pwc-archive/papers-with-abstracts huggingface dataset:
Towards Human-Understandable Visual Explanations: Human Imperceptible Cues Can Better Be Removed
Authors: Kaili Wang, Jose Oramas, Tinne Tuytelaars
Date: 2021-09-29 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

Explainable AI (XAI) methods focus on explaining what a neural network has learned - in other words, identifying the features that are the most influential to the prediction. In this paper, we call them "distinguishing features". 
However, whether a human can make sense of the generated explanation also depends on the perceptibility of these features to humans.
To make sure an explanation is human-understandable, we
argue that the capabilities of humans, constrained by the Human Visual System (HVS) and psychophysics, need to be taken into account. 
We propose the human perceptibility principle for XAI, stating that, to generate human-understandable explanations, neural networks should be steered towards focusing on human-understandable cues during training.
We conduct a case study 
regarding the classification of real vs. fake face images, where many of the distinguishing features picked up by standard neural networks turn out not to be perceptible to humans. By applying the proposed principle, a neural network with human-understandable explanations is trained which, in a survey, is shown to better align with human intuition. This is likely to make the AI more trustworthy and open the door to humans learning from machines. 
In the case study, we specifically investigate and analyze the behaviour of the human-imperceptible high spatial frequency features in neural networks and XAI methods.
Learning to Infer the Structure of Network Games
Authors: Emanuele Rossi, Federico Monti, Yan Leng, Michael M. Bronstein, Xiaowen Dong
Date: 2021-09-29 00:00:00

Strategic interactions between a group of individuals or organisations can be modelled as games played on networks, where a player's payoff depends not only on their actions but also on those of their neighbors. 
Inferring the network structure from observed game outcomes (equilibrium actions) is an important problem with numerous potential applications in economics and social sciences. 
Currently available methods require the knowledge of the utility function associated with the game, which is often unrealistic to obtain in real-world scenarios. To address this limitation, we propose a novel transformer-like architecture which correctly accounts for the symmetries of the problem and learns a mapping from the equilibrium actions to the network structure of the game without explicit knowledge of the utility function. We test our method on three different types of network games using both synthetic and real-world data, and demonstrate its effectiveness in network structure inference and superior performance over existing methods.
Causal Contextual Bandits with Targeted Interventions
Authors: Chandrasekar Subramanian, Balaraman Ravindran
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Multi-Armed Bandits

We study a contextual bandit setting where the learning agent has the ability to perform interventions on targeted subsets of the population, apart from possessing qualitative causal side-information. This novel formalism captures intricacies in real-world scenarios such as software product experimentation where targeted experiments can be conducted. However, this fundamentally changes the set of options that the agent has, compared to standard contextual bandit settings, necessitating new techniques. This is also the first work that integrates causal side-information in a contextual bandit setting, where the agent aims to learn a policy that maps contexts to arms (as opposed to just identifying one best arm). We propose a new algorithm, which we show empirically performs better than baselines on experiments that use purely synthetic data and on real world-inspired experiments. We also prove a bound on regret that theoretically guards performance.
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems
Authors: Feng Guo, Qu Wei, Miao Wang, Zhaoxia Guo
Date: 2021-09-29 00:00:00
Tasks: Combinatorial Optimization, Deep Reinforcement Learning

Vehicle routing problems (VRPs) are a type of classical combinatorial optimization problems widely existing in logistics and transportation operations. There has been an increasing interest to use deep reinforcement learning (DRL) techniques to tackle VRPs, and previous DRL-based studies assumed time-independent travel times between customers. However, travel times in real-world road networks are time-varying, which need to be considered in practical VRPs. We thus propose a Deep Dynamic Attention Models with Gate Mechanisms (DDAM-GM) to learn heuristics for time-dependent VRPs (TDVRPs) in real-world road networks. It extracts the information of node location, node demand, and time-varying travel times between nodes to obtain enhanced node embeddings through a dimension-reducing MHA layer and a synchronous encoder. In addition, we use a gate mechanism to obtain better context embedding. On the basis of a 110-day travel time dataset with 240 time periods per day from an urban road network with 408 nodes and 1250 directed links, we conduct a series of experiments to validate the effectiveness of the proposed model on TDVRPs without and with consideration of time windows, respectively. Experimental results show that our model outperforms significantly two state-of-the-art DRL-based models.
SpecTRA: Spectral Transformer for Graph Representation Learning
Authors: Anson Bastos, Abhishek Nadgeri, Kuldeep Singh, Hiroki Kanezashi, Toyotaro Suzumura, Isaiah Onando Mulang'
Date: 2021-09-29 00:00:00
Tasks: Graph Representation Learning, Representation Learning

Transformers have recently been applied in the more generic domain of graphs. For the same, researchers proposed various positional and structural encoding schemes to overcome the limitation of transformers in modeling the positional invariance in graphs and graph topology. Some of these encoding techniques use the spectrum of the graph. In addition to graph topology, graph signals could be multi-channeled and contain heterogeneous information. We argue that transformers cannot model multichannel signals inherently spread over the graph spectrum. To this end, we propose SpecTRA, a novel approach that induces a spectral module into the transformer architecture to enable decomposition of graph spectrum and selectively learn useful information akin to filtering in the frequency domain. Results on standard benchmark datasets show that the proposed method performs comparably or better than existing transformer and GNN based architectures.
Encoding Event-Based Gesture Data With a Hybrid SNN Guided Variational Auto-encoder
Authors: Kenneth Michael Stewart, Andreea Danielescu, Timothy Shea, Emre Neftci
Date: 2021-09-29 00:00:00
Tasks: Gesture Recognition, Self-Supervised Learning

Commercial mid-air gesture recognition systems have existed for at least a decade, but they have not become a widespread method of interacting with machines. These systems require rigid, dramatic gestures to be performed for accurate recognition that can be fatiguing and unnatural. To address this limitation, we propose a neuromorphic gesture analysis system which encodes event-based gesture data at high temporal resolution. Our novel approach consists of an event-based guided Variational Autoencoder (VAE) which encodes event-based data sensed by a Dynamic Vision Sensor (DVS) into a latent space representation suitable to compute the similarity of mid-air gesture data. We show that the Hybrid Guided-VAE achieves 87% classification accuracy on the DVSGesture dataset and it can encode the sparse, noisy inputs into an interpretable latent space representation, visualized through T-SNE plots. We also implement the encoder component of the model on neuromorphic hardware and discuss the potential for our algorithm to enable real-time, self-supervised learning of natural mid-air gestures.
Detecting Worst-case Corruptions via Loss Landscape Curvature in Deep Reinforcement Learning
Authors: Ezgi Korkmaz, Jonah Brown-Cohen
Date: 2021-09-29 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

The non-robustness of neural network policies to adversarial examples poses a challenge for deep reinforcement learning. One natural approach to mitigate the impact of adversarial examples is to develop methods to detect when a given input is adversarial. In this work we introduce a novel approach for detecting adversarial examples that is computationally efficient, is agnostic to the method used to generate adversarial examples, and theoretically well-motivated. Our method is based on a measure of the local curvature of the neural network policy, which we show differs between adversarial and clean examples. We empirically demonstrate the effectiveness of our method in the Atari environment against a large set of state-of-the-art algorithms for generating adversarial examples. Furthermore, we exhibit the effectiveness of our detection algorithm with the presence of multiple strong detection-aware adversaries.
GSD: Generalized Stochastic Decoding
Authors: Ning Gong, Nianmin Yao
Date: 2021-09-29 00:00:00
Tasks: Text Generation

Although substantial progress has been made in various text generation tasks, there remains a vast gap between current generations and human languages. One reason is that virtually all decoding methods currently developed are pragmatic to address the text degeneration problem, which exists in both deterministic and stochastic decoding algorithms. So, why text generated from these algorithms are divergent? What is the critical difference between these algorithms? Moreover, is it possible to design a generalized framework where existing decoding algorithms can be naturally connected, uniformly described, and mutually inspired?
In this paper, we try to explore answers to these intriguing questions. Correctly, we propose a generalized decoding framework that can be used to describe and connect existing popular decoding algorithms. Based on the framework, we propose a novel implementation with a distinctive core from existing decoding algorithms. As far as we know, this is the first work trying to propose a generalized framework to bridge these decoding algorithms using formal theorems and concrete implementations. By setting up different conditions, our framework provides infinite space to develop new decoding algorithms. Experiments show that text produced by our method is closest to the characteristics of human languages. Source code and the generated text can be accessed from https://github.com/ginoailab/gsd.git.
Towards Scaling Robustness Verification of Semantic Features via Proof Velocity
Authors: Anan Kabaha, Dana Drachsler Cohen
Date: 2021-09-29 00:00:00

Robustness analysis is important for understanding the reliability of neural networks. Despite the significant progress in the verification techniques for both $L_p$- and semantic features- neighborhoods, existing approaches struggle to scale to deep networks and large datasets. For example, we are unaware of any analyzer that scales to AlexNet trained for ImageNet (consisting of 224x224x3 images).
In this work, we take a step towards scaling robustness analysis. We focus on robustness to perturbations of semantic features and introduce the concept of proof guided by velocity to scale the analysis. The key idea is to phrase the verification task as a dynamic system and adaptively identify how to split it into subproblems each with maximal proof velocity. We propose a policy to determine the next subproblem based on the past and by leveraging input splitting, input refinement, and bound tightening. We evaluate our approach on CIFAR-10 and ImageNet and show that it can analyze neighborhoods of various features: hue, saturation, lightness, brightness, and PCA.
Scaling-up Diverse Orthogonal Convolutional Networks by a Paraunitary Framework
Authors: Jiahao Su, Wonmin Byeon, Furong Huang
Date: 2021-09-29 00:00:00

Enforcing orthogonality in neural networks is an antidote for gradient vanishing/exploding problems, sensitivity to adversarial perturbation, and bounding generalization errors. However, many previous approaches are heuristic, and the orthogonality of convolutional layers is not systematically studied. Some of these designs are not exactly orthogonal, while others only consider standard convolutional layers and propose specific classes of their realizations. We propose a theoretical framework for orthogonal convolutional layers to address this problem, establishing the equivalence between diverse orthogonal convolutional layers in the spatial domain and the paraunitary systems in the spectral domain. Since a complete factorization exists for paraunitary systems, any orthogonal convolution layer can be parameterized as convolutions of spatial filters. Our framework endows high expressive power to various convolutional layers while maintaining their exact orthogonality. Furthermore, our layers are memory and computationally efficient for deep networks compared to previous designs. Our versatile framework, for the first time, enables the study of architecture designs for deep orthogonal networks, such as choices of skip connection, initialization, stride, and dilation. Consequently, we scale up orthogonal networks to deep architectures, including ResNet and ShuffleNet, substantially increasing the performance over their shallower counterparts. Finally, we show how to construct residual flows, a flow-based generative model that requires strict Lipschitzness, using our orthogonal networks.
Neuron-Enhanced Autoencoder based Collaborative filtering: Theory and Practice
Authors: Jicong Fan, Rui Chen, Chris Ding
Date: 2021-09-29 00:00:00
Tasks: Collaborative Filtering

This paper presents a novel recommendation method called neuron-enhanced autoencoder based collaborative filtering (NE-AECF). The method uses an additional neural network to enhance the reconstruction capability of autoencoder. Different from the main neural network implemented in a layer-wise manner, the additional neural network is implemented in an element-wise manner. They are trained simultaneously to construct an enhanced autoencoder of which the activation function in the output layer is learned adaptively to approximate possibly complicated response functions in real data. We provide theoretical analysis for NE-AECF to investigate the generalization ability of autoencoder and deep learning in collaborative filtering. We prove that the element-wise neural network is able to reduce the upper bound of the prediction error for the unknown ratings, the data sparsity is not problematic but useful, and the prediction performance is closely related to the difference between the number of users and the number of items.
Numerical results show that our NE-AECF has promising performance on a few benchmark datasets.
Synaptic Diversity in ANNs Can Facilitate Faster Learning
Authors: Martin Hofmann, Moritz F. P. Becker, Christian Tetzlaff, Patrick Mäder
Date: 2021-09-29 00:00:00
Tasks: Diversity

Various advancements in artificial neural networks (ANNs) are inspired by biological concepts, e.g., the artificial neuron, an efficient model of biological nerve cells demonstrating learning capabilities on large amounts of data. More recent inspirations with promising results are advanced regularization techniques, e.g., synaptic scaling, and backpropagation alternatives, e.g., Targetprop. While neurosciences continuously discover and better understand the mechanisms of biological neural networks (BNNs), new opportunities for a transfer of these concepts towards ANNs arise. However, only few concepts are readily applicable and improvements for ANNs are far from being guaranteed. In this paper, we focus on the inhomogeneous and dynamically changing structures of BNNs in contrast to mostly homogeneous and fix topologies of ANNs. More specifically, we transfer concepts of synaptic diversity, namely spontaneous synaptic remodeling, diversity in synaptic plasticity and multi-synaptic connectivity to ANNs. We observe ANNs enhanced by synaptic diversity concepts to learn faster, to predict with higher accuracy and to be more resilient to gradient inversion attacks. Our proposed methods are easily applicable to existing ANN topologies and are therefore supposed to stimulate an adaptation of and further research into these mechanisms.
T-WaveNet: A Tree-Structured Wavelet Neural Network for Time Series Signal Analysis
Authors: Minhao Liu, Ailing Zeng, Qiuxia Lai, Ruiyuan Gao, Min Li, Jing Qin, Qiang Xu
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4
Tasks: Activity Recognition, Representation Learning, Time Series, Time Series Analysis, Time Series Classification

Time series signal analysis plays an essential role in many applications, e.g., activity recognition and healthcare monitoring.  Recently, features extracted with deep neural networks (DNNs) have shown to be more effective than conventional hand-crafted ones. However, most existing solutions rely solely on the network to extract information carried in the raw signal, regardless of its inherent physical and statistical properties, leading to sub-optimal performance particularly under a limited amount of training data. In this work, we propose a novel tree-structured wavelet neural network for time series signal analysis, namely T-WaveNet, by taking advantage of an inherent property of various types of signals, known as the dominant frequency range. Specifically, with T-WaveNet, we first conduct frequency spectrum energy analysis of the signals to get a set of dominant frequency subbands. Then, we construct a tree-structured network that iteratively decomposes the input signal into various frequency subbands with similar energies. Each node on the tree is built with an invertible neural network (INN) based wavelet transform unit. Such a disentangled representation learning method facilitates a more effective extraction of the discriminative features, as demonstrated by our extensive experiments on various real-life time series classification datasets.
Continual Learning of Neural Networks for Realtime Wireline Cable Position Inference
Authors: Jun Wang, Tianxiang Su
Date: 2021-09-29 00:00:00
Tasks: Continual Learning, Position

In the oil fields, Wireline cable is spooled onto a drum where computer vision techniques based on convolutional neural networks (CNNs) are applied to estimate the cable position in real time for automated spooling control. However, as new training data keeps arriving to continuously improve the network, the re-training procedure faces challenges. Online learning fashion with no memory to historical data leads to catastrophic forgetting. Meanwhile, saving all data will cause the disk space and training time to increase without bounds. In this paper, we proposed a method called the modified-REMIND (mREMIND) network. It is a replay-based continual learning method with longer memory to historical data and no memory overflow issues. Information of old data are kept for multiple iterations using a new dictionary update rule. Additionally, by dynamically partitioning the dataset, the method can be applied on devices with limited memory. In our experiments, we compared the proposed method with multiple state-of-the-art continual learning methods and the mREMIND network outperformed others both in accuracy and in disk space usage.
State-Only Imitation Learning by Trajectory Distribution Matching
Authors: Damian Boborzi, Christoph-Nikolas Straehle, Jens Stefan Buchner, Lars Mikelsons
Date: 2021-09-29 00:00:00
Tasks: continuous-control, Continuous Control, Imitation Learning

The best performing state-only imitation learning approaches are based on adversarial imitation learning. The main drawback, however, is that adversarial training is often unstable and lacks a reliable convergence estimator. When the true environment reward is unknown and cannot be used to select the best-performing model, this can result in bad real-world policy performance. We propose a non-adversarial learning-from-observations approach, with an interpretable convergence and performance metric. 
 
Our training objective minimizes the Kulback-Leibler divergence between the policy and expert state transition trajectories which can be optimized in a non-adversarial fashion. For this, additional density models estimate the expert state transition distribution and the environment's forward and backward dynamics. We demonstrate the effectiveness of our approach on well-known continuous control environments, where our method can generalize to expert performance. We demonstrate that our method and loss are better suited to select the best-performing policy compared to objectives from adversarial methods by being competitive to or outperforming the state-of-the-art learning-from-observation approach in these environments.
Personalized Neural Architecture Search for Federated Learning
Authors: Minh Hoang, Carl Kingsford
Date: 2021-09-29 00:00:00
Tasks: Federated Learning, Neural Architecture Search

Federated Learning (FL) is a recently proposed learning paradigm for decentralized devices to collaboratively train a predictive model without exchanging private data. Existing FL frameworks, however, assume a one-size-fit-all model architecture to be collectively trained by local devices, which is determined prior to observing their data. Even with good engineering acumen, this often falls apart when local tasks are different and require diverging choices of architecture modelling to learn effectively. This motivates us to develop a novel personalized neural architecture search (NAS) algorithm for FL. Our algorithm, FedPNAS, learns a base architecture that can be structurally personalized for quick adaptation to each local task. We empirically show that FedPNAS significantly outperforms other NAS and FL benchmarks on several real-world datasets.
DeSKO: Stability-Assured Robust Control with a Deep Stochastic Koopman Operator
Authors: Minghao Han, Jacob Euler-Rolle, Robert K. Katzschmann
Date: 2021-09-29 00:00:00
Proceeding: ICLR 2022 4

Koopman operator theory linearly describes nonlinear dynamical systems in high-dimensional functional space, this facilitates the application of linear control methods to nonlinear systems. However, the Koopman operator does not account for uncertainty in dynamical systems, leading to fragile control performance in real-world applications.
We propose using a deep stochastic Koopman operator (DeSKO) model in a robust learning control framework to guarantee stability. The DeSKO model can learn the uncertainty contained in the dynamical system and infer a distribution of observables. We use the inferred distribution to design a robust and stabilizing closed-loop controller for the dynamical system. Modeling and control experiments show that our presented framework is more robust and scalable than state-of-art deep Koopman operators and reinforcement learning methods on several advanced control benchmarks, including a soft robotic arm, a legged robot, and a biological gene regulatory network. Furthermore, we demonstrate that this method resists previously unseen uncertainties, such as external disturbances, with a magnitude of up to five times the maximum control input. Our approach opens up new possibilities in robustly managing internal or external uncertainty while controlling high-dimensional nonlinear systems in a learning framework.