pwc-archive/papers-with-abstracts huggingface dataset:
SAFE-DNN: A Deep Neural Network with Spike Assisted Feature Extraction for Noise Robust Inference
Authors: Xueyuan She, Priyabrata Saha, Daehyun Kim, Yun Long, Saibal Mukhopadhyay
Date: 2019-09-25 00:00:00
Tasks: Classification

We present a Deep Neural Network with Spike Assisted Feature Extraction (SAFE-DNN) to improve robustness of classification under stochastic perturbation of inputs. The proposed network augments a DNN with unsupervised learning of low-level features using spiking neuron network (SNN) with Spike-Time-Dependent-Plasticity (STDP). The complete network learns to ignore local perturbation while performing global feature detection and classification. The experimental results on CIFAR-10 and ImageNet subset demonstrate improved noise robustness for multiple DNN architectures without sacrificing accuracy on clean images.
TWIN GRAPH CONVOLUTIONAL NETWORKS: GCN WITH DUAL GRAPH SUPPORT FOR SEMI-SUPERVISED LEARNING
Authors: Feng Shi, Yizhou Zhao, Ziheng Xu, Tianyang Liu, Song-Chun Zhu
Date: 2019-09-25 00:00:00

Graph Neural Networks as a combination of Graph Signal Processing and Deep Convolutional Networks shows great power in pattern recognition in non-Euclidean domains. In this paper, we propose a new method to deploy two pipelines based on the duality of a graph to improve accuracy. By exploring the primal graph and its dual graph where nodes and edges can be treated as one another, we have exploited the benefits of both vertex features and edge features. As a result, we have arrived at a framework that has great potential in both semisupervised and unsupervised learning.
Deep End-to-end Unsupervised Anomaly Detection
Authors: Li Tangqing, Wang Zheng, Liu Siying, Daniel Lin Wen-Yan
Date: 2019-09-25 00:00:00
Tasks: Anomaly Detection, Clustering, Representation Learning, Unsupervised Anomaly Detection

This paper proposes a novel method to detect anomalies in large datasets under a fully unsupervised setting. The key idea behind our algorithm is to learn the representation underlying normal data. To this end, we leverage the latest clustering
technique suitable for handling high dimensional data. This hypothesis provides a reliable starting point for normal data selection. We train an autoencoder from the normal data subset, and iterate between hypothesizing normal candidate subset
based on clustering and representation learning. The reconstruction error from the learned autoencoder serves as a scoring function to assess the normality of the data. Experimental results on several public benchmark datasets show that the proposed method outperforms state-of-the-art unsupervised techniques and is comparable to semi-supervised techniques in most cases.
Policy Tree Network
Authors: Zac Wellmer, Sepanta Zeighami, James Kwok
Date: 2019-09-25 00:00:00
Tasks: Model-based Reinforcement Learning, MuJoCo, Policy Gradient Methods, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Decision-time planning policies with implicit dynamics models have been shown to work in discrete action spaces with Q learning. However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem. Recent work in Reinforcement Learning has allowed for implicit model based approaches to be extended to Policy Gradient methods. In this work we propose Policy Tree Network (PTN). Policy Tree Network lies at the intersection of Model-Based Reinforcement Learning and Model-Free Reinforcement Learning. Policy Tree Network is a novel approach which, for the first time, demonstrates how to leverage an implicit model to perform decision-time planning with Policy Gradient methods in continuous action spaces. This work is empirically justified on 8 standard MuJoCo environments so that it can easily be compared with similar work done in this area. Additionally, we offer a lower bound on the worst case change in the mean of the policy when tree planning is used and theoretically justify our design choices.
Bias-Resilient Neural Network
Authors: Ehsan Adeli, Qingyu Zhao, Adolf Pfefferbaum, Edith V. Sullivan, L. Fei-Fei, Juan Carlos Niebles, Kilian M. Pohl
Date: 2019-09-25 00:00:00
Tasks: Face Recognition, Gender Classification, Medical Diagnosis

Presence of bias and confounding effects is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in the recent years. Such challenges range from spurious associations of confounding variables in medical studies to the bias of race in gender or face recognition systems. One solution is to enhance datasets and organize them such that they do not reflect biases, which is a cumbersome and intensive task. The alternative is to make use of available data and build models considering these biases. Traditional statistical methods apply straightforward techniques such as residualization or stratification to precomputed features to account for confounding variables. However, these techniques are not in general applicable to end-to-end deep learning methods. In this paper, we propose a method based on the adversarial training strategy to learn discriminative features unbiased and invariant to the confounder(s). This is enabled by incorporating a new adversarial loss function that encourages a vanished correlation between the bias and learned features. We apply our method to a synthetic, a medical diagnosis, and a gender classification (Gender Shades) dataset. Our results show that the learned features by our method not only result in superior prediction performance but also are uncorrelated with the bias or confounder variables. The code is available at http://blinded_for_review/.
ASYNCHRONOUS MULTI-AGENT GENERATIVE ADVERSARIAL IMITATION LEARNING
Authors: Xin Zhang, Weixiao Huang, Renjie Liao, Yanhua Li
Date: 2019-09-25 00:00:00
Tasks: Decision Making, Imitation Learning

Imitation learning aims to inversely learn a policy from expert demonstrations, which has been extensively studied in the literature for both single-agent setting with Markov decision process (MDP) model, and multi-agent setting with Markov game (MG) model. However, existing approaches for general multi-agent Markov games are not applicable to multi-agent extensive Markov games, where agents make asynchronous decisions following a certain order, rather than simultaneous decisions. We propose a novel framework for asynchronous multi-agent generative adversarial imitation learning (AMAGAIL) under general extensive Markov game settings, and the learned expert policies are proven to guarantee subgame perfect equilibrium (SPE), a more general and stronger equilibrium than Nash equilibrium (NE). The experiment results demonstrate that compared to state-of-the-art baselines, our AMAGAIL model can better infer the policy of each expert agent using their demonstration data collected from asynchronous decision-making scenarios (i.e., extensive Markov games).
Efficient Training of Robust and Verifiable Neural Networks
Authors: Akhilan Boopathy, Lily Weng, Sijia Liu, Pin-Yu Chen, Luca Daniel
Date: 2019-09-25 00:00:00

Recent works have developed several methods of defending neural networks against adversarial attacks with certified guarantees. We propose that many common certified defenses can be viewed under a unified framework of regularization. This unified framework provides a technique for comparing different certified defenses with respect to robust generalization. In addition, we develop a new regularizer that is both more efficient than existing certified defenses and can be used to train networks with higher certified accuracy. Our regularizer also extends to an L0 threat model and ensemble models. Through experiments on MNIST, CIFAR-10 and GTSRB, we demonstrate improvements in training speed and certified accuracy compared to state-of-the-art certified defenses.
Data Augmentation in Training CNNs: Injecting Noise to Images
Authors: Murtaza Eren Akbiyik
Date: 2019-09-25 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification, SSIM

Noise injection is a fundamental tool for data augmentation, and yet there is no widely accepted procedure to incorporate it with learning frameworks. This study analyzes the effects of adding or applying different noise models of varying magnitudes to Convolutional Neural Network (CNN) architectures. Noise models that are distributed with different density functions are given common magnitude levels via Structural Similarity (SSIM) metric in order to create an appropriate ground for comparison. The basic results are conforming with the most of the common notions in machine learning, and also introduces some novel heuristics and recommendations on noise injection. The new approaches will provide better understanding on optimal learning procedures for image classification.
Scale-Equivariant Neural Networks with Decomposed Convolutional Filters
Authors: Wei Zhu, Qiang Qiu, Robert Calderbank, Guillermo Sapiro, Xiuyuan Cheng
Date: 2019-09-25 00:00:00
Tasks: image-classification, Image Classification

Encoding the input scale information explicitly into the representation learned by a convolutional neural network (CNN) is beneficial for many vision tasks especially when dealing with multiscale input signals. We study, in this paper, a scale-equivariant CNN architecture with joint convolutions across the space and the scaling group, which is shown to be both sufficient and necessary to achieve scale-equivariant representations. To reduce the model complexity and computational burden, we decompose the convolutional filters under two pre-fixed separable bases and truncate the expansion to low-frequency components. A further benefit of the truncated filter expansion is the improved deformation robustness of the equivariant representation. Numerical experiments demonstrate that the proposed scale-equivariant neural network with decomposed convolutional filters (ScDCFNet) achieves significantly improved performance in multiscale image classification and better interpretability than regular CNNs at a reduced model size.
The Effect of Residual Architecture on the Per-Layer Gradient of Deep Networks
Authors: Etai Littwin, Lior Wolf
Date: 2019-09-25 00:00:00

A critical part of the training process of neural networks takes place in the very first gradient steps post initialization. In this work, we study the connection between the network's architecture and initialization parameters, to the statistical properties of the gradient in random fully connected ReLU networks, through the study of the the Jacobian. We compare three types of architectures: vanilla networks, ResNets and DenseNets. The later two, as we show, preserve the variance of the gradient norm through arbitrary depths when initialized properly,  which prevents exploding or decaying gradients at deeper layers. In addition, we show that the statistics of the per layer gradient norm is a function of the architecture and the layer's size, but surprisingly not the layer's depth. 

This depth invariant result is surprising in light of the literature results that state that the norm of the layer's activations grows exponentially with the specific layer's depth. Experimental support is given in order to validate our theoretical results and to reintroduce concatenated ReLU blocks, which, as we show, present better initialization properties than ReLU blocks in the case of fully connected networks.
City Metro Network Expansion with Reinforcement Learning
Authors: Yu Wei, Minjia Mao, Xi Zhao, Jianhua Zou
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

This paper presents a method to solve the city metro network expansion problem using reinforcement learning (RL). In this method, we formulate the metro expansion as a process of sequential station selection, and design feasibility rules based on the selected station sequence to ensure the reasonable connection patterns of metro line. Following this formulation, we train an actor critic model to design the next metro line. The actor is a seq2seq network with attention mechanism to generate the parameterized policy which is the probability distribution over feasible stations. The critic is used to estimate the expected reward, which is determined by the output station sequences generated by the actor during training, in order to reduce the training variance. The learning procedure only requires the reward calculation, thus our general method can be extended to multi-factor cases easily. Considering origin-destination (OD) trips and social equity, we expand the current metro network in Xi'an, China, based on the real mobility information of 24,770,715 mobile phone users in the whole city. The results demonstrate the effectiveness of our method.
Verification of Generative-Model-Based Visual Transformations
Authors: Matthew Mirman, Timon Gehr, Martin Vechev
Date: 2019-09-25 00:00:00
Tasks: model

Generative networks are promising models for specifying visual transformations. Unfortunately, certification of generative models is challenging as one needs to capture sufficient non-convexity so to produce precise bounds on the output. Existing verification methods either fail to scale to generative networks or do not capture enough non-convexity. In this work, we present a new verifier, called ApproxLine, that can certify non-trivial properties of generative networks. ApproxLine performs both deterministic and probabilistic abstract interpretation and captures infinite sets of outputs of generative networks. We show that ApproxLine can verify interesting interpolations in the network's latent space.
Beyond GANs: Transforming without a Target Distribution
Authors: Matthew Amodio, David van Dijk, Ruth Montgomery, Guy Wolf, Smita Krishnaswamy
Date: 2019-09-25 00:00:00
Tasks: Generative Adversarial Network

While generative neural networks can learn to transform a specific input dataset into a specific target dataset, they require having just such a paired set of input/output datasets. For instance, to fool the discriminator, a generative adversarial network (GAN) exclusively trained to transform images of black-haired *men* to blond-haired *men* would need to change gender-related characteristics as well as hair color when given images of black-haired *women* as input. This is problematic, as often it is possible to obtain *a* pair of (source, target) distributions but then have a second source distribution where the target distribution is unknown. The computational challenge is that generative models are good at generation within the manifold of the data that they are trained on. However, generating new samples outside of the manifold or extrapolating "out-of-sample" is a much harder problem that has been less well studied. To address this, we introduce a technique called *neuron editing* that learns how neurons encode an edit for a particular transformation in a latent space. We use an autoencoder to decompose the variation within the dataset into activations of different neurons and generate transformed data by defining an editing transformation on those neurons. By performing the transformation in a latent trained space, we encode fairly complex and non-linear transformations to the data with much simpler distribution shifts to the neuron's activations. Our technique is general and works on a wide variety of data domains and applications. We first demonstrate it on image transformations and then move to our two main biological applications: removal of batch artifacts representing unwanted noise and modeling the effect of drug treatments to predict synergy between drugs.
Demystifying Graph Neural Network Via Graph Filter Assessment
Authors: Yewen Wang, Ziniu Hu, Yusong Ye, Yizhou Sun
Date: 2019-09-25 00:00:00
Tasks: Graph Neural Network

Graph Neural Networks (GNNs) have received tremendous attention recently due to their power in handling graph data for different downstream tasks across different application domains. The key of GNN is its graph convolutional filters, and recently various kinds of filters are designed. However, there still lacks in-depth analysis on (1) Whether there exists a best filter that can perform best on all graph data; (2) Which graph properties will influence the optimal choice of graph filter; (3) How to design appropriate filter adaptive to the graph data. In this paper, we focus on addressing the above three questions. We first propose a novel assessment tool to evaluate the effectiveness of graph convolutional filters for a given graph. Using the assessment tool, we find out that there is no single filter as a `silver bullet' that perform the best on all possible graphs. In addition, different graph structure properties will influence the optimal graph convolutional filter's design choice. Based on these findings, we develop Adaptive Filter Graph Neural Network (AFGNN), a simple but powerful model that can adaptively learn task-specific filter. For a given graph, it leverages graph filter assessment as regularization and learns to combine from a set of base filters. Experiments on both synthetic and real-world benchmark datasets demonstrate that our proposed model can indeed learn an appropriate filter and perform well on graph tasks.
``"Best-of-Many-Samples" Distribution Matching
Authors: Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Date: 2019-09-25 00:00:00

Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders (VAE) on the other hand explicitly maximize a reconstruction-based data log-likelihood forcing it to cover all modes, but suffer from poorer sample quality. Recent works have proposed hybrid VAE-GAN frameworks which integrate a GAN-based synthetic likelihood to the VAE objective to address both the mode collapse and sample quality issues, with limited success. This is because the VAE objective forces a trade-off between the data log-likelihood and divergence to the latent prior. The synthetic likelihood ratio term also shows instability during training. We propose a novel objective with a ``"Best-of-Many-Samples" reconstruction cost and a stable direct estimate of the synthetic likelihood. This enables our hybrid VAE-GAN framework to achieve high data log-likelihood and low divergence to the latent prior at the same time and shows significant improvement over both hybrid VAE-GANS and plain GANs in mode coverage and quality.
Swoosh! Rattle! Thump! - Actions that Sound
Authors: Dhiraj Gandhi, Abhinav Gupta, Lerrel Pinto
Date: 2019-09-25 00:00:00
Tasks: Object

Truly intelligent agents need to capture the interplay of all their senses to build a rich physical understanding of their world. In robotics, we have seen tremendous progress in using visual and tactile perception; however we have often ignored a key sense: sound. This is primarily due to lack of data that captures the interplay of action and sound. In this work, we perform the first large-scale study of the interactions between sound and robotic action. To do this, we create the largest available sound-action-vision dataset with 15,000 interactions on 60 objects using our robotic platform Tilt-Bot. By tilting objects and allowing them to crash into the walls of a robotic tray, we collect rich four-channel audio information. Using this data, we explore the synergies between sound and action, and present three key insights. First, sound is indicative of fine-grained object class information, e.g., sound can differentiate a metal screwdriver from a metal wrench. Second, sound also contains information about the causal effects of an action, i.e. given the sound produced, we can predict what action was applied on the object. Finally, object representations derived from audio embeddings are indicative of implicit physical properties. We demonstrate that on previously unseen objects, audio embeddings generated through interactions can predict forward models 24% better than passive visual embeddings.
Near-Zero-Cost Differentially Private Deep Learning with Teacher Ensembles
Authors: Lichao Sun, Yingbo Zhou, Jia Li, Richard Socher, Philip S. Yu, Caiming Xiong
Date: 2019-09-25 00:00:00
Tasks: Deep Learning

Ensuring the privacy of sensitive data used to train modern machine learning models is of paramount importance in many areas of practice. One approach to study these concerns is through the lens of differential privacy. In this framework, privacy guarantees are generally obtained by perturbing models in such a way that specifics of data used to train the model are made ambiguous. A particular instance of this approach is through a ``teacher-student'' model, wherein the teacher, who owns the sensitive data, provides the student with useful, but noisy, information, hopefully allowing the student model to perform well on a given task without access to particular features of the sensitive data. Because stronger privacy guarantees generally involve more significant noising on the part of the teacher, deploying existing frameworks fundamentally involves a trade-off between utility and privacy guarantee. One of the most important techniques used in previous work involves an ensemble of teacher models, which return information to a student based on a noisy voting procedure.  In this work, we propose a novel voting mechanism, which we call an Immutable Noisy ArgMax, that, under certain conditions, can bear very large random noising from the teacher without affecting the useful information transferred to the student. Our mechanisms improve over the state-of-the-art methods on all measures, and scale to larger tasks with both higher utility and stronger privacy ($\epsilon \approx 0$).
On the Tunability of Optimizers in Deep Learning
Authors: Prabhu Teja S*, Florian Mai*, Thijs Vogels, Martin Jaggi, Francois Fleuret
Date: 2019-09-25 00:00:00
Tasks: Deep Learning

There is no consensus yet on the question whether adaptive gradient methods like Adam are easier to use than non-adaptive optimization methods like SGD. In this work, we fill in the important, yet ambiguous concept of ‘ease-of-use’ by defining an optimizer’s tunability:  How easy is it to find good hyperparameter configurations using automatic random hyperparameter search? We propose a practical and universal quantitative measure for optimizer tunability that can form the basis for a fair optimizer benchmark.  Evaluating a variety of optimizers on an extensive set of standard datasets and architectures, we find  that Adam is the most tunable for the majority of problems, especially with a low budget for hyperparameter tuning.
GUIDEGAN: ATTENTION BASED SPATIAL GUIDANCE FOR IMAGE-TO-IMAGE TRANSLATION
Authors: Yu Lin, Yigong Wang, YiFan Li, Zhuoyi Wang, Yang Gao, Latifur Khan
Date: 2019-09-25 00:00:00
Tasks: Generative Adversarial Network, Image-to-Image Translation, Translation

Recently, Generative Adversarial Network (GAN) and numbers of its variants have been widely used to solve the image-to-image translation problem and achieved extraordinary results in both a supervised and unsupervised manner. However, most GAN-based methods suffer from the imbalance problem between the generator and discriminator in practice. Namely, the relative model capacities of the generator and discriminator do not match, leading to mode collapse and/or diminished gradients. To tackle this problem, we propose a GuideGAN based on attention mechanism. More specifically, we arm the discriminator with an attention mechanism so not only it estimates the probability that its input is real, but also does it create an attention map that highlights the critical features for such prediction. This attention map then assists the generator to produce more plausible and realistic images. We extensively evaluate the proposed GuideGAN framework on a  number of image transfer tasks. Both qualitative results and quantitative comparison demonstrate the superiority of our proposed approach.