pwc-archive/papers-with-abstracts huggingface dataset:
Local Orthogonal Decomposition for Maximum Inner Product Search
Authors: Xiang Wu, Ruiqi Guo, Sanjiv Kumar, David Simcha
Date: 2019-03-25 00:00:00
Tasks: Quantization

Inverted file and asymmetric distance computation (IVFADC) have been
successfully applied to approximate nearest neighbor search and subsequently
maximum inner product search. In such a framework, vector quantization is used
for coarse partitioning while product quantization is used for quantizing
residuals. In the original IVFADC as well as all of its variants, after
residuals are computed, the second production quantization step is completely
independent of the first vector quantization step. In this work, we seek to
exploit the connection between these two steps when we perform non-exhaustive
search. More specifically, we decompose a residual vector locally into two
orthogonal components and perform uniform quantization and multiscale
quantization to each component respectively. The proposed method, called local
orthogonal decomposition, combined with multiscale quantization consistently
achieves higher recall than previous methods under the same bitrates. We
conduct comprehensive experiments on large scale datasets as well as detailed
ablation tests, demonstrating effectiveness of our method.
MeshGAN: Non-linear 3D Morphable Models of Faces
Authors: Shiyang Cheng, Michael Bronstein, Yuxiang Zhou, Irene Kotsia, Maja Pantic, Stefanos Zafeiriou
Date: 2019-03-25 00:00:00

Generative Adversarial Networks (GANs) are currently the method of choice for
generating visual data. Certain GAN architectures and training methods have
demonstrated exceptional performance in generating realistic synthetic images
(in particular, of human faces). However, for 3D object, GANs still fall short
of the success they have had with images. One of the reasons is due to the fact
that so far GANs have been applied as 3D convolutional architectures to
discrete volumetric representations of 3D objects. In this paper, we propose
the first intrinsic GANs architecture operating directly on 3D meshes (named as
MeshGAN). Both quantitative and qualitative results are provided to show that
MeshGAN can be used to generate high-fidelity 3D face with rich identities and
expressions.
Structured 2D Representation of 3D Data for Shape Processing
Authors: Kripasindhu Sarkar, Elizabeth Mathews, Didier Stricker
Date: 2019-03-25 00:00:00
Tasks: Classification, General Classification

We represent 3D shape by structured 2D representations of fixed length making it feasible to apply well investigated 2D convolutional neural networks (CNN) for both discriminative and geometric tasks on 3D shapes. We first provide a general introduction to such structured descriptors, analyze their different forms and show how a simple 2D CNN can be used to achieve good classification result. With a specialized classification network for images and our structured representation, we achieve the classification accuracy of 99.7\% in the ModelNet40 test set - improving the previous state-of-the-art by a large margin. We finally provide a novel framework for performing the geometric task of 3D segmentation using 2D CNNs and the structured representation - concluding the utility of such descriptors for both discriminative and geometric tasks.
Noise-Tolerant Paradigm for Training Face Recognition CNNs
Authors: Wei Hu, Yangyu Huang, Fan Zhang, Ruirui Li
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Face Recognition

Benefit from large-scale training datasets, deep Convolutional Neural
Networks(CNNs) have achieved impressive results in face recognition(FR).
However, tremendous scale of datasets inevitably lead to noisy data, which
obviously reduce the performance of the trained CNN models. Kicking out wrong
labels from large-scale FR datasets is still very expensive, although some
cleaning approaches are proposed. According to the analysis of the whole
process of training CNN models supervised by angular margin based loss(AM-Loss)
functions, we find that the $\theta$ distribution of training samples
implicitly reflects their probability of being clean. Thus, we propose a novel
training paradigm that employs the idea of weighting samples based on the above
probability. Without any prior knowledge of noise, we can train high
performance CNN models with large-scale FR datasets. Experiments demonstrate
the effectiveness of our training paradigm. The codes are available at
https://github.com/huangyangyu/NoiseFace.
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network
Authors: Hee-Jin Yu, Chang-Hwan Son
Date: 2019-03-25 00:00:00
Tasks: Transfer Learning

A new method of recognizing apple leaf diseases through region-of-interest-aware deep convolutional neural network is proposed in this paper. The primary idea is that leaf disease symptoms appear in the leaf area whereas the background region contains no useful information regarding leaf diseases. To realize this idea, two subnetworks are first designed. One is for the division of the input image into three areas: background, leaf area, and spot area indicating the leaf diseases, which is the region of interest, and the other is for the classification of leaf diseases. The two subnetworks exhibit the architecture types of an encoder-decoder network and VGG network, respectively; subsequently, they are trained separately through transfer learning with a new training set containing class information, according to the types of leaf diseases and the ground truth images where the background, leaf area, and spot area are separated. Next, to connect these subnetworks and subsequently train the connected whole network in an end-to-end manner, the predicted ROI feature map is stacked on the top of the input image through a fusion layer, and subsequently fed into the subnetwork used for the leaf disease identification. The experimental results indicate that correct recognition accuracy can be increased using the predicted ROI feature map. It is also shown that the proposed method obtains better performance than the conventional state-of-the-art methods: transfer-learning-based methods, bilinear model, and multiscale-based deep feature extraction, and pooling approach.
Sample Complexity Lower Bounds for Linear System Identification
Authors: Yassir Jedra, Alexandre Proutiere
Date: 2019-03-25 00:00:00
Tasks: valid

This paper establishes problem-specific sample complexity lower bounds for
linear system identification problems. The sample complexity is defined in the
PAC framework: it corresponds to the time it takes to identify the system
parameters with prescribed accuracy and confidence levels. By problem-specific,
we mean that the lower bound explicitly depends on the system to be identified
(which contrasts with minimax lower bounds), and hence really captures the
identification hardness specific to the system. We consider both uncontrolled
and controlled systems. For uncontrolled systems, the lower bounds are valid
for any linear system, stable or not, and only depend of the system finite-time
controllability gramian. A simplified lower bound depending on the spectrum of
the system only is also derived. In view of recent finitetime analysis of
classical estimation methods (e.g. ordinary least squares), our sample
complexity lower bounds are tight for many systems. For controlled systems, our
lower bounds are not as explicit as in the case of uncontrolled systems, but
could well provide interesting insights into the design of control policy with
minimal sample complexity.
EM-like Learning Chaotic Dynamics from Noisy and Partial Observations
Authors: Duong Nguyen, Said Ouala, Lucas. Drumetz, Ronan Fablet
Date: 2019-03-25 00:00:00

The identification of the governing equations of chaotic dynamical systems
from data has recently emerged as a hot topic. While the seminal work by
Brunton et al. reported proof-of-concepts for idealized observation setting for
fully-observed systems, {\em i.e.} large signal-to-noise ratios and
high-frequency sampling of all system variables, we here address the learning
of data-driven representations of chaotic dynamics for partially-observed
systems, including significant noise patterns and possibly lower and irregular
sampling setting. Instead of considering training losses based on short-term
prediction error like state-of-the-art learning-based schemes, we adopt a
Bayesian formulation and state this issue as a data assimilation problem with
unknown model parameters. To solve for the joint inference of the hidden
dynamics and of model parameters, we combine neural-network representations and
state-of-the-art assimilation schemes. Using iterative Expectation-Maximization
(EM)-like procedures, the key feature of the proposed inference schemes is the
derivation of the posterior of the hidden dynamics. Using a
neural-network-based Ordinary Differential Equation (ODE) representation of
these dynamics, we investigate two strategies: their combination to Ensemble
Kalman Smoothers and Long Short-Term Memory (LSTM)-based variational
approximations of the posterior. Through numerical experiments on the Lorenz-63
system with different noise and time sampling settings, we demonstrate the
ability of the proposed schemes to recover and reproduce the hidden chaotic
dynamics, including their Lyapunov characteristic exponents, when classic
machine learning approaches fail.
Stochastic Gradient Hamiltonian Monte Carlo for Non-Convex Learning
Authors: Huy N. Chau, Miklos Rasonyi
Date: 2019-03-25 00:00:00

Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) is a momentum version of stochastic gradient descent with properly injected Gaussian noise to find a global minimum. In this paper, non-asymptotic convergence analysis of SGHMC is given in the context of non-convex optimization, where subsampling techniques are used over an i.i.d dataset for gradient updates. Our results complement those of [RRT17] and improve on those of [GGZ18].
Fine-tune BERT for Extractive Summarization
Authors: Yang Liu
Date: 2019-03-25 00:00:00
Proceeding: arXiv 2019 3
Tasks: Extractive Document Summarization, Extractive Summarization, Extractive Text Summarization

BERT, a pre-trained Transformer model, has achieved ground-breaking performance on multiple NLP tasks. In this paper, we describe BERTSUM, a simple variant of BERT, for extractive summarization. Our system is the state of the art on the CNN/Dailymail dataset, outperforming the previous best-performed system by 1.65 on ROUGE-L. The codes to reproduce our results are available at https://github.com/nlpyang/BertSum
Learning a Multi-Modal Policy via Imitating Demonstrations with Mixed Behaviors
Authors: Fang-I Hsiao, Jui-Hsuan Kuo, Min Sun
Date: 2019-03-25 00:00:00
Tasks: Decoder

We propose a novel approach to train a multi-modal policy from mixed
demonstrations without their behavior labels. We develop a method to discover
the latent factors of variation in the demonstrations. Specifically, our method
is based on the variational autoencoder with a categorical latent variable. The
encoder infers discrete latent factors corresponding to different behaviors
from demonstrations. The decoder, as a policy, performs the behaviors
accordingly. Once learned, the policy is able to reproduce a specific behavior
by simply conditioning on a categorical vector. We evaluate our method on three
different tasks, including a challenging task with high-dimensional visual
inputs. Experimental results show that our approach is better than various
baseline methods and competitive with a multi-modal policy trained by ground
truth behavior labels.
AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss
Authors: Chenyang Zhu, Kai Xu, Siddhartha Chaudhuri, Li Yi, Leonidas Guibas, Hao Zhang
Date: 2019-03-25 00:00:00
Proceeding: CVPR 2020 6
Tasks: Instance Segmentation, Segmentation, Semantic Segmentation

We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds. Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in. Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set. Specifically, given an input set of unsegmented shapes, we first employ an offline pre-trained part prior network to propose per-shape parts. Then, the co-segmentation network iteratively and} jointly optimizes the part labelings across the set subjected to a novel group consistency loss defined by matrix ranks. While the part prior network can be trained with noisy and inconsistently segmented shapes, the final output of AdaCoSeg is a consistent part labeling for the input set, with each shape segmented into up to (a user-specified) K parts. Overall, our method is weakly supervised, producing segmentations tailored to the test set, without consistent ground-truth segmentations. We show qualitative and quantitative results from AdaCoSeg and evaluate it via ablation studies and comparisons to state-of-the-art co-segmentation methods.
MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning
Authors: Zechun Liu, Haoyuan Mu, Xiangyu Zhang, Zichao Guo, Xin Yang, Tim Kwang-Ting Cheng, Jian Sun
Date: 2019-03-25 00:00:00
Proceeding: ICCV 2019 10
Tasks: AutoML, Meta-Learning

In this paper, we propose a novel meta learning approach for automatic channel pruning of very deep neural networks. We first train a PruningNet, a kind of meta network, which is able to generate weight parameters for any pruned structure given the target network. We use a simple stochastic structure sampling method for training the PruningNet. Then, we apply an evolutionary procedure to search for good-performing pruned networks. The search is highly efficient because the weights are directly generated by the trained PruningNet and we do not need any finetuning at search time. With a single PruningNet trained for the target network, we can search for various Pruned Networks under different constraints with little human participation. Compared to the state-of-the-art pruning methods, we have demonstrated superior performances on MobileNet V1/V2 and ResNet. Codes are available on https://github.com/liuzechun/MetaPruning.
Convolutional neural network for breathing phase detection in lung sounds
Authors: Cristina Jácome, Johan Ravn, Einar Holsbø, Juan Carlos Aviles-Solis, Hasse Melbye, Lars Ailo Bongo
Date: 2019-03-25 00:00:00
Tasks: Specificity, valid

We applied deep learning to create an algorithm for breathing phase detection
in lung sound recordings, and we compared the breathing phases detected by the
algorithm and manually annotated by two experienced lung sound researchers. Our
algorithm uses a convolutional neural network with spectrograms as the
features, removing the need to specify features explicitly. We trained and
evaluated the algorithm using three subsets that are larger than previously
seen in the literature. We evaluated the performance of the method using two
methods. First, discrete count of agreed breathing phases (using 50% overlap
between a pair of boxes), shows a mean agreement with lung sound experts of 97%
for inspiration and 87% for expiration. Second, the fraction of time of
agreement (in seconds) gives higher pseudo-kappa values for inspiration
(0.73-0.88) than expiration (0.63-0.84), showing an average sensitivity of 97%
and an average specificity of 84%. With both evaluation methods, the agreement
between the annotators and the algorithm shows human level performance for the
algorithm. The developed algorithm is valid for detecting breathing phases in
lung sound recordings.
Computational and Robotic Models of Early Language Development: A Review
Authors: Pierre-Yves Oudeyer, George Kachergis, William Schueller
Date: 2019-03-25 00:00:00
Tasks: BIG-bench Machine Learning, Language Acquisition

We review computational and robotics models of early language learning and
development. We first explain why and how these models are used to understand
better how children learn language. We argue that they provide concrete
theories of language learning as a complex dynamic system, complementing
traditional methods in psychology and linguistics. We review different modeling
formalisms, grounded in techniques from machine learning and artificial
intelligence such as Bayesian and neural network approaches. We then discuss
their role in understanding several key mechanisms of language development:
cross-situational statistical learning, embodiment, situated social
interaction, intrinsically motivated learning, and cultural evolution. We
conclude by discussing future challenges for research, including modeling of
large-scale empirical data about language acquisition in real-world
environments.
  Keywords: Early language learning, Computational and robotic models, machine
learning, development, embodiment, social interaction, intrinsic motivation,
self-organization, dynamical systems, complexity.
Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Authors: Zhibin Liu, Zheng-Yu Niu, Hua Wu, Haifeng Wang
Date: 2019-03-25 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Knowledge Graphs, Machine Reading Comprehension, Reading Comprehension, Response Generation

Two types of knowledge, triples from knowledge graphs and texts from documents, have been studied for knowledge aware open-domain conversation generation, in which graph paths can narrow down vertex candidates for knowledge selection decision, and texts can provide rich information for response generation. Fusion of a knowledge graph and texts might yield mutually reinforcing advantages, but there is less study on that. To address this challenge, we propose a knowledge aware chatting machine with three components, an augmented knowledge graph with both triples and texts, knowledge selector, and knowledge aware response generator. For knowledge selection on the graph, we formulate it as a problem of multi-hop graph reasoning to effectively capture conversation flow, which is more explainable and flexible in comparison with previous work. To fully leverage long text information that differentiates our graph from others, we improve a state of the art reasoning algorithm with machine reading comprehension technology. We demonstrate the effectiveness of our system on two datasets in comparison with state-of-the-art models.
Aligning Vector-spaces with Noisy Supervised Lexicons
Authors: Noa Yehezkel Lubin, Jacob Goldberger, Yoav Goldberg
Date: 2019-03-25 00:00:00
Tasks: Translation

The problem of learning to translate between two vector spaces given a set of
aligned points arises in several application areas of NLP. Current solutions
assume that the lexicon which defines the alignment pairs is noise-free. We
consider the case where the set of aligned points is allowed to contain an
amount of noise, in the form of incorrect lexicon pairs and show that this
arises in practice by analyzing the edited dictionaries after the cleaning
process. We demonstrate that such noise substantially degrades the accuracy of
the learned translation when using current methods. We propose a model that
accounts for noisy pairs. This is achieved by introducing a generative model
with a compatible iterative EM algorithm. The algorithm jointly learns the
noise level in the lexicon, finds the set of noisy pairs, and learns the
mapping between the spaces. We demonstrate the effectiveness of our proposed
algorithm on two alignment problems: bilingual word embedding translation, and
mapping between diachronic embedding spaces for recovering the semantic shifts
of words across time periods.
Learning from Adversarial Features for Few-Shot Classification
Authors: Wei Shen, Ziqiang Shi, Jun Sun
Date: 2019-03-25 00:00:00
Tasks: Classification, Few-Shot Learning, General Classification

Many recent few-shot learning methods concentrate on designing novel model
architectures. In this paper, we instead show that with a simple backbone
convolutional network we can even surpass state-of-the-art classification
accuracy. The essential part that contributes to this superior performance is
an adversarial feature learning strategy that improves the generalization
capability of our model. In this work, adversarial features are those features
that can cause the classifier uncertain about its prediction. In order to
generate adversarial features, we firstly locate adversarial regions based on
the derivative of the entropy with respect to an averaging mask. Then we use
the adversarial region attention to aggregate the feature maps to obtain the
adversarial features. In this way, we can explore and exploit the entire
spatial area of the feature maps to mine more diverse discriminative knowledge.
We perform extensive model evaluations and analyses on miniImageNet and
tieredImageNet datasets demonstrating the effectiveness of the proposed method.
Anxious Depression Prediction in Real-time Social Data
Authors: Akshi Kumar, Aditi Sharma, Anshika Arora
Date: 2019-03-25 00:00:00
Tasks: Prediction

Mental well-being and social media have been closely related domains of
study. In this research a novel model, AD prediction model, for anxious
depression prediction in real-time tweets is proposed. This mixed
anxiety-depressive disorder is a predominantly associated with erratic thought
process, restlessness and sleeplessness. Based on the linguistic cues and user
posting patterns, the feature set is defined using a 5-tuple vector <word,
timing, frequency, sentiment, contrast>. An anxiety-related lexicon is built to
detect the presence of anxiety indicators. Time and frequency of tweet is
analyzed for irregularities and opinion polarity analytics is done to find
inconsistencies in posting behaviour. The model is trained using three
classifiers (multinomial na\"ive bayes, gradient boosting, and random forest)
and majority voting using an ensemble voting classifier is done. Preliminary
results are evaluated for tweets of sampled 100 users and the proposed model
achieves a classification accuracy of 85.09%.
Robust Neural Networks using Randomized Adversarial Training
Authors: Alexandre Araujo, Laurent Meunier, Rafael Pinot, Benjamin Negrevergne
Date: 2019-03-25 00:00:00

This paper tackles the problem of defending a neural network against adversarial attacks crafted with different norms (in particular $\ell_\infty$ and $\ell_2$ bounded adversarial examples). It has been observed that defense mechanisms designed to protect against one type of attacks often offer poor performance against the other. We show that $\ell_\infty$ defense mechanisms cannot offer good protection against $\ell_2$ attacks and vice-versa, and we provide both theoretical and empirical insights on this phenomenon. Then, we discuss various ways of combining existing defense mechanisms in order to train neural networks robust against both types of attacks. Our experiments show that these new defense mechanisms offer better protection when attacked with both norms.