pwc-archive/papers-with-abstracts huggingface dataset:
A Marriage between Adversarial Team Games and 2-player Games: Enabling Abstractions, No-regret Learning, and Subgame Solving
Authors: Luca Carminati, Federico Cacciamani, Marco Ciccone, Nicola Gatti
Date: 2022-06-18 00:00:00

\emph{Ex ante} correlation is becoming the mainstream approach for \emph{sequential adversarial team games}, where a team of players faces another team in a zero-sum game. It is known that team members' asymmetric information makes both equilibrium computation \textsf{APX}-hard and team's strategies not directly representable on the game tree. This latter issue prevents the adoption of successful tools for huge 2-player zero-sum games such as, \emph{e.g.}, abstractions, no-regret learning, and subgame solving. This work shows that we can recover from this weakness by bridging the gap between sequential adversarial team games and 2-player games. In particular, we propose a new, suitable game representation that we call \emph{team-public-information}, in which a team is represented as a single coordinator who only knows information common to the whole team and prescribes to each member an action for any possible private state. The resulting representation is highly \emph{explainable}, being a 2-player tree in which the team's strategies are behavioral with a direct interpretation and more expressive than the original extensive form when designing abstractions. Furthermore, we prove payoff equivalence of our representation, and we provide techniques that, starting directly from the extensive form, generate dramatically more compact representations without information loss. Finally, we experimentally evaluate our techniques when applied to a standard testbed, comparing their performance with the current state of the art.
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
Authors: Peibei Cao, Chenyang Le, Yuming Fang, Kede Ma
Date: 2022-06-18 00:00:00
Tasks: Multi-Exposure Image Fusion, SSIM, Tone Mapping

With the increasing popularity and accessibility of high dynamic range (HDR) photography, tone mapping operators (TMOs) for dynamic range compression are practically demanding. In this paper, we develop a two-stage neural network-based TMO that is self-calibrated and perceptually optimized. In Stage one, motivated by the physiology of the early stages of the human visual system, we first decompose an HDR image into a normalized Laplacian pyramid. We then use two lightweight deep neural networks (DNNs), taking the normalized representation as input and estimating the Laplacian pyramid of the corresponding LDR image. We optimize the tone mapping network by minimizing the normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with human judgments of tone-mapped image quality. In Stage two, the input HDR image is self-calibrated to compute the final LDR image. We feed the same HDR image but rescaled with different maximum luminances to the learned tone mapping network, and generate a pseudo-multi-exposure image stack with different detail visibility and color saturation. We then train another lightweight DNN to fuse the LDR image stack into a desired LDR image by maximizing a variant of the structural similarity index for multi-exposure image fusion (MEF-SSIM), which has been proven perceptually relevant to fused image quality. The proposed self-calibration mechanism through MEF enables our TMO to accept uncalibrated HDR images, while being physiology-driven. Extensive experiments show that our method produces images with consistently better visual quality. Additionally, since our method builds upon three lightweight DNNs, it is among the fastest local TMOs.
Replacing Labeled Real-image Datasets with Auto-generated Contours
Authors: Hirokatsu Kataoka, Ryo Hayamizu, Ryosuke Yamada, Kodai Nakashima, Sora Takashima, Xinyu Zhang, Edgar Josafat Martinez-Noriega, Nakamasa Inoue, Rio Yokota
Date: 2022-06-18 00:00:00
Proceeding: CVPR 2022 1

In the present work, we show that the performance of formula-driven supervised learning (FDSL) can match or even exceed that of ImageNet-21k without the use of real images, human-, and self-supervision during the pre-training of Vision Transformers (ViTs). For example, ViT-Base pre-trained on ImageNet-21k shows 81.8% top-1 accuracy when fine-tuned on ImageNet-1k and FDSL shows 82.7% top-1 accuracy when pre-trained under the same conditions (number of images, hyperparameters, and number of epochs). Images generated by formulas avoid the privacy/copyright issues, labeling cost and errors, and biases that real images suffer from, and thus have tremendous potential for pre-training general models. To understand the performance of the synthetic images, we tested two hypotheses, namely (i) object contours are what matter in FDSL datasets and (ii) increased number of parameters to create labels affects performance improvement in FDSL pre-training. To test the former hypothesis, we constructed a dataset that consisted of simple object contour combinations. We found that this dataset can match the performance of fractals. For the latter hypothesis, we found that increasing the difficulty of the pre-training task generally leads to better fine-tuning accuracy.
VReBERT: A Simple and Flexible Transformer for Visual Relationship Detection
Authors: Yu Cui, Moshiur Farazi
Date: 2022-06-18 00:00:00
Tasks: Object, Relationship Detection, Visual Relationship Detection

Visual Relationship Detection (VRD) impels a computer vision model to 'see' beyond an individual object instance and 'understand' how different objects in a scene are related. The traditional way of VRD is first to detect objects in an image and then separately predict the relationship between the detected object instances. Such a disjoint approach is prone to predict redundant relationship tags (i.e., predicate) between the same object pair with similar semantic meaning, or incorrect ones that have a similar meaning to the ground truth but are semantically incorrect. To remedy this, we propose to jointly train a VRD model with visual object features and semantic relationship features. To this end, we propose VReBERT, a BERT-like transformer model for Visual Relationship Detection with a multi-stage training strategy to jointly process visual and semantic features. We show that our simple BERT-like model is able to outperform the state-of-the-art VRD models in predicate prediction. Furthermore, we show that by using the pre-trained VReBERT model, our model pushes the state-of-the-art zero-shot predicate prediction by a significant margin (+8.49 R@50 and +8.99 R@100).
Score-Guided Intermediate Layer Optimization: Fast Langevin Mixing for Inverse Problems
Authors: Giannis Daras, Yuval Dagan, Alexandros G. Dimakis, Constantinos Daskalakis
Date: 2022-06-18 00:00:00

We prove fast mixing and characterize the stationary distribution of the Langevin Algorithm for inverting random weighted DNN generators. This result extends the work of Hand and Voroninski from efficient inversion to efficient posterior sampling. In practice, to allow for increased expressivity, we propose to do posterior sampling in the latent space of a pre-trained generative model. To achieve that, we train a score-based model in the latent space of a StyleGAN-2 and we use it to solve inverse problems. Our framework, Score-Guided Intermediate Layer Optimization (SGILO), extends prior work by replacing the sparsity regularization with a generative prior in the intermediate layer. Experimentally, we obtain significant improvements over the previous state-of-the-art, especially in the low measurement regime.
From Understanding Genetic Drift to a Smart-Restart Mechanism for Estimation-of-Distribution Algorithms
Authors: Weijie Zheng, Benjamin Doerr
Date: 2022-06-18 00:00:00
Tasks: Combinatorial Optimization

Estimation-of-distribution algorithms (EDAs) are optimization algorithms that learn a distribution on the search space from which good solutions can be sampled easily. A key parameter of most EDAs is the sample size (population size). If the population size is too small, the update of the probabilistic model builds on few samples, leading to the undesired effect of genetic drift. Too large population sizes avoid genetic drift, but slow down the process. Building on a recent quantitative analysis of how the population size leads to genetic drift, we design a smart-restart mechanism for EDAs. By stopping runs when the risk for genetic drift is high, it automatically runs the EDA in good parameter regimes. Via a mathematical runtime analysis, we prove a general performance guarantee for this smart-restart scheme. This in particular shows that in many situations where the optimal (problem-specific) parameter values are known, the restart scheme automatically finds these, leading to the asymptotically optimal performance. We also conduct an extensive experimental analysis. On four classic benchmark problems, we clearly observe the critical influence of the population size on the performance, and we find that the smart-restart scheme leads to a performance close to the one obtainable with optimal parameter values. Our results also show that previous theory-based suggestions for the optimal population size can be far from the optimal ones, leading to a performance clearly inferior to the one obtained via the smart-restart scheme. We also conduct experiments with PBIL (cross-entropy algorithm) on two combinatorial optimization problems from the literature, the max-cut problem and the bipartition problem. Again, we observe that the smart-restart mechanism finds much better values for the population size than those suggested in the literature, leading to a much better performance.
Comment on Transferability and Input Transformation with Additive Noise
Authors: Hoki Kim, Jinseong Park, Jaewook Lee
Date: 2022-06-18 00:00:00

Adversarial attacks have verified the existence of the vulnerability of neural networks. By adding small perturbations to a benign example, adversarial attacks successfully generate adversarial examples that lead misclassification of deep learning models. More importantly, an adversarial example generated from a specific model can also deceive other models without modification. We call this phenomenon ``transferability". Here, we analyze the relationship between transferability and input transformation with additive noise by mathematically proving that the modified optimization can produce more transferable adversarial examples.
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
Authors: Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva, Georgios Chochlakis, Mohammad Rostami, Jesse Thomason
Date: 2022-06-18 00:00:00
Tasks: Continual Learning, Transfer Learning

Current state-of-the-art vision-and-language models are evaluated on tasks either individually or in a multi-task setting, overlooking the challenges of continually learning (CL) tasks as they arrive. Existing CL benchmarks have facilitated research on task adaptation and mitigating "catastrophic forgetting", but are limited to vision-only and language-only tasks. We present CLiMB, a benchmark to study the challenge of learning multimodal tasks in a CL setting, and to systematically evaluate how upstream continual learning can rapidly generalize to new multimodal and unimodal tasks. CLiMB includes implementations of several CL algorithms and a modified Vision-Language Transformer (ViLT) model that can be deployed on both multimodal and unimodal tasks. We find that common CL methods can help mitigate forgetting during multimodal task learning, but do not enable cross-task knowledge transfer. We envision that CLiMB will facilitate research on a new class of CL algorithms for this challenging multimodal setting.
Augmented Imagefication: A Data-driven Fault Detection Method for Aircraft Air Data Sensors
Authors: Hang Zhao, Jinyi Ma, Zhongzhi Li, Yiqun Dong, Jianliang Ai
Date: 2022-06-18 00:00:00
Tasks: Fault Detection

In this paper, a novel data-driven approach named Augmented Imagefication for Fault detection (FD) of aircraft air data sensors (ADS) is proposed. Exemplifying the FD problem of aircraft air data sensors, an online FD scheme on edge device based on deep neural network (DNN) is developed. First, the aircraft inertial reference unit measurements is adopted as equivalent inputs, which is scalable to different aircraft/flight cases. Data associated with 6 different aircraft/flight conditions are collected to provide diversity (scalability) in the training/testing database. Then Augmented Imagefication is proposed for the DNN-based prediction of flying conditions. The raw data are reshaped as a grayscale image for convolutional operation, and the necessity of augmentation is analyzed and pointed out. Different kinds of augmented method, i.e. Flip, Repeat, Tile and their combinations are discussed, the result shows that the All Repeat operation in both axes of image matrix leads to the best performance of DNN. The interpretability of DNN is studied based on Grad-CAM, which provide a better understanding and further solidifies the robustness of DNN. Next the DNN model, VGG-16 with augmented imagefication data is optimized for mobile hardware deployment. After pruning of DNN, a lightweight model (98.79% smaller than original VGG-16) with high accuracy (slightly up by 0.27%) and fast speed (time delay is reduced by 87.54%) is obtained. And the hyperparameters optimization of DNN based on TPE is implemented and the best combination of hyperparameters is determined (learning rate 0.001, iterative epochs 600, and batch size 100 yields the highest accuracy at 0.987). Finally, a online FD deployment based on edge device, Jetson Nano, is developed and the real time monitoring of aircraft is achieved. We believe that this method is instructive for addressing the FD problems in other similar fields.
Towards Better Selective Classification
Authors: Leo Feng, Mohamed Osama Ahmed, Hossein Hajimirsadeghi, Amir Abdi
Date: 2022-06-17 00:00:00
Tasks: Classification

We tackle the problem of Selective Classification where the objective is to achieve the best performance on a predetermined ratio (coverage) of the dataset. Recent state-of-the-art selective methods come with architectural changes either via introducing a separate selection head or an extra abstention logit. In this paper, we challenge the aforementioned methods. The results suggest that the superior performance of state-of-the-art methods is owed to training a more generalizable classifier rather than their proposed selection mechanisms. We argue that the best performing selection mechanism should instead be rooted in the classifier itself. Our proposed selection strategy uses the classification scores and achieves better results by a significant margin, consistently, across all coverages and all datasets, without any added compute cost. Furthermore, inspired by semi-supervised learning, we propose an entropy-based regularizer that improves the performance of selective classification methods. Our proposed selection mechanism with the proposed entropy-based regularizer achieves new state-of-the-art results.
Designing MacPherson Suspension Architectures using Bayesian Optimization
Authors: Sinnu Susan Thomas, Jacopo Palandri, Mohsen Lakehal-ayat, Punarjay Chakravarty, Friedrich Wolf-Monheim, Matthew B. Blaschko
Date: 2022-06-17 00:00:00
Tasks: Bayesian Optimization

Engineering design is traditionally performed by hand: an expert makes design proposals based on past experience, and these proposals are then tested for compliance with certain target specifications. Testing for compliance is performed first by computer simulation using what is called a discipline model. Such a model can be implemented by a finite element analysis, multibody systems approach, etc. Designs passing this simulation are then considered for physical prototyping. The overall process may take months, and is a significant cost in practice. We have developed a Bayesian optimization system for partially automating this process by directly optimizing compliance with the target specification with respect to the design parameters. The proposed method is a general framework for computing a generalized inverse of a high-dimensional non-linear function that does not require e.g. gradient information, which is often unavailable from discipline models. We furthermore develop a two-tier convergence criterion based on (i) convergence to a solution optimally satisfying all specified design criteria, or (ii) convergence to a minimum-norm solution. We demonstrate the proposed approach on a vehicle chassis design problem motivated by an industry setting using a state-of-the-art commercial discipline model. We show that the proposed approach is general, scalable, and efficient, and that the novel convergence criteria can be implemented straightforwardly based on existing concepts and subroutines in popular Bayesian optimization software packages.
Uniform and Modular Sequent Systems for Description Logics
Authors: Tim Lyon, Jonas Karge
Date: 2022-06-17 00:00:00

We introduce a framework that allows for the construction of sequent systems for expressive description logics extending ALC. Our framework not only covers a wide array of common description logics, but also allows for sequent systems to be obtained for extensions of description logics with special formulae that we call "role relational axioms." All sequent systems are sound, complete, and possess favorable properties such as height-preserving admissibility of common structural rules and height-preserving invertibility of rules.
Towards Efficient Active Learning of PDFA
Authors: Franz Mayr, Sergio Yovine, Federico Pan, Nicolas Basset, Thao Dang
Date: 2022-06-17 00:00:00
Tasks: Active Learning, Quantization

We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.
Actionable Guidance for High-Consequence AI Risk Management: Towards Standards Addressing AI Catastrophic Risks
Authors: Anthony M. Barrett, Dan Hendrycks, Jessica Newman, Brandie Nonnecke
Date: 2022-06-17 00:00:00
Tasks: Management

Artificial intelligence (AI) systems can provide many beneficial capabilities but also risks of adverse events. Some AI systems could present risks of events with very high or catastrophic consequences at societal scale. The US National Institute of Standards and Technology (NIST) has been developing the NIST Artificial Intelligence Risk Management Framework (AI RMF) as voluntary guidance on AI risk assessment and management for AI developers and others. For addressing risks of events with catastrophic consequences, NIST indicated a need to translate from high level principles to actionable risk management guidance. In this document, we provide detailed actionable-guidance recommendations focused on identifying and managing risks of events with very high or catastrophic consequences, intended as a risk management practices resource for NIST for AI RMF version 1.0 (released in January 2023), or for AI RMF users, or for other AI risk management guidance and standards as appropriate. We also provide our methodology for our recommendations. We provide actionable-guidance recommendations for AI RMF 1.0 on: identifying risks from potential unintended uses and misuses of AI systems; including catastrophic-risk factors within the scope of risk assessments and impact assessments; identifying and mitigating human rights harms; and reporting information on AI risk factors including catastrophic-risk factors. In addition, we provide recommendations on additional issues for a roadmap for later versions of the AI RMF or supplementary publications. These include: providing an AI RMF Profile with supplementary guidance for cutting-edge increasingly multi-purpose or general-purpose AI. We aim for this work to be a concrete risk-management practices contribution, and to stimulate constructive dialogue on how to address catastrophic risks and associated issues in AI standards.
KitBit: A New AI Model for Solving Intelligence Tests and Numerical Series
Authors: Víctor Corsino, José Manuel Gilpérez, Luis Herrera
Date: 2022-06-17 00:00:00

The resolution of intelligence tests, in particular numerical sequences, has been of great interest in the evaluation of AI systems. We present a new computational model called KitBit that uses a reduced set of algorithms and their combinations to build a predictive model that finds the underlying pattern in numerical sequences, such as those included in IQ tests and others of much greater complexity. We present the fundamentals of the model and its application in different cases. First, the system is tested on a set of number series used in IQ tests collected from various sources. Next, our model is successfully applied on the sequences used to evaluate the models reported in the literature. In both cases, the system is capable of solving these types of problems in less than a second using standard computing power. Finally, KitBit's algorithms have been applied for the first time to the complete set of entire sequences of the well-known OEIS database. We find a pattern in the form of a list of algorithms and predict the following terms in the largest number of series to date. These results demonstrate the potential of KitBit to solve complex problems that could be represented numerically.
Approximate Equivariance SO(3) Needlet Convolution
Authors: Kai Yi, Jialin Chen, Yu Guang Wang, Bingxin Zhou, Pietro Liò, Yanan Fan, Jan Hamann
Date: 2022-06-17 00:00:00
Tasks: Quantum Chemistry Regression

This paper develops a rotation-invariant needlet convolution for rotation group SO(3) to distill multiscale information of spherical signals. The spherical needlet transform is generalized from $\mathbb{S}^2$ onto the SO(3) group, which decomposes a spherical signal to approximate and detailed spectral coefficients by a set of tight framelet operators. The spherical signal during the decomposition and reconstruction achieves rotation invariance. Based on needlet transforms, we form a Needlet approximate Equivariance Spherical CNN (NES) with multiple SO(3) needlet convolutional layers. The network establishes a powerful tool to extract geometric-invariant features of spherical signals. The model allows sufficient network scalability with multi-resolution representation. A robust signal embedding is learned with wavelet shrinkage activation function, which filters out redundant high-pass representation while maintaining approximate rotation invariance. The NES achieves state-of-the-art performance for quantum chemistry regression and Cosmic Microwave Background (CMB) delensing reconstruction, which shows great potential for solving scientific challenges with high-resolution and multi-scale spherical signal representation.
The State of Sparse Training in Deep Reinforcement Learning
Authors: Laura Graesser, Utku Evci, Erich Elsen, Pablo Samuel Castro
Date: 2022-06-17 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The use of sparse neural networks has seen rapid growth in recent years, particularly in computer vision. Their appeal stems largely from the reduced number of parameters required to train and store, as well as in an increase in learning efficiency. Somewhat surprisingly, there have been very few efforts exploring their use in Deep Reinforcement Learning (DRL). In this work we perform a systematic investigation into applying a number of existing sparse training techniques on a variety of DRL agents and environments. Our results corroborate the findings from sparse training in the computer vision domain - sparse networks perform better than dense networks for the same parameter count - in the DRL domain. We provide detailed analyses on how the various components in DRL are affected by the use of sparse networks and conclude by suggesting promising avenues for improving the effectiveness of sparse training methods, as well as for advancing their use in DRL.