pwc-archive/papers-with-abstracts huggingface dataset:
QNNVerifier: A Tool for Verifying Neural Networks using SMT-Based Model Checking
Authors: Xidan Song, Edoardo Manino, Luiz Sena, Erickson Alves, Eddie de Lima Filho, Iury Bessa, Mikel Lujan, Lucas Cordeiro
Date: 2021-11-25 00:00:00
Tasks: Quantization

QNNVerifier is the first open-source tool for verifying implementations of neural networks that takes into account the finite word-length (i.e. quantization) of their operands. The novel support for quantization is achieved by employing state-of-the-art software model checking (SMC) techniques. It translates the implementation of neural networks to a decidable fragment of first-order logic based on satisfiability modulo theories (SMT). The effects of fixed- and floating-point operations are represented through direct implementations given a hardware-determined precision. Furthermore, QNNVerifier allows to specify bespoke safety properties and verify the resulting model with different verification strategies (incremental and k-induction) and SMT solvers. Finally, QNNVerifier is the first tool that combines invariant inference via interval analysis and discretization of non-linear activation functions to speed up the verification of neural networks by orders of magnitude. A video presentation of QNNVerifier is available at https://youtu.be/7jMgOL41zTY
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Authors: Matthias Perkonigg, Johannes Hofmanninger, Christian Herold, Helmut Prosch, Georg Langs
Date: 2021-11-25 00:00:00
Tasks: Active Learning, Age Estimation, Cardiac Segmentation, Continual Learning, Lung Nodule Detection

Machine learning in medical imaging during clinical routine is impaired by changes in scanner protocols, hardware, or policies resulting in a heterogeneous set of acquisition settings. When training a deep learning model on an initial static training set, model performance and reliability suffer from changes of acquisition characteristics as data and targets may become inconsistent. Continual learning can help to adapt models to the changing environment by training on a continuous data stream. However, continual manual expert labelling of medical imaging requires substantial effort. Thus, ways to use labelling resources efficiently on a well chosen sub-set of new examples is necessary to render this strategy feasible. Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting. The approach automatically recognizes shifts in image acquisition characteristics - new domains -, selects optimal examples for labelling and adapts training accordingly. Labelling is subject to a limited budget, resembling typical real world scenarios. To demonstrate generalizability, we evaluate the effectiveness of our method on three tasks: cardiac segmentation, lung nodule detection and brain age estimation. Results show that the proposed approach outperforms other active learning methods, while effectively counteracting catastrophic forgetting.
Toward an Idiomatic Framework for Cognitive Robotics
Authors: Malte R. Damgaard, Rasmus Pedersen, Thomas Bak
Date: 2021-11-25 00:00:00

Inspired by the "Cognitive Hour-glass" model presented in https://doi.org/10.1515/jagi-2016-0001, we propose a new framework for developing cognitive architectures aimed at cognitive robotics. The purpose of the proposed framework is foremost to ease the development of cognitive architectures by encouraging and mitigating cooperation and re-use of existing results. This is done by proposing a framework dividing the development of cognitive architectures into a series of layers that can be considered partly in isolation, and some of which directly relate to other research fields. Finally, we give introductions to and review some topics essential to the proposed framework.
Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation
Authors: Guillermo Jimenez-Perez, Juan Acosta, Alejandro Alcaine, Oscar Camara
Date: 2021-11-25 00:00:00
Tasks: Data Augmentation, Rhythm, Synthetic Data Generation

Obtaining per-beat information is a key task in the analysis of cardiac electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on ECG-based measurements. Those measurements, however, are costly to produce, especially in recordings that change throughout long periods of time. However, existing annotated databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves has two main contributions. First, a pseudo-synthetic data generation algorithm was developed, based in probabilistically composing ECG traces given "pools" of fundamental segments, as cropped from the original databases, and a set of rules for their arrangement into coherent synthetic traces. The generation of conditions is controlled by imposing expert knowledge on the generated trace, which increases the input variability for training the model. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples. The best performing model obtained an $F_1$-score of 99.38\% and a delineation error of $2.19 \pm 17.73$ ms and $4.45 \pm 18.32$ ms for all wave's fiducials (onsets and offsets, respectively), as averaged across the P, QRS and T waves for three distinct freely available databases. The excellent results were obtained despite the heterogeneous characteristics of the tested databases, in terms of lead configurations (Holter, 12-lead), sampling frequencies ($250$, $500$ and $2,000$ Hz) and represented pathophysiologies (e.g., different types of arrhythmias, sinus rhythm with structural heart disease), hinting at its generalization capabilities, while outperforming current state-of-the-art delineation approaches.
Learning Algebraic Representation for Systematic Generalization in Abstract Reasoning
Authors: Chi Zhang, Sirui Xie, Baoxiong Jia, Ying Nian Wu, Song-Chun Zhu, Yixin Zhu
Date: 2021-11-25 00:00:00
Tasks: Abstract Algebra, Systematic Generalization

Is intelligence realized by connectionist or classicist? While connectionist approaches have achieved superhuman performance, there has been growing evidence that such task-specific superiority is particularly fragile in systematic generalization. This observation lies in the central debate between connectionist and classicist, wherein the latter continually advocates an algebraic treatment in cognitive architectures. In this work, we follow the classicist's call and propose a hybrid approach to improve systematic generalization in reasoning. Specifically, we showcase a prototype with algebraic representation for the abstract spatial-temporal reasoning task of Raven's Progressive Matrices (RPM) and present the ALgebra-Aware Neuro-Semi-Symbolic (ALANS) learner. The ALANS learner is motivated by abstract algebra and the representation theory. It consists of a neural visual perception frontend and an algebraic abstract reasoning backend: the frontend summarizes the visual information from object-based representation, while the backend transforms it into an algebraic structure and induces the hidden operator on the fly. The induced operator is later executed to predict the answer's representation, and the choice most similar to the prediction is selected as the solution. Extensive experiments show that by incorporating an algebraic treatment, the ALANS learner outperforms various pure connectionist models in domains requiring systematic generalization. We further show the generative nature of the learned algebraic representation; it can be decoded by isomorphism to generate an answer.
A-Muze-Net: Music Generation by Composing the Harmony based on the Generated Melody
Authors: Or Goren, Eliya Nachmani, Lior Wolf
Date: 2021-11-25 00:00:00
Tasks: Music Generation

We present a method for the generation of Midi files of piano music. The method models the right and left hands using two networks, where the left hand is conditioned on the right hand. This way, the melody is generated before the harmony. The Midi is represented in a way that is invariant to the musical scale, and the melody is represented, for the purpose of conditioning the harmony, by the content of each bar, viewed as a chord. Finally, notes are added randomly, based on this chord representation, in order to enrich the generated audio. Our experiments show a significant improvement over the state of the art for training on such datasets, and demonstrate the contribution of each of the novel components.
Observing Interventions: A logic for thinking about experiments
Authors: Fausto Barbero, Katrin Schulz, Fernando R. Velázquez-Quesada, Kaibo Xie
Date: 2021-11-25 00:00:00
Tasks: Epistemic Reasoning

This paper makes a first step towards a logic of learning from experiments. For this, we investigate formal frameworks for modeling the interaction of causal and (qualitative) epistemic reasoning. Crucial for our approach is the idea that the notion of an intervention can be used as a formal expression of a (real or hypothetical) experiment. In a first step we extend the well-known causal models with a simple Hintikka-style representation of the epistemic state of an agent. In the resulting setting, one can talk not only about the knowledge of an agent about the values of variables and how interventions affect them, but also about knowledge update. The resulting logic can model reasoning about thought experiments. However, it is unable to account for learning from experiments, which is clearly brought out by the fact that it validates the no learning principle for interventions. Therefore, in a second step, we implement a more complex notion of knowledge that allows an agent to observe (measure) certain variables when an experiment is carried out. This extended system does allow for learning from experiments. For all the proposed logical systems, we provide a sound and complete axiomatization.
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Authors: Haitong Ma, Changliu Liu, Shengbo Eben Li, Sifa Zheng, Wenchao Sun, Jianyu Chen
Date: 2021-11-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough data and prior model about the dangerous region? Existing methods mostly use the posterior penalty for dangerous actions, which means that the agent is not penalized until experiencing danger. This fact causes that the agent cannot learn a zero-violation policy even after convergence. Otherwise, it would not receive any penalty and lose the knowledge about danger. In this paper, we propose the safe set actor-critic (SSAC) algorithm, which confines the policy update using safety-oriented energy functions, or the safety indexes. The safety index is designed to increase rapidly for potentially dangerous actions, which allows us to locate the safe set on the action space, or the control safe set. Therefore, we can identify the dangerous actions prior to taking them, and further obtain a zero constraint-violation policy after convergence.We claim that we can learn the energy function in a model-free manner similar to learning a value function. By using the energy function transition as the constraint objective, we formulate a constrained RL problem. We prove that our Lagrangian-based solutions make sure that the learned policy will converge to the constrained optimum under some assumptions. The proposed algorithm is evaluated on both the complex simulation environments and a hardware-in-loop (HIL) experiment with a real controller from the autonomous vehicle. Experimental results suggest that the converged policy in all environments achieves zero constraint violation and comparable performance with model-based baselines.
AutoHEnsGNN: Winning Solution to AutoGraph Challenge for KDD Cup 2020
Authors: Jin Xu, Mingjian Chen, Jianqiang Huang, Xingyuan Tang, Ke Hu, Jian Li, Jia Cheng, Jun Lei
Date: 2021-11-25 00:00:00
Tasks: Graph Classification, Node Classification

Graph Neural Networks (GNNs) have become increasingly popular and achieved impressive results in many graph-based applications. However, extensive manual work and domain knowledge are required to design effective architectures, and the results of GNN models have high variance with different training setups, which limits the application of existing GNN models. In this paper, we present AutoHEnsGNN, a framework to build effective and robust models for graph tasks without any human intervention. AutoHEnsGNN won first place in the AutoGraph Challenge for KDD Cup 2020, and achieved the best rank score of five real-life datasets in the final phase. Given a task, AutoHEnsGNN first applies a fast proxy evaluation to automatically select a pool of promising GNN models. Then it builds a hierarchical ensemble framework: 1) We propose graph self-ensemble (GSE), which can reduce the variance of weight initialization and efficiently exploit the information of local and global neighborhoods; 2) Based on GSE, a weighted ensemble of different types of GNN models is used to effectively learn more discriminative node representations. To efficiently search the architectures and ensemble weights, we propose AutoHEnsGNN$_{\text{Gradient}}$, which treats the architectures and ensemble weights as architecture parameters and uses gradient-based architecture search to obtain optimal configurations, and AutoHEnsGNN$_{\text{Adaptive}}$, which can adaptively adjust the ensemble weight based on the model accuracy. Extensive experiments on node classification, graph classification, edge prediction and KDD Cup challenge demonstrate the effectiveness and generality of AutoHEnsGNN
Reliable Graph Neural Networks for Drug Discovery Under Distributional Shift
Authors: Kehang Han, Balaji Lakshminarayanan, Jeremiah Liu
Date: 2021-11-25 00:00:00
Tasks: Drug Discovery

The concern of overconfident mis-predictions under distributional shift demands extensive reliability research on Graph Neural Networks used in critical tasks in drug discovery. Here we first introduce CardioTox, a real-world benchmark on drug cardio-toxicity to facilitate such efforts. Our exploratory study shows overconfident mis-predictions are often distant from training data. That leads us to develop distance-aware GNNs: GNN-SNGP. Through evaluation on CardioTox and three established benchmarks, we demonstrate GNN-SNGP's effectiveness in increasing distance-awareness, reducing overconfident mis-predictions and making better calibrated predictions without sacrificing accuracy performance. Our ablation study further reveals the representation learned by GNN-SNGP improves distance-preservation over its base architecture and is one major factor for improvements.
Unbiased Pairwise Learning to Rank in Recommender Systems
Authors: Yi Ren, Hongyan Tang, Siwen Zhu
Date: 2021-11-25 00:00:00
Tasks: Attribute, Learning-To-Rank, Position, Recommendation Systems

Nowadays, recommender systems already impact almost every facet of peoples lives. To provide personalized high quality recommendation results, conventional systems usually train pointwise rankers to predict the absolute value of objectives and leverage a distinct shallow tower to estimate and alleviate the impact of position bias. However, with such a training paradigm, the optimization target differs a lot from the ranking metrics valuing the relative order of top ranked items rather than the prediction precision of each item. Moreover, as the existing system tends to recommend more relevant items at higher positions, it is difficult for the shallow tower based methods to precisely attribute the user feedback to the impact of position or relevance. Therefore, there exists an exciting opportunity for us to get enhanced performance if we manage to solve the aforementioned issues. Unbiased learning to rank algorithms, which are verified to model the relative relevance accurately based on noisy feedback, are appealing candidates and have already been applied in many applications with single categorical labels, such as user click signals. Nevertheless, the existing unbiased LTR methods cannot properly handle multiple feedback incorporating both categorical and continuous labels. Accordingly, we design a novel unbiased LTR algorithm to tackle the challenges, which innovatively models position bias in the pairwise fashion and introduces the pairwise trust bias to separate the position bias, trust bias, and user relevance explicitly. Experiment results on public benchmark datasets and internal live traffic show the superior results of the proposed method for both categorical and continuous labels.
ContourletNet: A Generalized Rain Removal Architecture Using Multi-Direction Hierarchical Representation
Authors: Wei-Ting Chen, Cheng-Che Tsai, Hao-Yu Fang, I-Hsiang Chen, Jian-Jiun Ding, Sy-Yen Kuo
Date: 2021-11-25 00:00:00
Tasks: Rain Removal

Images acquired from rainy scenes usually suffer from bad visibility which may damage the performance of computer vision applications. The rainy scenarios can be categorized into two classes: moderate rain and heavy rain scenes. Moderate rain scene mainly consists of rain streaks while heavy rain scene contains both rain streaks and the veiling effect (similar to haze). Although existing methods have achieved excellent performance on these two cases individually, it still lacks a general architecture to address both heavy rain and moderate rain scenarios effectively. In this paper, we construct a hierarchical multi-direction representation network by using the contourlet transform (CT) to address both moderate rain and heavy rain scenarios. The CT divides the image into the multi-direction subbands (MS) and the semantic subband (SS). First, the rain streak information is retrieved to the MS based on the multi-orientation property of the CT. Second, a hierarchical architecture is proposed to reconstruct the background information including damaged semantic information and the veiling effect in the SS. Last, the multi-level subband discriminator with the feedback error map is proposed. By this module, all subbands can be well optimized. This is the first architecture that can address both of the two scenarios effectively. The code is available in https://github.com/cctakaet/ContourletNet-BMVC2021.
Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity
Authors: Bernardo Aquino, Arash Rahnama, Peter Seiler, Lizhen Lin, Vijay Gupta
Date: 2021-11-25 00:00:00

Adversarial examples can easily degrade the classification performance in neural networks. Empirical methods for promoting robustness to such examples have been proposed, but often lack both analytical insights and formal guarantees. Recently, some robustness certificates have appeared in the literature based on system theoretic notions. This work proposes an incremental dissipativity-based robustness certificate for neural networks in the form of a linear matrix inequality for each layer. We also propose an equivalent spectral norm bound for this certificate which is scalable to neural networks with multiple layers. We demonstrate the improved performance against adversarial attacks on a feed-forward neural network trained on MNIST and an Alexnet trained using CIFAR-10.
CIRCLE: Convolutional Implicit Reconstruction and Completion for Large-scale Indoor Scene
Authors: Haoxiang Chen, Jiahui Huang, Tai-Jiang Mu, Shi-Min Hu
Date: 2021-11-25 00:00:00

We present CIRCLE, a framework for large-scale scene completion and geometric refinement based on local implicit signed distance functions. It is based on an end-to-end sparse convolutional network, CircNet, that jointly models local geometric details and global scene structural contexts, allowing it to preserve fine-grained object detail while recovering missing regions commonly arising in traditional 3D scene data. A novel differentiable rendering module enables test-time refinement for better reconstruction quality. Extensive experiments on both real-world and synthetic datasets show that our concise framework is efficient and effective, achieving better reconstruction quality than the closest competitor while being 10-50x faster.
Recommending Multiple Positive Citations for Manuscript via Content-Dependent Modeling and Multi-Positive Triplet
Authors: Yang Zhang, Qiang Ma
Date: 2021-11-25 00:00:00
Tasks: Citation Recommendation, Triplet

Considering the rapidly increasing number of academic papers, searching for and citing appropriate references has become a non-trial task during the wiring of papers. Recommending a handful of candidate papers to a manuscript before publication could ease the burden of the authors, and help the reviewers to check the completeness of the cited resources. Conventional approaches on citation recommendation generally consider recommending one ground-truth citation for a query context from an input manuscript, but lack of consideration on co-citation recommendations. However, a piece of context often needs to be supported by two or more co-citation pairs. Here, we propose a novel scientific paper modeling for citation recommendations, namely Multi-Positive BERT Model for Citation Recommendation (MP-BERT4CR), complied with a series of Multi-Positive Triplet objectives to recommend multiple positive citations for a query context. The proposed approach has the following advantages: First, the proposed multi-positive objectives are effective to recommend multiple positive candidates. Second, we adopt noise distributions which are built based on the historical co-citation frequencies, so that MP-BERT4CR is not only effective on recommending high-frequent co-citation pairs; but also the performances on retrieving the low-frequent ones are significantly improved. Third, we propose a dynamic context sampling strategy which captures the ``macro-scoped'' citing intents from a manuscript and empowers the citation embeddings to be content-dependent, which allow the algorithm to further improve the performances. Single and multiple positive recommendation experiments testified that MP-BERT4CR delivered significant improvements. In addition, MP-BERT4CR are also effective in retrieving the full list of co-citations, and historically low-frequent co-citation pairs compared with the prior works.