pwc-archive/papers-with-abstracts huggingface dataset:
Relational Long Short-Term Memory for Video Action Recognition
Authors: Zexi Chen, Bharathkumar Ramachandra, Tianfu Wu, Ranga Raju Vatsavai
Date: 2018-11-16 00:00:00
Tasks: Action Recognition, Temporal Action Localization

Spatial and temporal relationships, both short-range and long-range, between objects in videos, are key cues for recognizing actions. It is a challenging problem to model them jointly. In this paper, we first present a new variant of Long Short-Term Memory, namely Relational LSTM, to address the challenge of relation reasoning across space and time between objects. In our Relational LSTM module, we utilize a non-local operation similar in spirit to the recently proposed non-local network to substitute the fully connected operation in the vanilla LSTM. By doing this, our Relational LSTM is capable of capturing long and short-range spatio-temporal relations between objects in videos in a principled way. Then, we propose a two-branch neural architecture consisting of the Relational LSTM module as the non-local branch and a spatio-temporal pooling based local branch. The local branch is utilized for capturing local spatial appearance and/or short-term motion features. The two branches are concatenated to learn video-level features from snippet-level ones which are then used for classification. Experimental results on UCF-101 and HMDB-51 datasets show that our model achieves state-of-the-art results among LSTM-based methods, while obtaining comparable performance with other state-of-the-art methods (which use not directly comparable schema). Further, on the more complex large-scale Charades dataset, we obtain a large 3.2% gain over state-of-the-art methods, verifying the effectiveness of our method in complex understanding.
Domain Adaptive Transfer Learning with Specialist Models
Authors: Jiquan Ngiam, Daiyi Peng, Vijay Vasudevan, Simon Kornblith, Quoc V. Le, Ruoming Pang
Date: 2018-11-16 00:00:00
Tasks: Domain Adaptation, Fine-Grained Image Classification, General Classification, Transfer Learning

Transfer learning is a widely used method to build high performing computer
vision models. In this paper, we study the efficacy of transfer learning by
examining how the choice of data impacts performance. We find that more
pre-training data does not always help, and transfer performance depends on a
judicious choice of pre-training data. These findings are important given the
continued increase in dataset sizes. We further propose domain adaptive
transfer learning, a simple and effective pre-training method using importance
weights computed based on the target dataset. Our method to compute importance
weights follow from ideas in domain adaptation, and we show a novel application
to transfer learning. Our methods achieve state-of-the-art results on multiple
fine-grained classification datasets and are well-suited for use in practice.
Minimum weight norm models do not always generalize well for over-parameterized problems
Authors: Vatsal Shah, Anastasios Kyrillidis, Sujay Sanghavi
Date: 2018-11-16 00:00:00

This work is substituted by the paper in arXiv:2011.14066. Stochastic gradient descent is the de facto algorithm for training deep neural networks (DNNs). Despite its popularity, it still requires fine tuning in order to achieve its best performance. This has led to the development of adaptive methods, that claim automatic hyper-parameter optimization. Recently, researchers have studied both algorithmic classes via toy examples: e.g., for over-parameterized linear regression, Wilson et. al. (2017) shows that, while SGD always converges to the minimum-norm solution, adaptive methods show no such inclination, leading to worse generalization capabilities. Our aim is to study this conjecture further. We empirically show that the minimum weight norm is not necessarily the proper gauge of good generalization in simplified scenaria, and different models found by adaptive methods could outperform plain gradient methods. In practical DNN settings, we observe that adaptive methods can outperform SGD, with larger weight norm output models, but without necessarily reducing the amount of tuning required.
Synergistic Drug Combination Prediction by Integrating Multi-omics Data in Deep Learning Models
Authors: Tianyu Zhang, Liwei Zhang, Philip R. O. Payne, Fuhai Li
Date: 2018-11-16 00:00:00

Drug resistance is still a major challenge in cancer therapy. Drug
combination is expected to overcome drug resistance. However, the number of
possible drug combinations is enormous, and thus it is infeasible to
experimentally screen all effective drug combinations considering the limited
resources. Therefore, computational models to predict and prioritize effective
drug combinations is important for combinatory therapy discovery in cancer. In
this study, we proposed a novel deep learning model, AuDNNsynergy, to
prediction drug combinations by integrating multi-omics data and chemical
structure data. In specific, three autoencoders were trained using the gene
expression, copy number and genetic mutation data of all tumor samples from The
Cancer Genome Atlas. Then the physicochemical properties of drugs combined with
the output of the three autoencoders, characterizing the individual cancer
cell-lines, were used as the input of a deep neural network that predicts the
synergy value of given pair-wise drug combinations against the specific cancer
cell-lines. The comparison results showed the proposed AuDNNsynergy model
outperforms four state-of-art approaches, namely DeepSynergy, Gradient Boosting
Machines, Random Forests, and Elastic Nets. Moreover, we conducted the
interpretation analysis of the deep learning model to investigate potential
vital genetic predictors and the underlying mechanism of synergistic drug
combinations on specific cancer cell-lines.
Symmetry constrained machine learning
Authors: Doron L. Bergman
Date: 2018-11-16 00:00:00
Tasks: BIG-bench Machine Learning

Symmetry, a central concept in understanding the laws of nature, has been used for centuries in physics, mathematics, and chemistry, to help make mathematical models tractable. Yet, despite its power, symmetry has not been used extensively in machine learning, until rather recently. In this article we show a general way to incorporate symmetries into machine learning models. We demonstrate this with a detailed analysis on a rather simple real world machine learning system - a neural network for classifying handwritten digits, lacking bias terms for every neuron. We demonstrate that ignoring symmetries can have dire over-fitting consequences, and that incorporating symmetry into the model reduces over-fitting, while at the same time reducing complexity, ultimately requiring less training data, and taking less time and resources to train.
Gaussian Process Accelerated Feldman-Cousins Approach for Physical Parameter Inference
Authors: Lingge Li, Nitish Nayak, Jianming Bian, Pierre Baldi
Date: 2018-11-16 00:00:00
Tasks: Gaussian Processes

The unified approach of Feldman and Cousins allows for exact statistical inference of small signals that commonly arise in high energy physics. It has gained widespread use, for instance, in measurements of neutrino oscillation parameters in long-baseline experiments. However, the approach relies on the Neyman construction of the classical confidence interval and is computationally intensive as it is typically done in a grid-based fashion over the entire parameter space. In this letter, we propose an efficient algorithm for the Feldman-Cousins approach using Gaussian processes to construct confidence intervals iteratively. We show that in the neutrino oscillation context, one can obtain confidence intervals 5 times faster in one dimension and 10 times faster in two dimensions, while maintaining an accuracy above 99.5%.
BLeSS: Bio-inspired Low-level Spatiochromatic Similarity Assisted Image Quality Assessment
Authors: Dogancan Temel, Ghassan AlRegib
Date: 2018-11-16 00:00:00
Tasks: Image Quality Assessment

This paper proposes a biologically-inspired low-level
spatiochromatic-model-based similarity method (BLeSS) to assist full-reference
image-quality estimators that originally oversimplify color perception
processes. More specifically, the spatiochromatic model is based on spatial
frequency, spatial orientation, and surround contrast effects. The assistant
similarity method is used to complement image-quality estimators based on phase
congruency, gradient magnitude, and spectral residual. The effectiveness of
BLeSS is validated using FSIM, FSIMc and SR-SIM methods on LIVE, Multiply
Distorted LIVE, and TID 2013 databases. In terms of Spearman correlation, BLeSS
enhances the performance of all quality estimators in color-based degradations
and the enhancement is at 100% for both feature- and spectral residual-based
similarity methods. Moreover, BleSS significantly enhances the performance of
SR-SIM and FSIM in the full TID 2013 database.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
Authors: Yixin Nie, Haonan Chen, Mohit Bansal
Date: 2018-11-16 00:00:00
Tasks: Claim Verification, Fact Checking, Fact Verification, Misinformation, Natural Language Inference, Retrieval, Sentence

The increasing concern with misinformation has stimulated research efforts on
automatic fact checking. The recently-released FEVER dataset introduced a
benchmark fact-verification task in which a system is asked to verify a claim
using evidential sentences from Wikipedia documents. In this paper, we present
a connected system consisting of three homogeneous neural semantic matching
models that conduct document retrieval, sentence selection, and claim
verification jointly for fact extraction and verification. For evidence
retrieval (document retrieval and sentence selection), unlike traditional
vector space IR models in which queries and sources are matched in some
pre-designed term vector space, we develop neural models to perform deep
semantic matching from raw textual input, assuming no intermediate term
representation and no access to structured external knowledge bases. We also
show that Pageview frequency can also help improve the performance of evidence
retrieval results, that later can be matched by using our neural semantic
matching network. For claim verification, unlike previous approaches that
simply feed upstream retrieved evidence and the claim to a natural language
inference (NLI) model, we further enhance the NLI model by providing it with
internal semantic relatedness scores (hence integrating it with the evidence
retrieval modules) and ontological WordNet features. Experiments on the FEVER
dataset indicate that (1) our neural semantic matching method outperforms
popular TF-IDF and encoder models, by significant margins on all evidence
retrieval metrics, (2) the additional relatedness score and WordNet features
improve the NLI model via better semantic awareness, and (3) by formalizing all
three subtasks as a similar semantic matching problem and improving on all
three stages, the complete model is able to achieve the state-of-the-art
results on the FEVER test set.
Analyzing Compositionality-Sensitivity of NLI Models
Authors: Yixin Nie, Yicheng Wang, Mohit Bansal
Date: 2018-11-16 00:00:00
Tasks: Diagnostic, Natural Language Inference, Sensitivity

Success in natural language inference (NLI) should require a model to
understand both lexical and compositional semantics. However, through
adversarial evaluation, we find that several state-of-the-art models with
diverse architectures are over-relying on the former and fail to use the
latter. Further, this compositionality unawareness is not reflected via
standard evaluation on current datasets. We show that removing RNNs in existing
models or shuffling input words during training does not induce large
performance loss despite the explicit removal of compositional information.
Therefore, we propose a compositionality-sensitivity testing setup that
analyzes models on natural examples from existing datasets that cannot be
solved via lexical features alone (i.e., on which a bag-of-words model gives a
high probability to one wrong label), hence revealing the models' actual
compositionality awareness. We show that this setup not only highlights the
limited compositional ability of current NLI models, but also differentiates
model performance based on design, e.g., separating shallow bag-of-words models
from deeper, linguistically-grounded tree-based models. Our evaluation setup is
an important analysis tool: complementing currently existing adversarial and
linguistically driven diagnostic evaluations, and exposing opportunities for
future work on evaluating models' compositional understanding.
Mining Entity Synonyms with Efficient Neural Set Generation
Authors: Jiaming Shen, Ruiliang Lyu, Xiang Ren, Michelle Vanni, Brian Sadler, Jiawei Han
Date: 2018-11-16 00:00:00

Mining entity synonym sets (i.e., sets of terms referring to the same entity)
is an important task for many entity-leveraging applications. Previous work
either rank terms based on their similarity to a given query term, or treats
the problem as a two-phase task (i.e., detecting synonymy pairs, followed by
organizing these pairs into synonym sets). However, these approaches fail to
model the holistic semantics of a set and suffer from the error propagation
issue. Here we propose a new framework, named SynSetMine, that efficiently
generates entity synonym sets from a given vocabulary, using example sets from
external knowledge bases as distant supervision. SynSetMine consists of two
novel modules: (1) a set-instance classifier that jointly learns how to
represent a permutation invariant synonym set and whether to include a new
instance (i.e., a term) into the set, and (2) a set generation algorithm that
enumerates the vocabulary only once and applies the learned set-instance
classifier to detect all entity synonym sets in it. Experiments on three real
datasets from different domains demonstrate both effectiveness and efficiency
of SynSetMine for mining entity synonym sets.
Improving Rotated Text Detection with Rotation Region Proposal Networks
Authors: Jing Huang, Viswanath Sivakumar, Mher Mnatsakanyan, Guan Pang
Date: 2018-11-16 00:00:00
Tasks: Misinformation, Region Proposal, Text Detection

A significant number of images shared on social media platforms such as
Facebook and Instagram contain text in various forms. It's increasingly
becoming commonplace for bad actors to share misinformation, hate speech or
other kinds of harmful content as text overlaid on images on such platforms. A
scene-text understanding system should hence be able to handle text in various
orientations that the adversary might use. Moreover, such a system can be
incorporated into screen readers used to aid the visually impaired. In this
work, we extend the scene-text extraction system at Facebook, Rosetta, to
efficiently handle text in various orientations. Specifically, we incorporate
the Rotation Region Proposal Networks (RRPN) in our text extraction pipeline
and offer practical suggestions for building and deploying a model for
detecting and recognizing text in arbitrary orientations efficiently.
Experimental results show a significant improvement on detecting rotated text.
Investigating the Effects of Word Substitution Errors on Sentence Embeddings
Authors: Rohit Voleti, Julie M. Liss, Visar Berisha
Date: 2018-11-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Semantic Similarity, Semantic Textual Similarity, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, speech-recognition, Speech Recognition

A key initial step in several natural language processing (NLP) tasks
involves embedding phrases of text to vectors of real numbers that preserve
semantic meaning. To that end, several methods have been recently proposed with
impressive results on semantic similarity tasks. However, all of these
approaches assume that perfect transcripts are available when generating the
embeddings. While this is a reasonable assumption for analysis of written text,
it is limiting for analysis of transcribed text. In this paper we investigate
the effects of word substitution errors, such as those coming from automatic
speech recognition errors (ASR), on several state-of-the-art sentence embedding
methods. To do this, we propose a new simulator that allows the experimenter to
induce ASR-plausible word substitution errors in a corpus at a desired word
error rate. We use this simulator to evaluate the robustness of several
sentence embedding methods. Our results show that pre-trained neural sentence
encoders are both robust to ASR errors and perform well on textual similarity
tasks after errors are introduced. Meanwhile, unweighted averages of word
vectors perform well with perfect transcriptions, but their performance
degrades rapidly on textual similarity tasks for text with word substitution
errors.
Brain Connectivity Impairments and Categorization Disabilities in Autism: A Theoretical Approach via Artificial Neural Networks
Authors: Daniele Q. M. Madureira, Vera Lucia P. S. Caminha, Rogerio Salvini
Date: 2018-11-16 00:00:00

A developmental disorder that severely damages communicative and social
functions, the Autism Spectrum Disorder (ASD) also presents aspects related to
mental rigidity, repetitive behavior, and difficulty in abstract reasoning.
More, imbalances between excitatory and inhibitory brain states, in addition to
cortical connectivity disruptions, are at the source of the autistic behavior.
Our main goal consists in unveiling the way by which these local excitatory
imbalances and/or long brain connections disruptions are linked to the above
mentioned cognitive features. We developed a theoretical model based on
Self-Organizing Maps (SOM), where a three-level artificial neural network
qualitatively incorporates these kinds of alterations observed in brains of
patients with ASD. Computational simulations of our model indicate that high
excitatory states or long distance under-connectivity are at the origins of
cognitive alterations, as difficulty in categorization and mental rigidity.
More specifically, the enlargement of excitatory synaptic reach areas in a
cortical map development conducts to low categorization (over-selectivity) and
poor concepts formation. And, both the over-strengthening of local excitatory
synapses and the long distance under-connectivity, although through distinct
mechanisms, contribute to impaired categorization (under-selectivity) and
mental rigidity. Our results indicate how, together, both local and global
brain connectivity alterations give rise to spoiled cortical structures in
distinct ways and in distinct cortical areas. These alterations would disrupt
the codification of sensory stimuli, the representation of concepts and, thus,
the process of categorization - by this way imposing serious limits to the
mental flexibility and to the capacity of generalization in the autistic
reasoning.
Towards Training Recurrent Neural Networks for Lifelong Learning
Authors: Shagun Sodhani, Sarath Chandar, Yoshua Bengio
Date: 2018-11-16 00:00:00
Tasks: Lifelong learning

Catastrophic forgetting and capacity saturation are the central challenges of any parametric lifelong learning system. In this work, we study these challenges in the context of sequential supervised learning with an emphasis on recurrent neural networks. To evaluate the models in the lifelong learning setting, we propose a curriculum-based, simple, and intuitive benchmark where the models are trained on tasks with increasing levels of difficulty. To measure the impact of catastrophic forgetting, the model is tested on all the previous tasks as it completes any task. As a step towards developing true lifelong learning systems, we unify Gradient Episodic Memory (a catastrophic forgetting alleviation approach) and Net2Net(a capacity expansion approach). Both these models are proposed in the context of feedforward networks and we evaluate the feasibility of using them for recurrent networks. Evaluation on the proposed benchmark shows that the unified model is more suitable than the constituent models for lifelong learning setting.
Topology-Aware Non-Rigid Point Cloud Registration
Authors: Konstantinos Zampogiannis, Cornelia Fermuller, Yiannis Aloimonos
Date: 2018-11-16 00:00:00
Tasks: Dynamic Reconstruction, Event Detection, Motion Estimation, Point Cloud Registration

In this paper, we introduce a non-rigid registration pipeline for pairs of unorganized point clouds that may be topologically different. Standard warp field estimation algorithms, even under robust, discontinuity-preserving regularization, tend to produce erratic motion estimates on boundaries associated with `close-to-open' topology changes. We overcome this limitation by exploiting backward motion: in the opposite motion direction, a `close-to-open' event becomes `open-to-close', which is by default handled correctly. At the core of our approach lies a general, topology-agnostic warp field estimation algorithm, similar to those employed in recently introduced dynamic reconstruction systems from RGB-D input. We improve motion estimation on boundaries associated with topology changes in an efficient post-processing phase. Based on both forward and (inverted) backward warp hypotheses, we explicitly detect regions of the deformed geometry that undergo topological changes by means of local deformation criteria and broadly classify them as `contacts' or `separations'. Subsequently, the two motion hypotheses are seamlessly blended on a local basis, according to the type and proximity of detected events. Our method achieves state-of-the-art motion estimation accuracy on the MPI Sintel dataset. Experiments on a custom dataset with topological event annotations demonstrate the effectiveness of our pipeline in estimating motion on event boundaries, as well as promising performance in explicit topological event detection.
Coupling weak and strong supervision for classification of prostate cancer histopathology images
Authors: Eirini Arvaniti, Manfred Claassen
Date: 2018-11-16 00:00:00
Tasks: General Classification, whole slide images

Automated grading of prostate cancer histopathology images is a challenging
task, with one key challenge being the scarcity of annotations down to the
level of regions of interest (strong labels), as typically the prostate cancer
Gleason score is known only for entire tissue slides (weak labels). In this
study, we focus on automated Gleason score assignment of prostate cancer
whole-slide images on the basis of a large weakly-labeled dataset and a smaller
strongly-labeled one. We efficiently leverage information from both label
sources by jointly training a classifier on the two datasets and by introducing
a gradient update scheme that assigns different relative importances to each
training example, as a means of self-controlling the weak supervision signal.
Our approach achieves superior performance when compared with standard Gleason
scoring methods.
Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights
Authors: Melanie F. Pradier, Weiwei Pan, Jiayu Yao, Soumya Ghosh, Finale Doshi-Velez
Date: 2018-11-16 00:00:00
Tasks: Variational Inference

As machine learning systems get widely adopted for high-stake decisions, quantifying uncertainty over predictions becomes crucial. While modern neural networks are making remarkable gains in terms of predictive accuracy, characterizing uncertainty over the parameters of these models is challenging because of the high dimensionality and complex correlations of the network parameter space. This paper introduces a novel variational inference framework for Bayesian neural networks that (1) encodes complex distributions in high-dimensional parameter space with representations in a low-dimensional latent space, and (2) performs inference efficiently on the low-dimensional representations. Across a large array of synthetic and real-world datasets, we show that our method improves uncertainty characterization and model generalization when compared with methods that work directly in the parameter space.