pwc-archive/papers-with-abstracts huggingface dataset:
Speaker Selective Beamformer with Keyword Mask Estimation
Authors: Yusuke Kida, Dung Tran, Motoi Omachi, Toru Taniguchi, Yuya Fujita
Date: 2018-10-25 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper addresses the problem of automatic speech recognition (ASR) of a
target speaker in background speech. The novelty of our approach is that we
focus on a wakeup keyword, which is usually used for activating ASR systems
like smart speakers. The proposed method firstly utilizes a DNN-based mask
estimator to separate the mixture signal into the keyword signal uttered by the
target speaker and the remaining background speech. Then the separated signals
are used for calculating a beamforming filter to enhance the subsequent
utterances from the target speaker. Experimental evaluations show that the
trained DNN-based mask can selectively separate the keyword and background
speech from the mixture signal. The effectiveness of the proposed method is
also verified with Japanese ASR experiments, and we confirm that the character
error rates are significantly improved by the proposed method for both
simulated and real recorded test sets.
Learning with Interpretable Structure from Gated RNN
Authors: Bo-Jian Hou, Zhi-Hua Zhou
Date: 2018-10-25 00:00:00
Tasks: Clustering, text-classification, Text Classification

The interpretability of deep learning models has raised extended attention these years. It will be beneficial if we can learn an interpretable structure from deep learning models. In this paper, we focus on Recurrent Neural Networks~(RNNs) especially gated RNNs whose inner mechanism is still not clearly understood. We find that Finite State Automaton~(FSA) that processes sequential data has more interpretable inner mechanism according to the definition of interpretability and can be learned from RNNs as the interpretable structure. We propose two methods to learn FSA from RNN based on two different clustering methods. With the learned FSA and via experiments on artificial and real datasets, we find that FSA is more trustable than the RNN from which it learned, which gives FSA a chance to substitute RNNs in applications involving humans' lives or dangerous facilities. Besides, we analyze how the number of gates affects the performance of RNN. Our result suggests that gate in RNN is important but the less the better, which could be a guidance to design other RNNs. Finally, we observe that the FSA learned from RNN gives semantic aggregated states and its transition graph shows us a very interesting vision of how RNNs intrinsically handle text classification tasks.
K for the Price of 1: Parameter-efficient Multi-task and Transfer Learning
Authors: Pramod Kaushik Mudrakarta, Mark Sandler, Andrey Zhmoginov, Andrew Howard
Date: 2018-10-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: image-classification, Image Classification, Multi-Task Learning, Transfer Learning

We introduce a novel method that enables parameter-efficient transfer and
multi-task learning with deep neural networks. The basic approach is to learn a
model patch - a small set of parameters - that will specialize to each task,
instead of fine-tuning the last layer or the entire network. For instance, we
show that learning a set of scales and biases is sufficient to convert a
pretrained network to perform well on qualitatively different problems (e.g.
converting a Single Shot MultiBox Detection (SSD) model into a 1000-class image
classification model while reusing 98% of parameters of the SSD feature
extractor). Similarly, we show that re-learning existing low-parameter layers
(such as depth-wise convolutions) while keeping the rest of the network frozen
also improves transfer-learning accuracy significantly. Our approach allows
both simultaneous (multi-task) as well as sequential transfer learning. In
several multi-task learning problems, despite using much fewer parameters than
traditional logits-only fine-tuning, we match single-task performance.
Subgradient Descent Learns Orthogonal Dictionaries
Authors: Yu Bai, Qijia Jiang, Ju Sun
Date: 2018-10-25 00:00:00
Proceeding: ICLR 2019 5
Tasks: Dictionary Learning, Representation Learning

This paper concerns dictionary learning, i.e., sparse coding, a fundamental representation learning problem. We show that a subgradient descent algorithm, with random initialization, can provably recover orthogonal dictionaries on a natural nonsmooth, nonconvex $\ell_1$ minimization formulation of the problem, under mild statistical assumptions on the data. This is in contrast to previous provable methods that require either expensive computation or delicate initialization schemes. Our analysis develops several tools for characterizing landscapes of nonsmooth functions, which might be of independent interest for provable training of deep networks with nonsmooth activations (e.g., ReLU), among numerous other applications. Preliminary experiments corroborate our analysis and show that our algorithm works well empirically in recovering orthogonal dictionaries.
Spectral Embedding Norm: Looking Deep into the Spectrum of the Graph Laplacian
Authors: Xiuyuan Cheng, Gal Mishne
Date: 2018-10-25 00:00:00
Tasks: Anomaly Detection, Clustering, Outlier Detection

The extraction of clusters from a dataset which includes multiple clusters and a significant background component is a non-trivial task of practical importance. In image analysis this manifests for example in anomaly detection and target detection. The traditional spectral clustering algorithm, which relies on the leading $K$ eigenvectors to detect $K$ clusters, fails in such cases. In this paper we propose the {\it spectral embedding norm} which sums the squared values of the first $I$ normalized eigenvectors, where $I$ can be significantly larger than $K$. We prove that this quantity can be used to separate clusters from the background in unbalanced settings, including extreme cases such as outlier detection. The performance of the algorithm is not sensitive to the choice of $I$, and we demonstrate its application on synthetic and real-world remote sensing and neuroimaging datasets.
SpiderBoost and Momentum: Faster Stochastic Variance Reduction Algorithms
Authors: Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh
Date: 2018-10-25 00:00:00

SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization. However, SPIDER uses an accuracy-dependent stepsize that slows down the convergence in practice, and cannot handle objective functions that involve nonsmooth regularizers. In this paper, we propose SpiderBoost as an improved scheme, which allows to use a much larger constant-level stepsize while maintaining the same near-optimal oracle complexity, and can be extended with proximal mapping to handle composite optimization (which is nonsmooth and nonconvex) with provable convergence guarantee. In particular, we show that proximal SpiderBoost achieves an oracle complexity of $\mathcal{O}(\min\{n^{1/2}\epsilon^{-2},\epsilon^{-3}\})$ in composite nonconvex optimization, improving the state-of-the-art result by a factor of $\mathcal{O}(\min\{n^{1/6},\epsilon^{-1/3}\})$. We further develop a novel momentum scheme to accelerate SpiderBoost for composite optimization, which achieves the near-optimal oracle complexity in theory and substantial improvement in experiments.
Structure Learning of Deep Networks via DNA Computing Algorithm
Authors: Guoqiang Zhong, Tao Li, Wenxue Liu, Yang Chen
Date: 2018-10-25 00:00:00

Convolutional Neural Network (CNN) has gained state-of-the-art results in
many pattern recognition and computer vision tasks. However, most of the CNN
structures are manually designed by experienced researchers. Therefore, auto-
matically building high performance networks becomes an important problem. In
this paper, we introduce the idea of using DNA computing algorithm to
automatically learn high-performance architectures. In DNA computing algorithm,
we use short DNA strands to represent layers and long DNA strands to represent
overall networks. We found that most of the learned models perform similarly,
and only those performing worse during the first runs of training will perform
worse finally than others. The indicates that: 1) Using DNA computing algorithm
to learn deep architectures is feasible; 2) Local minima should not be a
problem of deep networks; 3) We can use early stop to kill the models with the
bad performance just after several runs of training. In our experiments, an
accuracy 99.73% was obtained on the MNIST data set and an accuracy 95.10% was
obtained on the CIFAR-10 data set.
Truncated Back-propagation for Bilevel Optimization
Authors: Amirreza Shaban, Ching-An Cheng, Nathan Hatch, Byron Boots
Date: 2018-10-25 00:00:00
Tasks: Bilevel Optimization, Meta-Learning

Bilevel optimization has been recently revisited for designing and analyzing
algorithms in hyperparameter tuning and meta learning tasks. However, due to
its nested structure, evaluating exact gradients for high-dimensional problems
is computationally challenging. One heuristic to circumvent this difficulty is
to use the approximate gradient given by performing truncated back-propagation
through the iterative optimization procedure that solves the lower-level
problem. Although promising empirical performance has been reported, its
theoretical properties are still unclear. In this paper, we analyze the
properties of this family of approximate gradients and establish sufficient
conditions for convergence. We validate this on several hyperparameter tuning
and meta learning tasks. We find that optimization with the approximate
gradient computed using few-step back-propagation often performs comparably to
optimization with the exact gradient, while requiring far less memory and half
the computation time.
Engaging Image Captioning Via Personality
Authors: Kurt Shuster, Samuel Humeau, Hexiang Hu, Antoine Bordes, Jason Weston
Date: 2018-10-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Captioning, Sentence

Standard image captioning tasks such as COCO and Flickr30k are factual,
neutral in tone and (to a human) state the obvious (e.g., "a man playing a
guitar"). While such tasks are useful to verify that a machine understands the
content of an image, they are not engaging to humans as captions. With this in
mind we define a new task, Personality-Captions, where the goal is to be as
engaging to humans as possible by incorporating controllable style and
personality traits. We collect and release a large dataset of 201,858 of such
captions conditioned over 215 possible traits. We build models that combine
existing work from (i) sentence representations (Mazare et al., 2018) with
Transformers trained on 1.7 billion dialogue examples; and (ii) image
representations (Mahajan et al., 2018) with ResNets trained on 3.5 billion
social media images. We obtain state-of-the-art performance on Flickr30k and
COCO, and strong performance on our new task. Finally, online evaluations
validate that our task and models are engaging to humans, with our best model
close to human performance.
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health
Authors: Gregory Yauney, Aman Rana, Lawrence C. Wong, Perikumar Javia, Ali Muftu, Pratik Shah
Date: 2018-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Rhythm

Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive
method to screen for health conditions. We report an automated process that
combines intraoral fluorescent porphyrin biomarker imaging, clinical
examinations and machine learning for correlation of systemic health conditions
with periodontal disease. 1215 intraoral fluorescent images, from 284
consenting adults aged 18-90, were analyzed using a machine learning classifier
that can segment periodontal inflammation. The classifier achieved an AUC of
0.677 with precision and recall of 0.271 and 0.429, respectively, indicating a
learned association between disease signatures in collected images. Periodontal
diseases were more prevalent among males (p=0.0012) and older subjects
(p=0.0224) in the screened population. Physicians independently examined the
collected images, assigning localized modified gingival indices (MGIs). MGIs
and periodontal disease were then cross-correlated with responses to a medical
history questionnaire, blood pressure and body mass index measurements, and
optic nerve, tympanic membrane, neurological, and cardiac rhythm imaging
examinations. Gingivitis and early periodontal disease were associated with
subjects diagnosed with optic nerve abnormalities (p <0.0001) in their retinal
scans. We also report significant co-occurrences of periodontal disease in
subjects reporting swollen joints (p=0.0422) and a family history of eye
disease (p=0.0337). These results indicate cross-correlation of poor
periodontal health with systemic health outcomes and stress the importance of
oral health screenings at the primary care level. Our screening process and
analysis method, using images and machine learning, can be generalized for
automated diagnoses and systemic health screenings for other diseases.
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
Authors: Zhuwen Li, Qifeng Chen, Vladlen Koltun
Date: 2018-10-25 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Combinatorial Optimization

We present a learning-based approach to computing solutions for certain
NP-hard problems. Our approach combines deep learning techniques with useful
algorithmic elements from classic heuristics. The central component is a graph
convolutional network that is trained to estimate the likelihood, for each
vertex in a graph, of whether this vertex is part of the optimal solution. The
network is designed and trained to synthesize a diverse set of solutions, which
enables rapid exploration of the solution space via tree search. The presented
approach is evaluated on four canonical NP-hard problems and five datasets,
which include benchmark satisfiability problems and real social network graphs
with up to a hundred thousand nodes. Experimental results demonstrate that the
presented approach substantially outperforms recent deep learning work, and
performs on par with highly optimized state-of-the-art heuristic solvers for
some NP-hard problems. Experiments indicate that our approach generalizes
across datasets, and scales to graphs that are orders of magnitude larger than
those used during training.
Sports Camera Calibration via Synthetic Data
Authors: Jianhui Chen, James J. Little
Date: 2018-10-25 00:00:00
Tasks: Camera Calibration, Generative Adversarial Network, Sports Analytics

Calibrating sports cameras is important for autonomous broadcasting and
sports analysis. Here we propose a highly automatic method for calibrating
sports cameras from a single image using synthetic data. First, we develop a
novel camera pose engine. The camera pose engine has only three significant
free parameters so that it can effectively generate a lot of camera poses and
corresponding edge (i.e, field marking) images. Then, we learn compact deep
features via a siamese network from paired edge image and camera pose and build
a feature-pose database. After that, we use a novel two-GAN (generative
adversarial network) model to detect field markings in real images. Finally, we
query an initial camera pose from the feature-pose database and refine camera
poses using truncated distance images. We evaluate our method on both synthetic
and real data. Our method not only demonstrates the robustness on the synthetic
data but also achieves the state-of-the-art accuracy on a standard soccer
dataset and very high performance on a volleyball dataset.
Understand, Compose and Respond - Answering Visual Questions by a Composition of Abstract Procedures
Authors: Ben Zion Vatashsky, Shimon Ullman
Date: 2018-10-25 00:00:00

An image related question defines a specific visual task that is required in
order to produce an appropriate answer. The answer may depend on a minor detail
in the image and require complex reasoning and use of prior knowledge. When
humans perform this task, they are able to do it in a flexible and robust
manner, integrating modularly any novel visual capability with diverse options
for various elaborations of the task. In contrast, current approaches to solve
this problem by a machine are based on casting the problem as an end-to-end
learning problem, which lacks such abilities.
  We present a different approach, inspired by the aforementioned human
capabilities. The approach is based on the compositional structure of the
question. The underlying idea is that a question has an abstract representation
based on its structure, which is compositional in nature. The question can
consequently be answered by a composition of procedures corresponding to its
substructures. The basic elements of the representation are logical patterns,
which are put together to represent the question. These patterns include a
parametric representation for object classes, properties and relations. Each
basic pattern is mapped into a basic procedure that includes meaningful visual
tasks, and the patterns are composed to produce the overall answering
procedure.
  The UnCoRd (Understand Compose and Respond) system, based on this approach,
integrates existing detection and classification schemes for a set of object
classes, properties and relations. These schemes are incorporated in a modular
manner, providing elaborated answers and corrections for negative answers. In
addition, an external knowledge base is queried for required common-knowledge.
We performed a qualitative analysis of the system, which demonstrates its
representation capabilities and provide suggestions for future developments.
Sample-Efficient Learning of Nonprehensile Manipulation Policies via Physics-Based Informed State Distributions
Authors: Lerrel Pinto, Aditya Mandalika, Brian Hou, Siddhartha Srinivasa
Date: 2018-10-24 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

This paper proposes a sample-efficient yet simple approach to learning
closed-loop policies for nonprehensile manipulation. Although reinforcement
learning (RL) can learn closed-loop policies without requiring access to
underlying physics models, it suffers from poor sample complexity on
challenging tasks. To overcome this problem, we leverage rearrangement planning
to provide an informative physics-based prior on the environment's optimal
state-visitation distribution. Specifically, we present a new technique,
Learning with Planned Episodic Resets (LeaPER), that resets the environment's
state to one informed by the prior during the learning phase. We experimentally
show that LeaPER significantly outperforms traditional RL approaches by a
factor of up to 5X on simulated rearrangement. Further, we relax dynamics from
quasi-static to welded contacts to illustrate that LeaPER is robust to the use
of simpler physics models. Finally, LeaPER's closed-loop policies significantly
improve task success rates relative to both open-loop controls with a planned
path or simple feedback controllers that track open-loop trajectories. We
demonstrate the performance and behavior of LeaPER on a physical 7-DOF
manipulator in https://youtu.be/feS-zFq6J1c.
Meta-modeling game for deriving theoretical-consistent, micro-structural-based traction-separation laws via deep reinforcement learning
Authors: Kun Wang, WaiChing Sun
Date: 2018-10-24 00:00:00
Tasks: Deep Reinforcement Learning, Game of Go, Reinforcement Learning

This paper presents a new meta-modeling framework to employ deep
reinforcement learning (DRL) to generate mechanical constitutive models for
interfaces. The constitutive models are conceptualized as information flow in
directed graphs. The process of writing constitutive models are simplified as a
sequence of forming graph edges with the goal of maximizing the model score (a
function of accuracy, robustness and forward prediction quality). Thus
meta-modeling can be formulated as a Markov decision process with well-defined
states, actions, rules, objective functions, and rewards. By using neural
networks to estimate policies and state values, the computer agent is able to
efficiently self-improve the constitutive model it generated through
self-playing, in the same way AlphaGo Zero (the algorithm that outplayed the
world champion in the game of Go)improves its gameplay. Our numerical examples
show that this automated meta-modeling framework not only produces models which
outperform existing cohesive models on benchmark traction-separation data but
is also capable of detecting hidden mechanisms among micro-structural features
and incorporating them in constitutive models to improve the forward prediction
accuracy, which are difficult tasks to do manually.
Multi-level Memory for Task Oriented Dialogs
Authors: Revanth Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi
Date: 2018-10-24 00:00:00
Proceeding: NAACL 2019 6

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.
Predicting the Semantic Textual Similarity with Siamese CNN and LSTM
Authors: Elvys Linhares Pontes, Stéphane Huet, Andréa Carneiro Linhares, Juan-Manuel Torres-Moreno
Date: 2018-10-24 00:00:00
Proceeding: JEPTALNRECITAL 2018 5
Tasks: Semantic Similarity, Semantic Textual Similarity, STS

Semantic Textual Similarity (STS) is the basis of many applications in
Natural Language Processing (NLP). Our system combines convolution and
recurrent neural networks to measure the semantic similarity of sentences. It
uses a convolution network to take account of the local context of words and an
LSTM to consider the global context of sentences. This combination of networks
helps to preserve the relevant information of sentences and improves the
calculation of the similarity between sentences. Our model has achieved good
results and is competitive with the best state-of-the-art systems.