pwc-archive/papers-with-abstracts huggingface dataset:
Power analysis of knockoff filters for correlated designs
Authors: Jingbo Liu, Philippe Rigollet
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Variable Selection

The knockoff filter introduced by Barber and Cand\`es 2016 is an elegant framework for controlling the false discovery rate in variable selection. While empirical results indicate that this methodology is not too conservative, there is no conclusive theoretical result on its power. When the predictors are i.i.d. Gaussian, it is known that as the signal to noise ratio tend to infinity, the knockoff filter is consistent in the sense that one can make FDR go to 0 and power go to 1 simultaneously. In this work we study the case where the predictors have a general covariance matrix $\Sigma$. We introduce a simple functional called effective signal deficiency (ESD) of the covariance matrix $\Sigma$ that predicts consistency of various variable selection methods. In particular, ESD reveals that the structure of the precision matrix $\Sigma^{-1}$ plays a central role in consistency and therefore, so does the conditional independence structure of the predictors. To leverage this connection, we introduce Conditional Independence knockoff, a simple procedure that is able to compete with the more sophisticated knockoff filters and that is defined when the predictors obey a Gaussian tree graphical models (or when the graph is sufficiently sparse). Our theoretical results are supported by numerical evidence on synthetic data.
Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback
Authors: Mingrui Zhang, Lin Chen, Hamed Hassani, Amin Karbasi
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12

In this paper, we propose three online algorithms for submodular maximisation. The first one, Mono-Frank-Wolfe, reduces the number of per-function gradient evaluations from $T^{1/2}$ [Chen2018Online] and $T^{3/2}$ [chen2018projection] to 1, and achieves a $(1-1/e)$-regret bound of $O(T^{4/5})$. The second one, Bandit-Frank-Wolfe, is the first bandit algorithm for continuous DR-submodular maximization, which achieves a $(1-1/e)$-regret bound of $O(T^{8/9})$. Finally, we extend Bandit-Frank-Wolfe to a bandit algorithm for discrete submodular maximization, Responsive-Frank-Wolfe, which attains a $(1-1/e)$-regret bound of $O(T^{8/9})$ in the responsive bandit setting.
ACE: Adaptive Confusion Energy for Natural World Data Distribution
Authors: Yen-Chi Hsu, Cheng-Yao Hong, Wan-Cyuan Fan, Ming-Sui Lee, Davi Geiger, Tyng-Luh Liu
Date: 2019-10-28 00:00:00
Tasks: Fine-Grained Image Classification, Fine-Grained Visual Recognition

With the development of deep learning, standard classification problems have achieved good results. However, conventional classification problems are often too idealistic. Most data in the natural world usually have imbalanced distribution and fine-grained characteristics. Recently, many state-of-the-art approaches tend to focus on one or another separately, but rarely on both. In this paper, we introduce a novel and adaptive batch-wise regularization based on the proposed Adaptive Confusion Energy (ACE) to flexibly address the nature world distribution, which usually involves fine-grained and long-tailed properties at the same time. ACE increases the difficulty of the training process and further alleviates the overfitting problem. Through the datasets with the technical issue in fine-grained (CUB, CAR, AIR) and long-tailed (ImageNet-LT), or comprehensive issues (CUB-LT, iNaturalist), the result shows that the ACE is not only competitive to some state-of-the-art on performance but also demonstrates the effectiveness of training.
Attention-Gated Graph Convolutions for Extracting Drug Interaction Information from Drug Labels
Authors: Tung Tran, Ramakanth Kavuluru, Halil Kilicoglu
Date: 2019-10-28 00:00:00
Tasks: Relation Extraction, Transfer Learning

Preventable adverse events as a result of medical errors present a growing concern in the healthcare system. As drug-drug interactions (DDIs) may lead to preventable adverse events, being able to extract DDIs from drug labels into a machine-processable form is an important step toward effective dissemination of drug safety information. In this study, we tackle the problem of jointly extracting drugs and their interactions, including interaction outcome, from drug labels. Our deep learning approach entails composing various intermediate representations including sequence and graph based context, where the latter is derived using graph convolutions (GC) with a novel attention-based gating mechanism (holistically called GCA). These representations are then composed in meaningful ways to handle all subtasks jointly. To overcome scarcity in training data, we additionally propose transfer learning by pre-training on related DDI data. Our model is trained and evaluated on the 2018 TAC DDI corpus. Our GCA model in conjunction with transfer learning performs at 39.20% F1 and 26.09% F1 on entity recognition (ER) and relation extraction (RE) respectively on the first official test set and at 45.30% F1 and 27.87% F1 on ER and RE respectively on the second official test set corresponding to an improvement over our prior best results by up to 6 absolute F1 points. After controlling for available training data, our model exhibits state-of-the-art performance by improving over the next comparable best outcome by roughly three F1 points in ER and 1.5 F1 points in RE evaluation across two official test sets.
Unsupervised pre-training for sequence to sequence speech recognition
Authors: Zhiyun Fan, Shiyu Zhou, Bo Xu
Date: 2019-10-28 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Sequence-To-Sequence Speech Recognition, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Unsupervised Pre-training

This paper proposes a novel approach to pre-train encoder-decoder sequence-to-sequence (seq2seq) model with unpaired speech and transcripts respectively. Our pre-training method is divided into two stages, named acoustic pre-trianing and linguistic pre-training. In the acoustic pre-training stage, we use a large amount of speech to pre-train the encoder by predicting masked speech feature chunks with its context. In the linguistic pre-training stage, we generate synthesized speech from a large number of transcripts using a single-speaker text to speech (TTS) system, and use the synthesized paired data to pre-train decoder. This two-stage pre-training method integrates rich acoustic and linguistic knowledge into seq2seq model, which will benefit downstream automatic speech recognition (ASR) tasks. The unsupervised pre-training is finished on AISHELL-2 dataset and we apply the pre-trained model to multiple paired data ratios of AISHELL-1 and HKUST. We obtain relative character error rate reduction (CERR) from 38.24% to 7.88% on AISHELL-1 and from 12.00% to 1.20% on HKUST. Besides, we apply our pretrained model to a cross-lingual case with CALLHOME dataset. For all six languages in CALLHOME dataset, our pre-training method makes model outperform baseline consistently.
Deep causal representation learning for unsupervised domain adaptation
Authors: Raha Moraffah, Kai Shu, Adrienne Raglin, Huan Liu
Date: 2019-10-28 00:00:00
Tasks: Domain Adaptation, Representation Learning, Unsupervised Domain Adaptation

Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data. However, as we transition to higher layers in the model, the representations become more task-specific and less generalizable. Recent research on deep domain adaptation proposed to mitigate this problem by forcing the deep model to learn more transferable feature representations across domains. This is achieved by incorporating domain adaptation methods into deep learning pipeline. The majority of existing models learn the transferable feature representations which are highly correlated with the outcome. However, correlations are not always transferable. In this paper, we propose a novel deep causal representation learning framework for unsupervised domain adaptation, in which we propose to learn domain-invariant causal representations of the input from the source domain. We simulate a virtual target domain using reweighted samples from the source domain and estimate the causal effect of features on the outcomes. The extensive comparative study demonstrates the strengths of the proposed model for unsupervised domain adaptation via causal representations.
Robotic Hierarchical Graph Neurons. A novel implementation of HGN for swarm robotic behaviour control
Authors: Phillip Smith, Aldeida Aleti, Vincent C. S. Lee, Robert Hunjet, Asad Khan
Date: 2019-10-28 00:00:00

This paper explores the use of a novel form of Hierarchical Graph Neurons (HGN) for in-operation behaviour selection in a swarm of robotic agents. This new HGN is called Robotic-HGN (R-HGN), as it matches robot environment observations to environment labels via fusion of match probabilities from both temporal and intra-swarm collections. This approach is novel for HGN as it addresses robotic observations being pseudo-continuous numbers, rather than categorical values. Additionally, the proposed approach is memory and computation-power conservative and thus is acceptable for use in mobile devices such as single-board computers, which are often used in mobile robotic agents. This R-HGN approach is validated against individual behaviour implementation and random behaviour selection. This contrast is made in two sets of simulated environments: environments designed to challenge the held behaviours of the R-HGN, and randomly generated environments which are more challenging for the robotic swarm than R-HGN training conditions. R-HGN has been found to enable appropriate behaviour selection in both these sets, allowing significant swarm performance in pre-trained and unexpected environment conditions.
Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond
Authors: Lin Chen, Hossein Esfandiari, Thomas Fu, Vahab S. Mirrokni
Date: 2019-10-28 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Model Compression

Computing approximate nearest neighbors in high dimensional spaces is a central problem in large-scale data mining with a wide range of applications in machine learning and data science. A popular and effective technique in computing nearest neighbors approximately is the locality-sensitive hashing (LSH) scheme. In this paper, we aim to develop LSH schemes for distance functions that measure the distance between two probability distributions, particularly for f-divergences as well as a generalization to capture mutual information loss. First, we provide a general framework to design LHS schemes for f-divergence distance functions and develop LSH schemes for the generalized Jensen-Shannon divergence and triangular discrimination in this framework. We show a two-sided approximation result for approximation of the generalized Jensen-Shannon divergence by the Hellinger distance, which may be of independent interest. Next, we show a general method of reducing the problem of designing an LSH scheme for a Krein kernel (which can be expressed as the difference of two positive definite kernels) to the problem of maximum inner product search. We exemplify this method by applying it to the mutual information loss, due to its several important applications such as model compression.
Swarm Behaviour Evolution via Rule Sharing and Novelty Search
Authors: Phillip Smith, Robert Hunjet, Aldeida Aleti, Asad Khan
Date: 2019-10-28 00:00:00

We present in this paper an exertion of our previous work by increasing the robustness and coverage of the evolution search via hybridisation with a state-of-the-art novelty search and accelerate the individual agent behaviour searches via a novel behaviour-component sharing technique. Via these improvements, we present Swarm Learning Classifier System 2.0 (SLCS2), a behaviour evolving algorithm which is robust to complex environments, and seen to out-perform a human behaviour designer in challenging cases of the data-transfer task in a range of environmental conditions. Additionally, we examine the impact of tailoring the SLCS2 rule generator for specific environmental conditions. We find this leads to over-fitting, as might be expected, and thus conclude that for greatest environment flexibility a general rule generator should be utilised.
Adaptive Sampling for Estimating Multiple Probability Distributions
Authors: Shubhanshu Shekhar, Tara Javidi, Mohammad Ghavamzadeh
Date: 2019-10-28 00:00:00

We consider the problem of allocating samples to a finite set of discrete distributions in order to learn them uniformly well in terms of four common distance measures: $\ell_2^2$, $\ell_1$, $f$-divergence, and separation distance. To present a unified treatment of these distances, we first propose a general optimistic tracking algorithm and analyze its sample allocation performance w.r.t.~an oracle. We then instantiate this algorithm for the four distance measures and derive bounds on the regret of their resulting allocation schemes. We verify our theoretical findings through some experiments. Finally, we show that the techniques developed in the paper can be easily extended to the related setting of minimizing the average error (in terms of the four distances) in learning a set of distributions.
Computational design of organic solar cell active layer through genetic algorithm
Authors: Caine Ardayfio
Date: 2019-10-28 00:00:00

The active layer microstructure of organic solar cells is critical to efficiency. By studying the photovoltaic properties of organic solar cell's microstructure, it is possible to increase the efficiency of the solar cell. A graph-based microstructure model was employed to approximate the efficiency, measured as short circuit current, of a solar cell given a microstructure. Through probabilistic graph-based optimization, a class of microstructures were found with an efficiency surpassing that of more conventional morphologies. These optimized solar cells surpass the efficiency of more conventional photovoltaic devices as they better facilitate charge transport, generation, and dissociation. A device was designed with a 40.29% increase in short circuit current from the solar cells with the currently believed optimal morphology. The designed morphologies feature two dendritic clusters of the donor material poly(3-hexylthiophene-2,5-diyl) (P3HT) and the acceptor material phenyl-C61-Butyric-Acid-Methyl Ester (PCBM). The designed microstructure's increase in performance contrasts with more conventional structures featuring interdigitated or bilayer strands of P3HT and PCBM. The change of microstructure morphology through graph-based evolution obtains an organic solar cell with an efficiency significantly greater than conventional organic solar cells, proves the validity of graph-based microstructure models for simulation in materials science, and advances the vision of an inexpensive, efficient form of renewable energy.
What does BERT Learn from Multiple-Choice Reading Comprehension Datasets?
Authors: Chenglei Si, Shuohang Wang, Min-Yen Kan, Jing Jiang
Date: 2019-10-28 00:00:00
Tasks: Multiple-choice, Reading Comprehension

Multiple-Choice Reading Comprehension (MCRC) requires the model to read the passage and question, and select the correct answer among the given options. Recent state-of-the-art models have achieved impressive performance on multiple MCRC datasets. However, such performance may not reflect the model's true ability of language understanding and reasoning. In this work, we adopt two approaches to investigate what BERT learns from MCRC datasets: 1) an un-readable data attack, in which we add keywords to confuse BERT, leading to a significant performance drop; and 2) an un-answerable data training, in which we train BERT on partial or shuffled input. Under un-answerable data training, BERT achieves unexpectedly high performance. Based on our experiments on the 5 key MCRC datasets - RACE, MCTest, MCScript, MCScript2.0, DREAM - we observe that 1) fine-tuned BERT mainly learns how keywords lead to correct prediction, instead of learning semantic understanding and reasoning; and 2) BERT does not need correct syntactic information to solve the task; 3) there exists artifacts in these datasets such that they can be solved even without the full context.
Variable Selection with Copula Entropy
Authors: Jian Ma
Date: 2019-10-28 00:00:00
Tasks: Explainable Models, Feature Importance, Variable Selection

Variable selection is of significant importance for classification and regression tasks in machine learning and statistical applications where both predictability and explainability are needed. In this paper, a Copula Entropy (CE) based method for variable selection which use CE based ranks to select variables is proposed. The method is both model-free and tuning-free. Comparison experiments between the proposed method and traditional variable selection methods, such as Distance Correlation, Hilbert-Schmidt Independence Criterion, Stepwise Selection, regularized generalized linear models and Adaptive LASSO, were conducted on the UCI heart disease data. Experimental results show that CE based method can select the `right' variables out more effectively and derive better interpretable results than traditional methods do without sacrificing accuracy performance. It is believed that CE based variable selection can help to build more explainable models.
Adaptive Loss Scaling for Mixed Precision Training
Authors: Ruizhe Zhao, Brian Vogel, Tanvir Ahmed
Date: 2019-10-28 00:00:00
Proceeding: ICLR 2020 1

Mixed precision training (MPT) is becoming a practical technique to improve the speed and energy efficiency of training deep neural networks by leveraging the fast hardware support for IEEE half-precision floating point that is available in existing GPUs. MPT is typically used in combination with a technique called loss scaling, that works by scaling up the loss value up before the start of backpropagation in order to minimize the impact of numerical underflow on training. Unfortunately, existing methods make this loss scale value a hyperparameter that needs to be tuned per-model, and a single scale cannot be adapted to different layers at different training stages. We introduce a loss scaling-based training method called adaptive loss scaling that makes MPT easier and more practical to use, by removing the need to tune a model-specific loss scale hyperparameter. We achieve this by introducing layer-wise loss scale values which are automatically computed during training to deal with underflow more effectively than existing methods. We present experimental results on a variety of networks and tasks that show our approach can shorten the time to convergence and improve accuracy compared to the existing state-of-the-art MPT and single-precision floating point
Pushing the Frontiers of Unconstrained Crowd Counting: New Dataset and Benchmark Method
Authors: Vishwanath A. Sindagi, Rajeev Yasarla, Vishal M. Patel
Date: 2019-10-28 00:00:00
Proceeding: ICCV 2019 10
Tasks: Crowd Counting

In this work, we propose a novel crowd counting network that progressively generates crowd density maps via residual error estimation. The proposed method uses VGG16 as the backbone network and employs density map generated by the final layer as a coarse prediction to refine and generate finer density maps in a progressive fashion using residual learning. Additionally, the residual learning is guided by an uncertainty-based confidence weighting mechanism that permits the flow of only high-confidence residuals in the refinement path. The proposed Confidence Guided Deep Residual Counting Network (CG-DRCN) is evaluated on recent complex datasets, and it achieves significant improvements in errors. Furthermore, we introduce a new large scale unconstrained crowd counting dataset (JHU-CROWD) that is ~2.8 larger than the most recent crowd counting datasets in terms of the number of images. It contains 4,250 images with 1.11 million annotations. In comparison to existing datasets, the proposed dataset is collected under a variety of diverse scenarios and environmental conditions. Specifically, the dataset includes several images with weather-based degradations and illumination variations in addition to many distractor images, making it a very challenging dataset. Additionally, the dataset consists of rich annotations at both image-level and head-level. Several recent methods are evaluated and compared on this dataset.
Effect of choice of probability distribution, randomness, and search methods for alignment modeling in sequence-to-sequence text-to-speech synthesis using hard alignment
Authors: Yusuke Yasuda, Xin Wang, Junichi Yamagishi
Date: 2019-10-28 00:00:00
Tasks: Hard Attention, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Sequence-to-sequence text-to-speech (TTS) is dominated by soft-attention-based methods. Recently, hard-attention-based methods have been proposed to prevent fatal alignment errors, but their sampling method of discrete alignment is poorly investigated. This research investigates various combinations of sampling methods and probability distributions for alignment transition modeling in a hard-alignment-based sequence-to-sequence TTS method called SSNT-TTS. We clarify the common sampling methods of discrete variables including greedy search, beam search, and random sampling from a Bernoulli distribution in a more general way. Furthermore, we introduce the binary Concrete distribution to model discrete variables more properly. The results of a listening test shows that deterministic search is more preferable than stochastic search, and the binary Concrete distribution is robust with stochastic search for natural alignment transition.
Transferring neural speech waveform synthesizers to musical instrument sounds generation
Authors: Yi Zhao, Xin Wang, Lauri Juvela, Junichi Yamagishi
Date: 2019-10-27 00:00:00
Tasks: Audio Generation, Audio Synthesis, Speech Synthesis, Zero-Shot Learning

Recent neural waveform synthesizers such as WaveNet, WaveGlow, and the neural-source-filter (NSF) model have shown good performance in speech synthesis despite their different methods of waveform generation. The similarity between speech and music audio synthesis techniques suggests interesting avenues to explore in terms of the best way to apply speech synthesizers in the music domain. This work compares three neural synthesizers used for musical instrument sounds generation under three scenarios: training from scratch on music data, zero-shot learning from the speech domain, and fine-tuning-based adaptation from the speech to the music domain. The results of a large-scale perceptual test demonstrated that the performance of three synthesizers improved when they were pre-trained on speech data and fine-tuned on music data, which indicates the usefulness of knowledge from speech data for music audio generation. Among the synthesizers, WaveGlow showed the best potential in zero-shot learning while NSF performed best in the other scenarios and could generate samples that were perceptually close to natural audio.