pwc-archive/papers-with-abstracts huggingface dataset:
Under what circumstances do local codes emerge in feed-forward neural networks
Authors: Ella M. Gale, Nicolas Martin
Date: 2019-09-25 00:00:00

Localist coding schemes are more easily interpretable than the distributed schemes but generally believed to be biologically implausible. Recent results have found highly selective units and object detectors in NNs that are indicative of local codes (LCs). Here we undertake a constructionist study on feed-forward NNs and find LCs emerging in response to invariant features, and this finding is robust until the invariant feature is perturbed by 40%. Decreasing the number of input data, increasing the relative weight of the invariant features and large values of dropout all increase the number of LCs. Longer training times increase the number of LCs and the turning point of the LC-epoch curve correlates well with the point at which NNs reach 90-100% on both test and training accuracy. Pseudo-deep networks (2 hidden layers) which have many LCs lose them when common aspects of deep-NN research are applied (large training data, ReLU activations, early stopping on training accuracy and softmax), suggesting that LCs may not be found in deep-NNs. Switching to more biologically feasible constraints (sigmoidal activation functions, longer training times, dropout, activation noise) increases the number of LCs. If LCs are not found in the feed-forward classification layers of modern deep-CNNs these data suggest this could either be caused by a lack of (moderately) invariant features being passed to the fully connected layers or due to the choice of training conditions and architecture. Should the interpretability and resilience to noise of LCs be required, this work suggests how to tune a NN so they emerge.
Global Adversarial Robustness Guarantees for Neural Networks
Authors: Luca Laurenti, Andrea Patane, Matthew Wicker, Luca Bortolussi, Luca Cardelli, Marta Kwiatkowska
Date: 2019-09-25 00:00:00
Tasks: Adversarial Robustness

We investigate global adversarial robustness guarantees for machine learning models.  Specifically, given a trained model we consider the problem of computing the probability that its prediction at any point sampled from the (unknown) input distribution is susceptible to adversarial attacks.  Assuming continuity of the model, we prove measurability for a selection of local robustness properties used in the literature. We then show how concentration inequalities can be employed to compute global robustness with estimation error upper-bounded by $\epsilon$, for any $\epsilon > 0$ selected a priori. We utilise the methods to provide statistically sound analysis of the robustness/accuracy trade-off for a variety of neural networks architectures and training methods on MNIST, Fashion-MNIST and CIFAR. We empirically observe that robustness and accuracy tend to be negatively correlated for networks trained via stochastic gradient descent and with iterative pruning techniques, while a positive trend is observed between them in Bayesian settings.
Learning to Reason: Distilling Hierarchy via Self-Supervision and Reinforcement Learning
Authors: Jung-Su Ha, Young-Jin Park, Hyeok-Joo Chae, Soon-Seo Park, Han-Lim Choi
Date: 2019-09-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

We present a hierarchical planning and control framework that enables an agent to perform various tasks and adapt to a new task flexibly. Rather than learning an individual policy for each particular task, the proposed framework, DISH, distills a hierarchical policy from a set of tasks by self-supervision and reinforcement learning. The framework is based on the idea of latent variable models that represent high-dimensional observations using low-dimensional latent variables. The resulting policy consists of two levels of hierarchy: (i) a planning module that reasons a sequence of latent intentions that would lead to optimistic future and (ii) a feedback control policy, shared across the tasks, that executes the inferred intention. Because the reasoning is performed in low-dimensional latent space, the learned policy can immediately be used to solve or adapt to new tasks without additional training. We demonstrate the proposed framework can learn compact representations (3-dimensional latent states for a 90-dimensional humanoid system) while solving a small number of imitation tasks, and the resulting policy is directly applicable to other types of tasks, i.e., navigation in cluttered environments.
COMBINED FLEXIBLE ACTIVATION FUNCTIONS FOR DEEP NEURAL NETWORKS
Authors: Renlong Jie, Junbin Gao, Andrey Vasnev, Minh-Ngoc Tran
Date: 2019-09-25 00:00:00
Tasks: image-classification, Image Classification, Philosophy, Time Series, Time Series Analysis, Time Series Forecasting

Activation in deep neural networks is fundamental to achieving non-linear mappings. Traditional studies mainly focus on finding fixed activations for a particular set of learning tasks or model architectures. The research on flexible activation is quite limited in both designing philosophy and application scenarios. In this study, we propose a general combined form of flexible activation functions as well as three principles of choosing flexible activation component. Based on this, we develop two novel flexible activation functions that can be implemented in LSTM cells and auto-encoder layers. Also two new regularisation terms based on assumptions as prior knowledge are proposed. We find that LSTM and auto-encoder models with proposed flexible activations provides significant improvements on time series forecasting and image compressing tasks, while layer-wise regularization can improve the performance of CNN (LeNet-5) models with RPeLu activation in image classification tasks.
Meta-RCNN: Meta Learning for Few-Shot Object Detection
Authors: Xiongwei Wu, Doyen Sahoo, Steven C. H. Hoi
Date: 2019-09-25 00:00:00
Tasks: Cross-Domain Few-Shot Object Detection, Few-Shot Object Detection, Meta-Learning, Object, object-detection, Object Detection, Region Proposal

Despite significant advances in object detection in recent years, training effective detectors in a small data regime remains an open challenge. Labelling training data for object detection is extremely expensive, and there is a need to develop techniques that can generalize well from small amounts of labelled data. We investigate this problem of few-shot object detection, where a detector has access to only limited amounts of annotated data. Based on the recently evolving meta-learning principle, we propose a novel meta-learning framework for object detection named ``Meta-RCNN", which learns the ability to perform few-shot detection via meta-learning. Specifically, Meta-RCNN learns an object detector in an episodic learning paradigm on the (meta) training data. This learning scheme helps acquire a prior which enables Meta-RCNN to do few-shot detection on novel tasks. Built on top of the Faster RCNN model, in Meta-RCNN, both the Region Proposal Network (RPN) and the object classification branch are meta-learned. The meta-trained RPN learns to provide class-specific proposals, while the object classifier learns to do few-shot classification. The novel loss objectives and learning strategy of Meta-RCNN can be trained in an end-to-end manner. We demonstrate the effectiveness of Meta-RCNN in addressing few-shot detection on Pascal VOC dataset and achieve promising results.
Learning from Imperfect Annotations: An End-to-End Approach
Authors: Emmanouil Antonios Platanios, Maruan Al-Shedivat, Eric Xing, Tom Mitchell
Date: 2019-09-25 00:00:00
Tasks: Ensemble Learning

Many machine learning systems today are trained on large amounts of human-annotated data. Annotation tasks that require a high level of competency make data acquisition  expensive, while the resulting labels are often subjective, inconsistent, and may contain a variety of human biases. To improve data quality, practitioners often need to collect multiple annotations per example and aggregate them before training models. Such a multi-stage approach results in redundant annotations and may often produce imperfect ``ground truth'' labels that limit the potential of training supervised machine learning models. We propose a new end-to-end framework that enables us to: (i) merge the aggregation step with model training, thus allowing deep learning systems to learn to predict ground truth estimates directly from the available data, and (ii) model difficulties of examples and learn representations of the annotators that allow us to estimate and take into account their competencies. Our approach is general and has many applications, including training more accurate models on crowdsourced data, ensemble learning, as well as classifier accuracy estimation from unlabeled data. We conduct an extensive experimental evaluation of our method on 5 crowdsourcing datasets of varied difficulty and show accuracy gains of up to 25% over the current state-of-the-art approaches for aggregating annotations, as well as significant reductions in the required annotation redundancy.
Zero-Shot Out-of-Distribution Detection with Feature Correlations
Authors: Chandramouli S Sastry, Sageev Oore
Date: 2019-09-25 00:00:00
Tasks: Feature Correlation, Out-of-Distribution Detection, Out of Distribution (OOD) Detection

When presented with Out-of-Distribution (OOD) examples, deep neural networks yield confident, incorrect predictions. Detecting OOD examples is challenging, and the potential risks are high. In this paper, we propose to detect OOD examples by identifying inconsistencies between activity patterns and class predicted. We find that characterizing activity patterns by feature correlations and identifying anomalies in pairwise feature correlation values can yield high OOD detection rates. We identify anomalies in the pairwise feature correlations by simply comparing each pairwise correlation value with its respective range observed over the training data. Unlike many approaches, this can be used with any pre-trained softmax classifier and does not require access to OOD data for fine-tuning hyperparameters, nor does it require OOD access for inferring parameters. The method is applicable across a variety of architectures and vision datasets and generally performs better than or equal to state-of-the-art OOD detection methods, including those that do assume access to OOD examples.
Neural Video Encoding
Authors: Abel Brown, Robert DiPietro
Date: 2019-09-25 00:00:00

Deep neural networks have had unprecedented success in computer vision, natural language processing, and speech largely due to the ability to search for suitable task algorithms via differentiable programming. In this paper, we borrow ideas from Kolmogorov complexity theory and normalizing flows to explore the possibilities of finding arbitrary algorithms that represent data. In particular, algorithms which encode sequences of video image frames. Ultimately, we demonstrate neural video encoded using convolutional neural networks to transform autoregressive noise processes and show that this method has surprising cryptographic analogs for information security.
Continuous Adaptation in Multi-agent Competitive Environments
Authors: Kuei-Tso Lee, Sheng-Jyh Wang
Date: 2019-09-25 00:00:00

In a multi-agent competitive environment, we would expect an agent who can quickly adapt to environmental changes may have a higher probability to survive and beat other agents. In this paper, to discuss whether the adaptation capability can help a learning agent to improve its competitiveness in a multi-agent environment, we construct a simplified baseball game scenario to develop and evaluate the adaptation capability of learning agents. Our baseball game scenario is modeled as a two-player zero-sum stochastic game with only the final reward. We purpose a modified Deep CFR algorithm to learn a strategy that approximates the Nash equilibrium strategy. We also form several teams, with different teams adopting different playing strategies, trying to analyze (1) whether an adaptation mechanism can help in increasing the winning percentage and (2) what kind of initial strategies can help a team to get a higher winning percentage. The experimental results show that the learned Nash-equilibrium strategy is very similar to real-life baseball game strategy. Besides, with the proposed strategy adaptation mechanism, the winning percentage can be increased for the team with a Nash-equilibrium initial strategy. Nevertheless, based on the same adaptation mechanism, those teams with deterministic initial strategies actually become less competitive.
Simple and Effective Stochastic Neural Networks
Authors: Tianyuan Yu, Yongxin Yang, Da Li, Timothy Hospedales, Tao Xiang
Date: 2019-09-25 00:00:00
Tasks: Adversarial Attack, Adversarial Defense

Stochastic neural networks (SNNs) are currently topical, with several paradigms being actively investigated including dropout, Bayesian neural networks, variational information bottleneck (VIB) and noise regularized learning. These neural network variants impact several major considerations, including generalization, network compression, and robustness against adversarial attack and label noise. However, many existing networks are complicated and expensive to train, and/or only address one or two of these practical considerations. In this paper we propose a simple and effective stochastic neural network (SE-SNN) architecture for discriminative learning by directly modeling activation uncertainty and encouraging high activation variability. Compared to existing SNNs, our SE-SNN is simpler to implement and faster to train, and produces state of the art results on network compression by pruning,  adversarial defense and learning with label noise.
On The Difficulty of Warm-Starting Neural Network Training
Authors: Jordan T. Ash, Ryan P. Adams
Date: 2019-09-25 00:00:00
Tasks: Experimental Design

In many real-world deployments of machine learning systems, data arrive piecemeal. These learning scenarios may be passive, where data arrive incrementally due to structural properties of the problem (e.g., daily financial data) or active, where samples are selected according to a measure of their quality (e.g., experimental design). In both of these cases, we are building a sequence of models that incorporate an increasing amount of data. We would like each of these models in the sequence to be performant and take advantage of all the data that are available to that point. Conventional intuition suggests that when solving a sequence of related optimization problems of this form, it should be possible to initialize using the solution of the previous iterate---to "warm start'' the optimization rather than initialize from scratch---and see reductions in wall-clock time. However, in practice this warm-starting seems to yield poorer generalization performance than models that have fresh random initializations, even though the final training losses are similar. While it appears that some hyperparameter settings allow a practitioner to close this generalization gap, they seem to only do so in regimes that damage the wall-clock gains of the warm start. Nevertheless, it is highly desirable to be able to warm-start neural network training, as it would dramatically reduce the resource usage associated with the construction of performant deep learning systems. In this work, we take a closer look at this empirical phenomenon and try to understand when and how it occurs. Although the present investigation did not lead to a solution, we hope that a thorough articulation of the problem will spur new research that may lead to improved methods that consume fewer resources during training.
An Empirical and Comparative Analysis of Data Valuation with Scalable Algorithms
Authors: Ruoxi Jia, Xuehui Sun, Jiacen Xu, Ce Zhang, Bo Li, Dawn Song
Date: 2019-09-25 00:00:00
Tasks: Data Summarization, Data Valuation, Domain Adaptation

This paper focuses on valuating training data for supervised learning tasks and studies the Shapley value, a data value notion originated in cooperative game theory. The Shapley value defines a unique value distribution scheme that satisfies a set of appealing properties desired by a data value notion. However, the Shapley value requires exponential complexity to calculate exactly. Existing approximation algorithms, although achieving great improvement over the exact algorithm, relies on retraining models for multiple times, thus remaining limited when applied to larger-scale learning tasks and real-world datasets.

In this work, we develop a simple and efficient algorithm to estimate the Shapley value with complexity independent with the model size. The key idea is to approximate the model via a $K$-nearest neighbor ($K$NN) classifier, which has a locality structure that can lead to efficient Shapley value calculation. We evaluate the utility of the values produced by the $K$NN proxies in various settings, including label noise correction, watermark detection, data summarization, active data acquisition, and domain adaption. Extensive experiments demonstrate that our algorithm achieves at least comparable utility to the values produced by existing algorithms while significant efficiency improvement. Moreover, we theoretically analyze the Shapley value and justify its advantage over the leave-one-out error as a data value measure.
Unsupervised Learning of Node Embeddings by Detecting Communities
Authors: Chi Thang Duong, Dung Hoang, Truong Giang Le Ba, Thanh Le Cong, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, Karl Aberer
Date: 2019-09-25 00:00:00
Tasks: Clustering, Node Classification, Node Clustering

We present Deep MinCut (DMC), an unsupervised approach to learn node embeddings for graph-structured data. It derives node representations based on their membership in communities. As such, the embeddings directly provide interesting insights into the graph structure, so that the separate node clustering step of existing methods is no longer needed. DMC learns both, node embeddings and communities, simultaneously by minimizing the mincut loss, which captures the number of connections between communities. Striving for high scalability, we also propose a training process for DMC based on minibatches. We provide empirical evidence that the communities learned by DMC are meaningful and that the node embeddings are competitive in different node classification benchmarks.
Global graph curvature
Authors: Liudmila Prokhorenkova, Egor Samosvat, Pim van der Hoorn
Date: 2019-09-25 00:00:00
Tasks: Graph Embedding

Recently, non-Euclidean spaces became popular for embedding structured data. However, determining suitable geometry and, in particular, curvature for a given dataset is still an open problem. In this paper, we define a notion of global graph curvature, specifically catered to the problem of embedding graphs, and analyze the problem of estimating this curvature using only graph-based characteristics (without actual graph embedding). We show that optimal curvature essentially depends on dimensionality of the embedding space and loss function one aims to minimize via embedding. We review the existing notions of local curvature (e.g., Ollivier-Ricci curvature) and analyze their properties theoretically and empirically. In particular, we show that such curvatures are often unable to properly estimate the global one. Hence, we propose a new estimator of global graph curvature specifically designed for zero-one loss function.
Decaying momentum helps neural network training
Authors: John Chen, Anastasios Kyrillidis
Date: 2019-09-25 00:00:00

Momentum is a simple and popular technique in deep learning for gradient-based optimizers. We propose a decaying momentum (Demon) rule, motivated by decaying the total contribution of a gradient to all future updates. Applying Demon to Adam leads to significantly improved training, notably competitive to momentum SGD with learning rate decay, even in settings in which adaptive methods are typically non-competitive. Similarly, applying Demon to momentum SGD rivals momentum SGD with learning rate decay, and in many cases leads to improved performance. Demon is trivial to implement and incurs limited extra computational overhead, compared to the vanilla counterparts.
Quantum algorithm for finding the negative curvature direction
Authors: Kaining Zhang, Min-Hsiu Hsieh, Liu Liu, DaCheng Tao
Date: 2019-09-25 00:00:00

We present an efficient quantum algorithm aiming to find the negative curvature direction for escaping the saddle point, which is a critical subroutine for many second-order non-convex optimization algorithms. We prove that our algorithm could produce the target state corresponding to the negative curvature direction with query complexity O(polylog(d)ε^(-1)), where d is the dimension of the optimization function. The quantum negative curvature finding algorithm is exponentially faster than any known classical method which takes time at least O(dε^(−1/2)). Moreover, we propose an efficient algorithm to achieve the classical read-out of the target state. Our classical read-out algorithm runs exponentially faster on the degree of d than existing counterparts.
Deep Reasoning Networks: Thinking Fast and Slow, for Pattern De-mixing
Authors: Di Chen, Yiwei Bai, Wenting Zhao, Sebastian Ament, John M. Gregoire, Carla P. Gomes
Date: 2019-09-25 00:00:00
Tasks: scientific discovery

We introduce Deep Reasoning Networks (DRNets), an end-to-end framework that combines deep learning with reasoning for solving pattern de-mixing problems, typically in an unsupervised or weakly-supervised setting.  DRNets exploit problem structure and prior knowledge by tightly combining logic and constraint reasoning with stochastic-gradient-based neural network optimization.  We illustrate the power of DRNets on de-mixing overlapping hand-written Sudokus (Multi-MNIST-Sudoku) and on a substantially more complex task in scientific discovery that concerns inferring crystal structures of materials from X-ray diffraction data (Crystal-Structure-Phase-Mapping). DRNets significantly outperform the state of the art and experts' capabilities on Crystal-Structure-Phase-Mapping, recovering more precise and physically meaningful crystal structures. On Multi-MNIST-Sudoku, DRNets perfectly recovered the mixed Sudokus' digits, with 100% digit accuracy, outperforming the supervised state-of-the-art MNIST de-mixing models.
BRIDGING ADVERSARIAL SAMPLES AND ADVERSARIAL NETWORKS
Authors: Faqiang Liu, Mingkun Xu, Guoqi Li, Jing Pei, Luping Shi
Date: 2019-09-25 00:00:00
Tasks: Diversity, Image Generation

Generative adversarial networks have achieved remarkable performance on various tasks but suffer from sensitivity to hyper-parameters, training instability, and mode collapse. We find that this is partly due to gradient given by non-robust discriminator containing non-informative adversarial noise, which can hinder generator from catching the pattern of real samples. Inspired by defense against adversarial samples, we introduce adversarial training of discriminator on real samples that does not exist in classic GANs framework to make adversarial training symmetric, which can balance min-max game and make discriminator more robust. Robust discriminator can give more informative gradient with less adversarial noise, which can stabilize training and accelerate convergence. We validate the proposed method on image generation tasks with varied network architectures quantitatively. Experiments show that training stability, perceptual quality, and diversity of generated samples are consistently improved with small additional training computation cost.