018-06-18 00:00:00
Tasks: Entity Alignment, Knowledge Graphs

Multilingual knowledge graph (KG) embeddings provide latent semantic
representations of entities and structured knowledge with cross-lingual
inferences, which benefit various knowledge-driven cross-lingual NLP tasks.
However, precisely learning such cross-lingual inferences is usually hindered
by the low coverage of entity alignment in many KGs. Since many multilingual
KGs also provide literal descriptions of entities, in this paper, we introduce
an embedding-based approach which leverages a weakly aligned multilingual KG
for semi-supervised cross-lingual learning using entity descriptions. Our
approach performs co-training of two embedding models, i.e. a multilingual KG
embedding model and a multilingual literal description embedding model. The
models are trained on a large Wikipedia-based trilingual dataset where most
entity alignment is unknown to training. Experimental results show that the
performance of the proposed approach on the entity alignment task improves at
each iteration of co-training, and eventually reaches a stage at which it
significantly surpasses previous approaches. We also show that our approach has
promising abilities for zero-shot entity alignment, and cross-lingual KG
completion.
Video Salient Object Detection Using Spatiotemporal Deep Features
Authors: Trung-Nghia Le, Akihiro Sugimoto
Date: 2017-08-04 00:00:00
Tasks: Object, object-detection, Object Detection, RGB Salient Object Detection, Salient Object Detection, Semantic Segmentation, Video Object Segmentation, Video Salient Object Detection, Video Semantic Segmentation

This paper presents a method for detecting salient objects in videos where
temporal information in addition to spatial information is fully taken into
account. Following recent reports on the advantage of deep features over
conventional hand-crafted features, we propose a new set of SpatioTemporal Deep
(STD) features that utilize local and global contexts over frames. We also
propose new SpatioTemporal Conditional Random Field (STCRF) to compute saliency
from STD features. STCRF is our extension of CRF to the temporal domain and
describes the relationships among neighboring regions both in a frame and over
frames. STCRF leads to temporally consistent saliency maps over frames,
contributing to the accurate detection of salient objects' boundaries and noise
reduction during detection. Our proposed method first segments an input video
into multiple scales and then computes a saliency map at each scale level using
STD features with STCRF. The final saliency map is computed by fusing saliency
maps at different scale levels. Our experiments, using publicly available
benchmark datasets, confirm that the proposed method significantly outperforms
state-of-the-art methods. We also applied our saliency computation to the video
object segmentation task, showing that our method outperforms existing video
object segmentation methods.
Reinforcement Learning in Rich-Observation MDPs using Spectral Methods
Authors: Kamyar Azizzadenesheli, Alessandro Lazaric, Animashree Anandkumar
Date: 2016-11-11 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) in Markov decision processes (MDPs) with large
state spaces is a challenging problem. The performance of standard RL
algorithms degrades drastically with the dimensionality of state space.
However, in practice, these large MDPs typically incorporate a latent or hidden
low-dimensional structure. In this paper, we study the setting of
rich-observation Markov decision processes (ROMDP), where there are a small
number of hidden states which possess an injective mapping to the observation
states. In other words, every observation state is generated through a single
hidden state, and this mapping is unknown a priori. We introduce a spectral
decomposition method that consistently learns this mapping, and more
importantly, achieves it with low regret. The estimated mapping is integrated
into an optimistic RL algorithm (UCRL), which operates on the estimated hidden
space. We derive finite-time regret bounds for our algorithm with a weak
dependence on the dimensionality of the observed space. In fact, our algorithm
asymptotically achieves the same average regret as the oracle UCRL algorithm,
which has the knowledge of the mapping from hidden to observed spaces. Thus, we
derive an efficient spectral RL algorithm for ROMDPs.
Breaking Transferability of Adversarial Samples with Randomness
Authors: Yan Zhou, Murat Kantarcioglu, Bowei Xi
Date: 2018-05-11 00:00:00

We investigate the role of transferability of adversarial attacks in the
observed vulnerabilities of Deep Neural Networks (DNNs). We demonstrate that
introducing randomness to the DNN models is sufficient to defeat adversarial
attacks, given that the adversary does not have an unlimited attack budget.
Instead of making one specific DNN model robust to perfect knowledge attacks
(a.k.a, white box attacks), creating randomness within an army of DNNs
completely eliminates the possibility of perfect knowledge acquisition,
resulting in a significantly more robust DNN ensemble against the strongest
form of attacks. We also show that when the adversary has an unlimited budget
of data perturbation, all defensive techniques would eventually break down as
the budget increases. Therefore, it is important to understand the game saddle
point where the adversary would not further pursue this endeavor.
  Furthermore, we explore the relationship between attack severity and decision
boundary robustness in the version space. We empirically demonstrate that by
simply adding a small Gaussian random noise to the learned weights, a DNN model
can increase its resilience to adversarial attacks by as much as 74.2%. More
importantly, we show that by randomly activating/revealing a model from a pool
of pre-trained DNNs at each query request, we can put a tremendous strain on
the adversary's attack strategies. We compare our randomization techniques to
the Ensemble Adversarial Training technique and show that our randomization
techniques are superior under different attack budget constraints.
The RBO Dataset of Articulated Objects and Interactions
Authors: Roberto Martín-Martín, Clemens Eppner, Oliver Brock
Date: 2018-06-17 00:00:00

We present a dataset with models of 14 articulated objects commonly found in
human environments and with RGB-D video sequences and wrenches recorded of
human interactions with them. The 358 interaction sequences total 67 minutes of
human manipulation under varying experimental conditions (type of interaction,
lighting, perspective, and background). Each interaction with an object is
annotated with the ground truth poses of its rigid parts and the kinematic
state obtained by a motion capture system. For a subset of 78 sequences (25
minutes), we also measured the interaction wrenches. The object models contain
textured three-dimensional triangle meshes of each link and their motion
constraints. We provide Python scripts to download and visualize the data. The
data is available at https://tu-rbo.github.io/articulated-objects/ and hosted
at https://zenodo.org/record/1036660/.
Learning Policy Representations in Multiagent Systems
Authors: Aditya Grover, Maruan Al-Shedivat, Jayesh K. Gupta, Yura Burda, Harrison Edwards
Date: 2018-06-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Clustering, continuous-control, Continuous Control, Deep Reinforcement Learning, Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

Modeling agent behavior is central to understanding the emergence of complex
phenomena in multiagent systems. Prior work in agent modeling has largely been
task-specific and driven by hand-engineering domain-specific prior knowledge.
We propose a general learning framework for modeling agent behavior in any
multiagent system using only a handful of interaction data. Our framework casts
agent modeling as a representation learning problem. Consequently, we construct
a novel objective inspired by imitation learning and agent identification and
design an algorithm for unsupervised learning of representations of agent
policies. We demonstrate empirically the utility of the proposed framework in
(i) a challenging high-dimensional competitive environment for continuous
control and (ii) a cooperative environment for communication, on supervised
predictive tasks, unsupervised clustering, and policy optimization using deep
reinforcement learning.
Sub-Gaussian estimators of the mean of a random matrix with heavy-tailed entries
Authors: Stanislav Minsker
Date: 2016-05-23 00:00:00
Tasks: Matrix Completion

Estimation of the covariance matrix has attracted a lot of attention of the
statistical research community over the years, partially due to important
applications such as Principal Component Analysis. However, frequently used
empirical covariance estimator (and its modifications) is very sensitive to
outliers in the data. As P. J. Huber wrote in 1964, "...This raises a question
which could have been asked already by Gauss, but which was, as far as I know,
only raised a few years ago (notably by Tukey): what happens if the true
distribution deviates slightly from the assumed normal one? As is now well
known, the sample mean then may have a catastrophically bad performance..."
Motivated by this question, we develop a new estimator of the (element-wise)
mean of a random matrix, which includes covariance estimation problem as a
special case. Assuming that the entries of a matrix possess only finite second
moment, this new estimator admits sub-Gaussian or sub-exponential concentration
around the unknown mean in the operator norm. We will explain the key ideas
behind our construction, as well as applications to covariance estimation and
matrix completion problems.
Fast Convex Pruning of Deep Neural Networks
Authors: Alireza Aghasi, Afshin Abdi, Justin Romberg
Date: 2018-06-17 00:00:00
Tasks: Network Pruning

We develop a fast, tractable technique called Net-Trim for simplifying a
trained neural network. The method is a convex post-processing module, which
prunes (sparsifies) a trained network layer by layer, while preserving the
internal responses. We present a comprehensive analysis of Net-Trim from both
the algorithmic and sample complexity standpoints, centered on a fast, scalable
convex optimization program. Our analysis includes consistency results between
the initial and retrained models before and after Net-Trim application and
guarantees on the number of training samples needed to discover a network that
can be expressed using a certain number of nonzero terms. Specifically, if
there is a set of weights that uses at most $s$ terms that can re-create the
layer outputs from the layer inputs, we can find these weights from
$\mathcal{O}(s\log N/s)$ samples, where $N$ is the input size. These
theoretical results are similar to those for sparse regression using the Lasso,
and our analysis uses some of the same recently-developed tools (namely recent
results on the concentration of measure and convex analysis). Finally, we
propose an algorithmic framework based on the alternating direction method of
multipliers (ADMM), which allows a fast and simple implementation of Net-Trim
for network pruning and compression.
Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size
Authors: Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Jerry L. Prince, Nobuhiko Sugano, Yoshinobu Sato
Date: 2018-03-18 00:00:00
Tasks: Image Generation

CT is commonly used in orthopedic procedures. MRI is used along with CT to
identify muscle structures and diagnose osteonecrosis due to its superior soft
tissue contrast. However, MRI has poor contrast for bone structures. Clearly,
it would be helpful if a corresponding CT were available, as bone boundaries
are more clearly seen and CT has standardized (i.e., Hounsfield) units.
Therefore, we aim at MR-to-CT synthesis. The CycleGAN was successfully applied
to unpaired CT and MR images of the head, these images do not have as much
variation of intensity pairs as do images in the pelvic region due to the
presence of joints and muscles. In this paper, we extended the CycleGAN
approach by adding the gradient consistency loss to improve the accuracy at the
boundaries. We conducted two experiments. To evaluate image synthesis, we
investigated dependency of image synthesis accuracy on 1) the number of
training data and 2) the gradient consistency loss. To demonstrate the
applicability of our method, we also investigated a segmentation accuracy on
synthesized images.
Self-Attentive Neural Collaborative Filtering
Authors: Yi Tay, Shuai Zhang, Luu Anh Tuan, Siu Cheung Hui
Date: 2018-06-17 00:00:00
Tasks: Collaborative Filtering

This paper has been withdrawn as we discovered a bug in our tensorflow
implementation that involved accidental mixing of vectors across batches. This
lead to different inference results given different batch sizes which is
completely strange. The performance scores still remain the same but we
concluded that it was not the self-attention that contributed to the
performance. We are withdrawing the paper because this renders the main claim
of the paper false. Thanks to Guan Xinyu from NUS for discovering this issue in
our previously open source code.
NCRF++: An Open-source Neural Sequence Labeling Toolkit
Authors: Jie Yang, Yue Zhang
Date: 2018-06-14 00:00:00
Proceeding: ACL 2018 7
Tasks: Chunking, GPU, Named Entity Recognition (NER), Part-Of-Speech Tagging

This paper describes NCRF++, a toolkit for neural sequence labeling. NCRF++
is designed for quick implementation of different neural sequence labeling
models with a CRF inference layer. It provides users with an inference for
building the custom model structure through configuration file with flexible
neural feature design and utilization. Built on PyTorch, the core operations
are calculated in batch, making the toolkit efficient with the acceleration of
GPU. It also includes the implementations of most state-of-the-art neural
sequence labeling models such as LSTM-CRF, facilitating reproducing and
refinement on those methods.
Online Prediction of Switching Graph Labelings with Cluster Specialists
Authors: Mark Herbster, James Robinson
Date: 2018-06-17 00:00:00
Proceeding: NeurIPS 2019 12

We address the problem of predicting the labeling of a graph in an online setting when the labeling is changing over time. We present an algorithm based on a specialist approach; we develop the machinery of cluster specialists which probabilistically exploits the cluster structure in the graph. Our algorithm has two variants, one of which surprisingly only requires $\mathcal{O}(\log n)$ time on any trial $t$ on an $n$-vertex graph, an exponential speed up over existing methods. We prove switching mistake-bound guarantees for both variants of our algorithm. Furthermore these mistake bounds smoothly vary with the magnitude of the change between successive labelings. We perform experiments on Chicago Divvy Bicycle Sharing data and show that our algorithms significantly outperform an existing algorithm (a kernelized Perceptron) as well as several natural benchmarks.
Compressed Sensing with Deep Image Prior and Learned Regularization
Authors: Dave Van Veen, Ajil Jalal, Mahdi Soltanolkotabi, Eric Price, Sriram Vishwanath, Alexandros G. Dimakis
Date: 2018-06-17 00:00:00
Tasks: compressed sensing

We propose a novel method for compressed sensing recovery using untrained deep generative models. Our method is based on the recently proposed Deep Image Prior (DIP), wherein the convolutional weights of the network are optimized to match the observed measurements. We show that this approach can be applied to solve any differentiable linear inverse problem, outperforming previous unlearned methods. Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets. We further introduce a novel learned regularization technique, which incorporates prior information on the network weights. This reduces reconstruction error, especially for noisy measurements. Finally, we prove that, using the DIP optimization approach, moderately overparameterized single-layer networks can perfectly fit any signal despite the non-convex nature of the fitting problem. This theoretical result provides justification for early stopping.
Subspace Embedding and Linear Regression with Orlicz Norm
Authors: Alexandr Andoni, Chengyu Lin, Ying Sheng, Peilin Zhong, Ruiqi Zhong
Date: 2018-06-17 00:00:00
Proceeding: ICML 2018 7
Tasks: regression

We consider a generalization of the classic linear regression problem to the
case when the loss is an Orlicz norm. An Orlicz norm is parameterized by a
non-negative convex function $G:\mathbb{R}_+\rightarrow\mathbb{R}_+$ with
$G(0)=0$: the Orlicz norm of a vector $x\in\mathbb{R}^n$ is defined as $
\|x\|_G=\inf\left\{\alpha>0\large\mid\sum_{i=1}^n G(|x_i|/\alpha)\leq
1\right\}. $ We consider the cases where the function $G(\cdot)$ grows
subquadratically. Our main result is based on a new oblivious embedding which
embeds the column space of a given matrix $A\in\mathbb{R}^{n\times d}$ with
Orlicz norm into a lower dimensional space with $\ell_2$ norm. Specifically, we
show how to efficiently find an embedding matrix $S\in\mathbb{R}^{m\times
n},m<n$ such that $\forall x\in\mathbb{R}^{d},\Omega(1/(d\log n)) \cdot
\|Ax\|_G\leq \|SAx\|_2\leq O(d^2\log n) \cdot \|Ax\|_G.$ By applying this
subspace embedding technique, we show an approximation algorithm for the
regression problem $\min_{x\in\mathbb{R}^d} \|Ax-b\|_G$, up to a $O(d\log^2 n)$
factor. As a further application of our techniques, we show how to also use
them to improve on the algorithm for the $\ell_p$ low rank matrix approximation
problem for $1\leq p<2$.
Scalable Methods for 8-bit Training of Neural Networks
Authors: Ron Banner, Itay Hubara, Elad Hoffer, Daniel Soudry
Date: 2018-05-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Quantization

Quantized Neural Networks (QNNs) are often used to improve network efficiency
during the inference phase, i.e. after the network has been trained. Extensive
research in the field suggests many different quantization schemes. Still, the
number of bits required, as well as the best quantization scheme, are yet
unknown. Our theoretical analysis suggests that most of the training process is
robust to substantial precision reduction, and points to only a few specific
operations that require higher precision. Armed with this knowledge, we
quantize the model parameters, activations and layer gradients to 8-bit,
leaving at a higher precision only the final step in the computation of the
weight gradients. Additionally, as QNNs require batch-normalization to be
trained at high precision, we introduce Range Batch-Normalization (BN) which
has significantly higher tolerance to quantization noise and improved
computational complexity. Our simulations show that Range BN is equivalent to
the traditional batch norm if a precise scale adjustment, which can be
approximated analytically, is applied. To the best of the authors' knowledge,
this work is the first to quantize the weights, activations, as well as a
substantial volume of the gradients stream, in all layers (including batch
normalization) to 8-bit while showing state-of-the-art results over the
ImageNet-1K dataset.
A Novel Hybrid Machine Learning Model for Auto-Classification of Retinal Diseases
Authors: C. -H. Huck Yang, Jia-Hong Huang, Fangyu Liu, Fang-Yi Chiu, Mengya Gao, Weifeng Lyu, I-Hung Lin M. D., Jesper Tegner
Date: 2018-06-17 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Hybrid Machine Learning

Automatic clinical diagnosis of retinal diseases has emerged as a promising
approach to facilitate discovery in areas with limited access to specialists.
We propose a novel visual-assisted diagnosis hybrid model based on the support
vector machine (SVM) and deep neural networks (DNNs). The model incorporates
complementary strengths of DNNs and SVM. Furthermore, we present a new clinical
retina label collection for ophthalmology incorporating 32 retina diseases
classes. Using EyeNet, our model achieves 89.73% diagnosis accuracy and the
model performance is comparable to the professional ophthalmologists.
Learning to Evaluate Image Captioning
Authors: Yin Cui, Guandao Yang, Andreas Veit, Xun Huang, Serge Belongie
Date: 2018-06-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: 8k, Data Augmentation, Image Captioning, Sentence

Evaluation metrics for image captioning face two challenges. Firstly,
commonly used metrics such as CIDEr, METEOR, ROUGE and BLEU often do not
correlate well with human judgments. Secondly, each metric has well known blind
spots to pathological caption constructions, and rule-based metrics lack
provisions to repair such blind spots once identified. For example, the newly
proposed SPICE correlates well with human judgments, but fails to capture the
syntactic structure of a sentence. To address these two challenges, we propose
a novel learning based discriminative evaluation metric that is directly
trained to distinguish between human and machine-generated captions. In
addition, we further propose a data augmentation scheme to explicitly
incorporate pathological transformations as negative examples during training.
The proposed metric is evaluated with three kinds of robustness tests and its
correlation with human judgments. Extensive experiments show that the proposed
data augmentation scheme not only makes our metric more robust toward several
pathological transformations, but also improves its correlation with human
judgments. Our metric outperforms other metrics on both caption level human
correlation in Flickr 8k and system level human correlation in COCO. The
proposed approach could be served as a learning based evaluation metric that is
complementary to existing rule-based metrics.