pwc-archive/papers-with-abstracts huggingface dataset:
GILE: A Generalized Input-Label Embedding for Text Classification
Authors: Nikolaos Pappas, James Henderson
Date: 2018-06-16 00:00:00
Proceeding: TACL 2019 3
Tasks: Classification, General Classification, Multi-Task Learning, text-classification, Text Classification, Zero-Shot Learning

Neural text classification models typically treat output labels as
categorical variables which lack description and semantics. This forces their
parametrization to be dependent on the label set size, and, hence, they are
unable to scale to large label sets and generalize to unseen ones. Existing
joint input-label text models overcome these issues by exploiting label
descriptions, but they are unable to capture complex label relationships, have
rigid parametrization, and their gains on unseen labels happen often at the
expense of weak performance on the labels seen during training. In this paper,
we propose a new input-label model which generalizes over previous such models,
addresses their limitations and does not compromise performance on seen labels.
The model consists of a joint non-linear input-label embedding with
controllable capacity and a joint-space-dependent classification unit which is
trained with cross-entropy loss to optimize classification performance. We
evaluate models on full-resource and low- or zero-resource text classification
of multilingual news and biomedical text with a large label set. Our model
outperforms monolingual and multilingual models which do not leverage label
semantics and previous joint input-label space models in both scenarios.
Natasha: Faster Non-Convex Stochastic Optimization Via Strongly Non-Convex Parameter
Authors: Zeyuan Allen-Zhu
Date: 2017-02-02 00:00:00
Proceeding: ICML 2017 8
Tasks: Stochastic Optimization

Given a nonconvex function that is an average of $n$ smooth functions, we
design stochastic first-order methods to find its approximate stationary
points. The convergence of our new methods depends on the smallest (negative)
eigenvalue $-\sigma$ of the Hessian, a parameter that describes how nonconvex
the function is.
  Our methods outperform known results for a range of parameter $\sigma$, and
can be used to find approximate local minima. Our result implies an interesting
dichotomy: there exists a threshold $\sigma_0$ so that the currently fastest
methods for $\sigma>\sigma_0$ and for $\sigma<\sigma_0$ have different
behaviors: the former scales with $n^{2/3}$ and the latter scales with
$n^{3/4}$.
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks
Authors: Arjun Sondhi, Ali Shojaie
Date: 2018-06-16 00:00:00

We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By exploiting properties of common random graphs, we develop a new algorithm that requires conditioning only on small sets of variables. The proposed algorithm, which is essentially a modified version of the PC-Algorithm, offers significant gains in both computational complexity and estimation accuracy. In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems. We prove the consistency of the proposed algorithm, and show that it also requires a less stringent faithfulness assumption than the PC-Algorithm. Simulations in low and high-dimensional settings are used to illustrate these findings. An application to gene expression data suggests that the proposed algorithm can identify a greater number of clinically relevant genes than current methods.
Offline Extraction of Indic Regional Language from Natural Scene Image using Text Segmentation and Deep Convolutional Sequence
Authors: Sauradip Nag, Pallab Kumar Ganguly, Sumit Roy, Sourab Jha, Krishna Bose, Abhishek Jha, Kousik Dasgupta
Date: 2018-06-16 00:00:00
Tasks: Text Detection, Text Segmentation

Regional language extraction from a natural scene image is always a
challenging proposition due to its dependence on the text information extracted
from Image. Text Extraction on the other hand varies on different lighting
condition, arbitrary orientation, inadequate text information, heavy background
influence over text and change of text appearance. This paper presents a novel
unified method for tackling the above challenges. The proposed work uses an
image correction and segmentation technique on the existing Text Detection
Pipeline an Efficient and Accurate Scene Text Detector (EAST). EAST uses
standard PVAnet architecture to select features and non maximal suppression to
detect text from image. Text recognition is done using combined architecture of
MaxOut convolution neural network (CNN) and Bidirectional long short term
memory (LSTM) network. After recognizing text using the Deep Learning based
approach, the native Languages are translated to English and tokenized using
standard Text Tokenizers. The tokens that very likely represent a location is
used to find the Global Positioning System (GPS) coordinates of the location
and subsequently the regional languages spoken in that location is extracted.
The proposed method is tested on a self generated dataset collected from
Government of India dataset and experimented on Standard Dataset to evaluate
the performance of the proposed technique. Comparative study with a few
state-of-the-art methods on text detection, recognition and extraction of
regional language from images shows that the proposed method outperforms the
existing methods.
Meta-learning: searching in the model space
Authors: Włodzisław Duch, Karol Grudzińsk
Date: 2018-06-16 00:00:00
Tasks: All, Meta-Learning, model

There is no free lunch, no single learning algorithm that will outperform
other algorithms on all data. In practice different approaches are tried and
the best algorithm selected. An alternative solution is to build new algorithms
on demand by creating a framework that accommodates many algorithms. The best
combination of parameters and procedures is searched here in the space of all
possible models belonging to the framework of Similarity-Based Methods (SBMs).
Such meta-learning approach gives a chance to find the best method in all
cases. Issues related to the meta-learning and first tests of this approach are
presented.
EARL: Joint Entity and Relation Linking for Question Answering over Knowledge Graphs
Authors: Mohnish Dubey, Debayan Banerjee, Debanjan Chaudhuri, Jens Lehmann
Date: 2018-01-11 00:00:00
Tasks: Entity Linking, Knowledge Graphs, Question Answering, Relation, Relation Linking, Re-Ranking

Many question answering systems over knowledge graphs rely on entity and
relation linking components in order to connect the natural language input to
the underlying knowledge graph. Traditionally, entity linking and relation
linking have been performed either as dependent sequential tasks or as
independent parallel tasks. In this paper, we propose a framework called EARL,
which performs entity linking and relation linking as a joint task. EARL
implements two different solution strategies for which we provide a comparative
analysis in this paper: The first strategy is a formalisation of the joint
entity and relation linking tasks as an instance of the Generalised Travelling
Salesman Problem (GTSP). In order to be computationally feasible, we employ
approximate GTSP solvers. The second strategy uses machine learning in order to
exploit the connection density between nodes in the knowledge graph. It relies
on three base features and re-ranking steps in order to predict entities and
relations. We compare the strategies and evaluate them on a dataset with 5000
questions. Both strategies significantly outperform the current
state-of-the-art approaches for entity and relation linking.
TrQuery: An Embedding-based Framework for Recommanding SPARQL Queries
Authors: Lijing Zhang, Xiaowang Zhang, Zhiyong Feng
Date: 2018-06-16 00:00:00
Tasks: Graph Matching

In this paper, we present an embedding-based framework (TrQuery) for
recommending solutions of a SPARQL query, including approximate solutions when
exact querying solutions are not available due to incompleteness or
inconsistencies of real-world RDF data. Within this framework, embedding is
applied to score solutions together with edit distance so that we could obtain
more fine-grained recommendations than those recommendations via edit distance.
For instance, graphs of two querying solutions with a similar structure can be
distinguished in our proposed framework while the edit distance depending on
structural difference becomes unable. To this end, we propose a novel score
model built on vector space generated in embedding system to compute the
similarity between an approximate subgraph matching and a whole graph matching.
Finally, we evaluate our approach on large RDF datasets DBpedia and YAGO, and
experimental results show that TrQuery exhibits an excellent behavior in terms
of both effectiveness and efficiency.
Learning towards Minimum Hyperspherical Energy
Authors: Weiyang Liu, Rongmei Lin, Zhen Liu, Lixin Liu, Zhiding Yu, Bo Dai, Le Song
Date: 2018-05-23 00:00:00
Proceeding: NeurIPS 2018 12

Neural networks are a powerful class of nonlinear functions that can be trained end-to-end on various applications. While the over-parametrization nature in many neural networks renders the ability to fit complex functions and the strong representation power to handle challenging tasks, it also leads to highly correlated neurons that can hurt the generalization ability and incur unnecessary computation cost. As a result, how to regularize the network to avoid undesired representation redundancy becomes an important issue. To this end, we draw inspiration from a well-known problem in physics -- Thomson problem, where one seeks to find a state that distributes N electrons on a unit sphere as evenly as possible with minimum potential energy. In light of this intuition, we reduce the redundancy regularization problem to generic energy minimization, and propose a minimum hyperspherical energy (MHE) objective as generic regularization for neural networks. We also propose a few novel variants of MHE, and provide some insights from a theoretical point of view. Finally, we apply neural networks with MHE regularization to several challenging tasks. Extensive experiments demonstrate the effectiveness of our intuition, by showing the superior performance with MHE regularization.
Study of Semi-supervised Approaches to Improving English-Mandarin Code-Switching Speech Recognition
Authors: Pengcheng Guo, Hai-Hua Xu, Lei Xie, Eng Siong Chng
Date: 2018-06-16 00:00:00
Tasks: speech-recognition, Speech Recognition

In this paper, we present our overall efforts to improve the performance of a
code-switching speech recognition system using semi-supervised training methods
from lexicon learning to acoustic modeling, on the South East Asian
Mandarin-English (SEAME) data. We first investigate semi-supervised lexicon
learning approach to adapt the canonical lexicon, which is meant to alleviate
the heavily accented pronunciation issue within the code-switching conversation
of the local area. As a result, the learned lexicon yields improved
performance. Furthermore, we attempt to use semi-supervised training to deal
with those transcriptions that are highly mismatched between human transcribers
and ASR system. Specifically, we conduct semi-supervised training assuming
those poorly transcribed data as unsupervised data. We found the
semi-supervised acoustic modeling can lead to improved results. Finally, to
make up for the limitation of the conventional n-gram language models due to
data sparsity issue, we perform lattice rescoring using neural network language
models, and significant WER reduction is obtained.
Part-Aware Fine-grained Object Categorization using Weakly Supervised Part Detection Network
Authors: Yabin Zhang, Kui Jia, Zhixin Wang
Date: 2018-06-16 00:00:00
Tasks: Object, Object Categorization

Fine-grained object categorization aims for distinguishing objects of subordinate categories that belong to the same entry-level object category. The task is challenging due to the facts that (1) training images with ground-truth labels are difficult to obtain, and (2) variations among different subordinate categories are subtle. It is well established that characterizing features of different subordinate categories are located on local parts of object instances. In fact, careful part annotations are available in many fine-grained categorization datasets. However, manually annotating object parts requires expertise, which is also difficult to generalize to new fine-grained categorization tasks. In this work, we propose a Weakly Supervised Part Detection Network (PartNet) that is able to detect discriminative local parts for use of fine-grained categorization. A vanilla PartNet builds on top of a base subnetwork two parallel streams of upper network layers, which respectively compute scores of classification probabilities (over subordinate categories) and detection probabilities (over a specified number of discriminative part detectors) for local regions of interest (RoIs). The image-level prediction is obtained by aggregating element-wise products of these region-level probabilities. To generate a diverse set of RoIs as inputs of PartNet, we propose a simple Discretized Part Proposals module (DPP) that directly targets for proposing candidates of discriminative local parts, with no bridging via object-level proposals. Experiments on the benchmark CUB-200-2011 and Oxford Flower 102 datasets show the efficacy of our proposed method for both discriminative part detection and fine-grained categorization. In particular, we achieve the new state-of-the-art performance on CUB-200-2011 dataset when ground-truth part annotations are not available.
Show, Attend and Translate: Unsupervised Image Translation with Self-Regularization and Attention
Authors: Chao Yang, Taehwan Kim, Ruizhe Wang, Hao Peng, C. -C. Jay Kuo
Date: 2018-06-16 00:00:00
Tasks: Data Augmentation, Domain Adaptation, Saliency Detection, Translation

Image translation between two domains is a class of problems aiming to learn mapping from an input image in the source domain to an output image in the target domain. It has been applied to numerous domains, such as data augmentation, domain adaptation, and unsupervised training. When paired training data is not accessible, image translation becomes an ill-posed problem. We constrain the problem with the assumption that the translated image needs to be perceptually similar to the original image and also appears to be drawn from the new domain, and propose a simple yet effective image translation model consisting of a single generator trained with a self-regularization term and an adversarial term. We further notice that existing image translation techniques are agnostic to the subjects of interest and often introduce unwanted changes or artifacts to the input. Thus we propose to add an attention module to predict an attention map to guide the image translation process. The module learns to attend to key parts of the image while keeping everything else unaltered, essentially avoiding undesired artifacts or changes. The predicted attention map also opens door to applications such as unsupervised segmentation and saliency detection. Extensive experiments and evaluations show that our model while being simpler, achieves significantly better performance than existing image translation methods.
Large Scale Fine-Grained Categorization and Domain-Specific Transfer Learning
Authors: Yin Cui, Yang song, Chen Sun, Andrew Howard, Serge Belongie
Date: 2018-06-16 00:00:00
Proceeding: CVPR 2018 6
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Categorization, Transfer Learning

Transferring the knowledge learned from large scale datasets (e.g., ImageNet)
via fine-tuning offers an effective solution for domain-specific fine-grained
visual categorization (FGVC) tasks (e.g., recognizing bird species or car make
and model). In such scenarios, data annotation often calls for specialized
domain knowledge and thus is difficult to scale. In this work, we first tackle
a problem in large scale FGVC. Our method won first place in iNaturalist 2017
large scale species classification challenge. Central to the success of our
approach is a training scheme that uses higher image resolution and deals with
the long-tailed distribution of training data. Next, we study transfer learning
via fine-tuning from large scale datasets to small scale, domain-specific FGVC
datasets. We propose a measure to estimate domain similarity via Earth Mover's
Distance and demonstrate that transfer learning benefits from pre-training on a
source domain that is similar to the target domain by this measure. Our
proposed transfer learning outperforms ImageNet pre-training and obtains
state-of-the-art results on multiple commonly used FGVC datasets.
Statistics of Deep Generated Images
Authors: Yu Zeng, Huchuan Lu, Ali Borji
Date: 2017-08-09 00:00:00
Tasks: Generative Adversarial Network

Here, we explore the low-level statistics of images generated by state-of-the-art deep generative models. First, Variational auto-encoder (VAE~\cite{kingma2013auto}), Wasserstein generative adversarial network (WGAN~\cite{arjovsky2017wasserstein}) and deep convolutional generative adversarial network (DCGAN~\cite{radford2015unsupervised}) are trained on the ImageNet dataset and a large set of cartoon frames from animations. Then, for images generated by these models as well as natural scenes and cartoons, statistics including mean power spectrum, the number of connected components in a given image area, distribution of random filter responses, and contrast distribution are computed. Our analyses on training images support current findings on scale invariance, non-Gaussianity, and Weibull contrast distribution of natural scenes. We find that although similar results hold over cartoon images, there is still a significant difference between statistics of natural scenes and images generated by VAE, DCGAN and WGAN models. In particular, generated images do not have scale invariant mean power spectrum magnitude, which indicates existence of extra structures in these images. Inspecting how well the statistics of deep generated images match the known statistical properties of natural images, such as scale invariance, non-Gaussianity, and Weibull contrast distribution, can a) reveal the degree to which deep learning models capture the essence of the natural scenes, b) provide a new dimension to evaluate models, and c) allow possible improvement of image generative models (e.g., via defining new loss functions).
Handling Cold-Start Collaborative Filtering with Reinforcement Learning
Authors: Hima Varsha Dureddy, Zachary Kaden
Date: 2018-06-16 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

A major challenge in recommender systems is handling new users, whom are also
called $\textit{cold-start}$ users. In this paper, we propose a novel approach
for learning an optimal series of questions with which to interview cold-start
users for movie recommender systems. We propose learning interview questions
using Deep Q Networks to create user profiles to make better recommendations to
cold-start users. While our proposed system is trained using a movie
recommender system, our Deep Q Network model should generalize across various
types of recommender systems.
Accurate Spectral Super-resolution from Single RGB Image Using Multi-scale CNN
Authors: Yiqi Yan, Lei Zhang, Jun Li, Wei Wei, Yanning Zhang
Date: 2018-06-10 00:00:00
Tasks: Spectral Reconstruction, Spectral Super-Resolution, Super-Resolution

Different from traditional hyperspectral super-resolution approaches that
focus on improving the spatial resolution, spectral super-resolution aims at
producing a high-resolution hyperspectral image from the RGB observation with
super-resolution in spectral domain. However, it is challenging to accurately
reconstruct a high-dimensional continuous spectrum from three discrete
intensity values at each pixel, since too much information is lost during the
procedure where the latent hyperspectral image is downsampled (e.g., with x10
scaling factor) in spectral domain to produce an RGB observation. To address
this problem, we present a multi-scale deep convolutional neural network (CNN)
to explicitly map the input RGB image into a hyperspectral image. Through
symmetrically downsampling and upsampling the intermediate feature maps in a
cascading paradigm, the local and non-local image information can be jointly
encoded for spectral representation, ultimately improving the spectral
reconstruction accuracy. Extensive experiments on a large hyperspectral dataset
demonstrate the effectiveness of the proposed method.
Scheduled Policy Optimization for Natural Language Communication with Intelligent Agents
Authors: Wenhan Xiong, Xiaoxiao Guo, Mo Yu, Shiyu Chang, Bo-Wen Zhou, William Yang Wang
Date: 2018-06-16 00:00:00
Tasks: Efficient Exploration, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We investigate the task of learning to follow natural language instructions
by jointly reasoning with visual observations and language inputs. In contrast
to existing methods which start with learning from demonstrations (LfD) and
then use reinforcement learning (RL) to fine-tune the model parameters, we
propose a novel policy optimization algorithm which dynamically schedules
demonstration learning and RL. The proposed training paradigm provides
efficient exploration and better generalization beyond existing methods.
Comparing to existing ensemble models, the best single model based on our
proposed method tremendously decreases the execution error by over 50% on a
block-world environment. To further illustrate the exploration strategy of our
RL algorithm, We also include systematic studies on the evolution of policy
entropy during training.
Orthogonal Machine Learning: Power and Limitations
Authors: Lester Mackey, Vasilis Syrgkanis, Ilias Zadik
Date: 2017-11-01 00:00:00
Proceeding: ICML 2018 7
Tasks: 2k, BIG-bench Machine Learning, Causal Inference, LEMMA

Double machine learning provides $\sqrt{n}$-consistent estimates of
parameters of interest even when high-dimensional or nonparametric nuisance
parameters are estimated at an $n^{-1/4}$ rate. The key is to employ
Neyman-orthogonal moment equations which are first-order insensitive to
perturbations in the nuisance parameters. We show that the $n^{-1/4}$
requirement can be improved to $n^{-1/(2k+2)}$ by employing a $k$-th order
notion of orthogonality that grants robustness to more complex or
higher-dimensional nuisance parameters. In the partially linear regression
setting popular in causal inference, we show that we can construct second-order
orthogonal moments if and only if the treatment residual is not normally
distributed. Our proof relies on Stein's lemma and may be of independent
interest. We conclude by demonstrating the robustness benefits of an explicit
doubly-orthogonal estimation procedure for treatment effect.