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Title: Formulating Robustness Against Unforeseen Attacks Abstract: Existing defenses against adversarial examples such as adversarial training typically assume that the adversary will conform to a specific or known threat model, such as $\ell_p$ perturbations within a fixed budget. In this paper, we focus on the scenar... |
Title: AGIC: Approximate Gradient Inversion Attack on Federated Learning Abstract: Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to compute a global model. Depending on the aggregation met... |
Title: Depth Estimation with Simplified Transformer Abstract: Transformer and its variants have shown state-of-the-art results in many vision tasks recently, ranging from image classification to dense prediction. Despite of their success, limited work has been reported on improving the model efficiency for deployment i... |
Title: Probabilistic Models for Manufacturing Lead Times Abstract: In this study, we utilize Gaussian processes, probabilistic neural network, natural gradient boosting, and quantile regression augmented gradient boosting to model lead times of laser manufacturing processes. We introduce probabilistic modelling in the ... |
Title: Analysing the Influence of Attack Configurations on the Reconstruction of Medical Images in Federated Learning Abstract: The idea of federated learning is to train deep neural network models collaboratively and share them with multiple participants without exposing their private training data to each other. This... |
Title: Visualization and Optimization Techniques for High Dimensional Parameter Spaces Abstract: High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimization... |
Title: An Online Ensemble Learning Model for Detecting Attacks in Wireless Sensor Networks Abstract: In today's modern world, the usage of technology is unavoidable and the rapid advances in the Internet and communication fields have resulted to expand the Wireless Sensor Network (WSN) technology. A huge number of sens... |
Title: Automatic Machine Learning for Multi-Receiver CNN Technology Classifiers Abstract: Convolutional Neural Networks (CNNs) are one of the most studied family of deep learning models for signal classification, including modulation, technology, detection, and identification. In this work, we focus on technology class... |
Title: A Neural Network-enhanced Reproducing Kernel Particle Method for Modeling Strain Localization Abstract: Modeling the localized intensive deformation in a damaged solid requires highly refined discretization for accurate prediction, which significantly increases the computational cost. Although adaptive model ref... |
Title: Noise-reducing attention cross fusion learning transformer for histological image classification of osteosarcoma Abstract: The degree of malignancy of osteosarcoma and its tendency to metastasize/spread mainly depend on the pathological grade (determined by observing the morphology of the tumor under a microscop... |
Title: An Extensive Data Processing Pipeline for MIMIC-IV Abstract: An increasing amount of research is being devoted to applying machine learning methods to electronic health record (EHR) data for various clinical tasks. This growing area of research has exposed the limitation of accessibility of EHR datasets for all,... |
Title: VPNets: Volume-preserving neural networks for learning source-free dynamics Abstract: We propose volume-preserving networks (VPNets) for learning unknown source-free dynamical systems using trajectory data. We propose three modules and combine them to obtain two network architectures, coined R-VPNet and LA-VPNet... |
Title: GenDR: A Generalized Differentiable Renderer Abstract: In this work, we present and study a generalized family of differentiable renderers. We discuss from scratch which components are necessary for differentiable rendering and formalize the requirements for each component. We instantiate our general differentia... |
Title: RoSA: A Robust Self-Aligned Framework for Node-Node Graph Contrastive Learning Abstract: Graph contrastive learning has gained significant progress recently. However, existing works have rarely explored non-aligned node-node contrasting. In this paper, we propose a novel graph contrastive learning method named R... |
Title: CATNet: Cross-event Attention-based Time-aware Network for Medical Event Prediction Abstract: Medical event prediction (MEP) is a fundamental task in the medical domain, which needs to predict medical events, including medications, diagnosis codes, laboratory tests, procedures, outcomes, and so on, according to ... |
Title: Goldilocks-curriculum Domain Randomization and Fractal Perlin Noise with Application to Sim2Real Pneumonia Lesion Detection Abstract: A computer-aided detection (CAD) system based on machine learning is expected to assist radiologists in making a diagnosis. It is desirable to build CAD systems for the various ty... |
Title: COVID-Net US-X: Enhanced Deep Neural Network for Detection of COVID-19 Patient Cases from Convex Ultrasound Imaging Through Extended Linear-Convex Ultrasound Augmentation Learning Abstract: As the global population continues to face significant negative impact by the on-going COVID-19 pandemic, there has been an... |
Title: H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness Abstract: The complex nature of real-world problems calls for heterogeneity in both machine learning (ML) models and hardware systems. The heterogeneity in ML models comes from multi-sensor perceiving and multi-... |
Title: Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations Abstract: Although deep neural networks have achieved state-of-the-art performance in various machine learning tasks, adversarial examples, constructed by adding small non-random perturbations to correctly clas... |
Title: One-Way Matching of Datasets with Low Rank Signals Abstract: We study one-way matching of a pair of datasets with low rank signals. Under a stylized model, we first derive information-theoretic limits of matching. We then show that linear assignment with projected data achieves fast rates of convergence and some... |
Title: Energy Minimization for Federated Asynchronous Learning on Battery-Powered Mobile Devices via Application Co-running Abstract: Energy is an essential, but often forgotten aspect in large-scale federated systems. As most of the research focuses on tackling computational and statistical heterogeneity from the mach... |
Title: Autonomous In-Situ Soundscape Augmentation via Joint Selection of Masker and Gain Abstract: The selection of maskers and playback gain levels in a soundscape augmentation system is crucial to its effectiveness in improving the overall acoustic comfort of a given environment. Traditionally, the selection of appro... |
Title: Framework for Behavioral Disorder Detection Using Machine Learning and Application of Virtual Cognitive Behavioral Therapy in COVID-19 Pandemic Abstract: In this modern world, people are becoming more self-centered and unsocial. On the other hand, people are stressed, becoming more anxious during COVID-19 pandem... |
Title: Fast Sampling of Diffusion Models with Exponential Integrator Abstract: The past few years have witnessed the great success of Diffusion models~(DMs) in generating high-fidelity samples in generative modeling tasks. A major limitation of the DM is its notoriously slow sampling procedure which normally requires h... |
Title: Unsupervised Reinforcement Learning for Transferable Manipulation Skill Discovery Abstract: Current reinforcement learning (RL) in robotics often experiences difficulty in generalizing to new downstream tasks due to the innate task-specific training paradigm. To alleviate it, unsupervised RL, a framework that pr... |
Title: Task Embedding Temporal Convolution Networks for Transfer Learning Problems in Renewable Power Time-Series Forecast Abstract: Task embeddings in multi-layer perceptrons for multi-task learning and inductive transfer learning in renewable power forecasts have recently been introduced. In many cases, this approach... |
Title: A study of tree-based methods and their combination Abstract: Tree-based methods are popular machine learning techniques used in various fields. In this work, we review their foundations and a general framework the importance sampled learning ensemble (ISLE) that accelerates their fitting process. Furthermore, w... |
Title: A Mixed-Domain Self-Attention Network for Multilabel Cardiac Irregularity Classification Using Reduced-Lead Electrocardiogram Abstract: Electrocardiogram(ECG) is commonly used to detect cardiac irregularities such as atrial fibrillation, bradycardia, and other irregular complexes. While previous studies have ach... |
Title: Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows Abstract: The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing el... |
Title: Learned Gradient of a Regularizer for Plug-and-Play Gradient Descent Abstract: The Plug-and-Play (PnP) framework allows integrating advanced image denoising priors into optimization algorithms, to efficiently solve a variety of image restoration tasks. The Plug-and-Play alternating direction method of multiplier... |
Title: Tailored Uncertainty Estimation for Deep Learning Systems Abstract: Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combinations of strengths and weaknesses, e.g., with respect to estima... |
Title: Cost Effective MLaaS Federation: A Combinatorial Reinforcement Learning Approach Abstract: With the advancement of deep learning techniques, major cloud providers and niche machine learning service providers start to offer their cloud-based machine learning tools, also known as machine learning as a service (MLa... |
Title: Making sense of violence risk predictions using clinical notes Abstract: Violence risk assessment in psychiatric institutions enables interventions to avoid violence incidents. Clinical notes written by practitioners and available in electronic health records (EHR) are valuable resources that are seldom used to ... |
Title: Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features Abstract: Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and wor... |
Title: Particle Swarm Optimization Based Demand Response Using Artificial Neural Network Based Load Prediction Abstract: In the present study, a Particle Swarm Optimization (PSO) based Demand Response (DR) model, using Artificial Neural Network (ANN) to predict load is proposed. The electrical load and climatological d... |
Title: Physical Deep Learning with Biologically Plausible Training Method Abstract: The ever-growing demand for further advances in artificial intelligence motivated research on unconventional computation based on analog physical devices. While such computation devices mimic brain-inspired analog information processing... |
Title: Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting Abstract: Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learnin... |
Title: Statistical applications of contrastive learning Abstract: The likelihood function plays a crucial role in statistical inference and experimental design. However, it is computationally intractable for several important classes of statistical models, including energy-based models and simulator-based models. Contr... |
Title: Machine Learning-Based GPS Multipath Detection Method Using Dual Antennas Abstract: In urban areas, global navigation satellite system (GNSS) signals are often reflected or blocked by buildings, thus resulting in large positioning errors. In this study, we proposed a machine learning approach for global position... |
Title: No Task Left Behind: Multi-Task Learning of Knowledge Tracing and Option Tracing for Better Student Assessment Abstract: Student assessment is one of the most fundamental tasks in the field of AI Education (AIEd). One of the most common approach to student assessment is Knowledge Tracing (KT), which evaluates a ... |
Title: Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs Abstract: On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. ... |
Title: Biologically-inspired neuronal adaptation improves learning in neural networks Abstract: Since humans still outperform artificial neural networks on many tasks, drawing inspiration from the brain may help to improve current machine learning algorithms. Contrastive Hebbian Learning (CHL) and Equilibrium Propagati... |
Title: Local Explanation of Dimensionality Reduction Abstract: Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant percentage of their origin... |
Title: Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling Abstract: Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due to the potential risks of adversaries par... |
Title: Exploration and Exploitation in Federated Learning to Exclude Clients with Poisoned Data Abstract: Federated Learning (FL) is one of the hot research topics, and it utilizes Machine Learning (ML) in a distributed manner without directly accessing private data on clients. However, FL faces many challenges, includ... |
Title: Data+Shift: Supporting visual investigation of data distribution shifts by data scientists Abstract: Machine learning on data streams is increasingly more present in multiple domains. However, there is often data distribution shift that can lead machine learning models to make incorrect decisions. While there ar... |
Title: Who will stay? Using Deep Learning to predict engagement of citizen scientists Abstract: Citizen science and machine learning should be considered for monitoring the coastal and ocean environment due to the scale of threats posed by climate change and the limited resources to fill knowledge gaps. Using data from... |
Title: Topological Data Analysis in Time Series: Temporal Filtration and Application to Single-Cell Genomics Abstract: The absence of a conventional association between the cell-cell cohabitation and its emergent dynamics into cliques during development has hindered our understanding of how cell populations proliferate... |
Title: A Collection of Quality Diversity Optimization Problems Derived from Hyperparameter Optimization of Machine Learning Models Abstract: The goal of Quality Diversity Optimization is to generate a collection of diverse yet high-performing solutions to a given problem at hand. Typical benchmark problems are, for exa... |
Title: Multimodal Transformer-based Model for Buchwald-Hartwig and Suzuki-Miyaura Reaction Yield Prediction Abstract: Predicting the yield percentage of a chemical reaction is useful in many aspects such as reducing wet-lab experimentation by giving the priority to the reactions with a high predicted yield. In this wor... |
Title: Escaping Spurious Local Minima of Low-Rank Matrix Factorization Through Convex Lifting Abstract: This work proposes a rapid global solver for nonconvex low-rank matrix factorization (MF) problems that we name MF-Global. Through convex lifting steps, our method efficiently escapes saddle points and spurious local... |
Title: Controlled Generation of Unseen Faults for Partial and OpenSet&Partial Domain Adaptation Abstract: New operating conditions can result in a performance drop of fault diagnostics models due to the domain gap between the training and the testing data distributions. While several domain adaptation approaches have b... |
Title: Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning Abstract: We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM). While ABMs are known to offer microfoundational simulations at the cost of computat... |
Title: Fix the Noise: Disentangling Source Feature for Transfer Learning of StyleGAN Abstract: Transfer learning of StyleGAN has recently shown great potential to solve diverse tasks, especially in domain translation. Previous methods utilized a source model by swapping or freezing weights during transfer learning, how... |
Title: Few-shot learning for medical text: A systematic review Abstract: Objective: Few-shot learning (FSL) methods require small numbers of labeled instances for training. As many medical topics have limited annotated textual data in practical settings, FSL-based natural language processing (NLP) methods hold substant... |
Title: Bayesian Information Criterion for Event-based Multi-trial Ensemble data Abstract: Transient recurring phenomena are ubiquitous in many scientific fields like neuroscience and meteorology. Time inhomogenous Vector Autoregressive Models (VAR) may be used to characterize peri-event system dynamics associated with ... |
Title: Reducing Neural Architecture Search Spaces with Training-Free Statistics and Computational Graph Clustering Abstract: The computational demands of neural architecture search (NAS) algorithms are usually directly proportional to the size of their target search spaces. Thus, limiting the search to high-quality sub... |
Title: On the Optimization of Margin Distribution Abstract: Margin has played an important role on the design and analysis of learning algorithms during the past years, mostly working with the maximization of the minimum margin. Recent years have witnessed the increasing empirical studies on the optimization of margin ... |
Title: Wide and Deep Neural Networks Achieve Optimality for Classification Abstract: While neural networks are used for classification tasks across domains, a long-standing open problem in machine learning is determining whether neural networks trained using standard procedures are optimal for classification, i.e., whe... |
Title: Network Topology Optimization via Deep Reinforcement Learning Abstract: Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central importance to network operators. However, due to the combinatorial nature of network topology, it is extremely diff... |
Title: Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population Abstract: Interacting particle system (IPS) models have proven to be highly successful for describing the spatial movement of organisms. However, it has proven challenging to infer the interaction rules dir... |
Title: A Framework for Constructing Machine Learning Models with Feature Set Optimisation for Evapotranspiration Partitioning Abstract: A deeper understanding of the drivers of evapotranspiration and the modelling of its constituent parts (evaporation and transpiration) could be of significant importance to the monitor... |
Title: Training Language Models with Language Feedback Abstract: Pretrained language models often do not perform tasks in ways that are in line with our preferences, e.g., generating offensive text or factually incorrect summaries. Recent work approaches the above issue by learning from a simple form of human evaluatio... |
Title: Tractable Uncertainty for Structure Learning Abstract: Bayesian structure learning allows one to capture uncertainty over the causal directed acyclic graph (DAG) responsible for generating given data. In this work, we present Tractable Uncertainty for STructure learning (TRUST), a framework for approximate poste... |
Title: Explainable AI via Learning to Optimize Abstract: Indecipherable black boxes are common in machine learning (ML), but applications increasingly require explainable artificial intelligence (XAI). The core of XAI is to establish transparent and interpretable data-driven algorithms. This work provides concrete tool... |
Title: Industry-academia research collaboration and knowledge co-creation: Patterns and anti-patterns Abstract: Increasing the impact of software engineering research in the software industry and the society at large has long been a concern of high priority for the software engineering community. The problem of two cul... |
Title: Randomized Smoothing under Attack: How Good is it in Pratice? Abstract: Randomized smoothing is a recent and celebrated solution to certify the robustness of any classifier. While it indeed provides a theoretical robustness against adversarial attacks, the dimensionality of current classifiers necessarily impose... |
Title: Human's Role in-the-Loop Abstract: Data integration has been recently challenged by the need to handle large volumes of data, arriving at high velocity from a variety of sources, which demonstrate varying levels of veracity. This challenging setting, often referred to as big data, renders many of the existing te... |
Title: Flamingo: a Visual Language Model for Few-Shot Learning Abstract: Building models that can be rapidly adapted to numerous tasks using only a handful of annotated examples is an open challenge for multimodal machine learning research. We introduce Flamingo, a family of Visual Language Models (VLM) with this abili... |
Title: Preoperative brain tumor imaging: models and software for segmentation and standardized reporting Abstract: For patients suffering from brain tumor, prognosis estimation and treatment decisions are made by a multidisciplinary team based on a set of preoperative MR scans. Currently, the lack of standardized and a... |
Title: Modular Domain Adaptation Abstract: Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment. However, without access to source data it is difficult to account for domain shift, which represents a threat to validity. Here, we treat domain a... |
Title: CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers Abstract: The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and l... |
Title: Application of machine learning methods to detect and classify Core images using GAN and texture recognition Abstract: During exploration campaigns, oil companies rely heavily on drill core samples as they provide valuable geological information that helps them find important oil deposits. Traditional core loggi... |
Title: Recommendations on test datasets for evaluating AI solutions in pathology Abstract: Artificial intelligence (AI) solutions that automatically extract information from digital histology images have shown great promise for improving pathological diagnosis. Prior to routine use, it is important to evaluate their pr... |
Title: Brainish: Formalizing A Multimodal Language for Intelligence and Consciousness Abstract: Having a rich multimodal inner language is an important component of human intelligence that enables several necessary core cognitive functions such as multimodal prediction, translation, and generation. Building upon the Co... |
Title: Graph Learning from Multivariate Dependent Time Series via a Multi-Attribute Formulation Abstract: We consider the problem of inferring the conditional independence graph (CIG) of a high-dimensional stationary multivariate Gaussian time series. In a time series graph, each component of the vector series is repre... |
Title: Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI Abstract: Self-learning paradigms in large-scale conversational AI agents tend to leverage user feedback in bridging between what they say and what they mean. However, such learning, particularly in Markov-based query rewriting systems have... |
Title: A Human-Centric Perspective on Fairness and Transparency in Algorithmic Decision-Making Abstract: Automated decision systems (ADS) are increasingly used for consequential decision-making. These systems often rely on sophisticated yet opaque machine learning models, which do not allow for understanding how a give... |
Title: Logically Consistent Adversarial Attacks for Soft Theorem Provers Abstract: Recent efforts within the AI community have yielded impressive results towards "soft theorem proving" over natural language sentences using language models. We propose a novel, generative adversarial framework for probing and improving t... |
Title: Joint Multisided Exposure Fairness for Recommendation Abstract: Prior research on exposure fairness in the context of recommender systems has focused mostly on disparities in the exposure of individual or groups of items to individual users of the system. The problem of how individual or groups of items may be s... |
Title: Prompt Consistency for Zero-Shot Task Generalization Abstract: One of the most impressive results of recent NLP history is the ability of pre-trained language models to solve new tasks in a zero-shot setting. To achieve this, NLP tasks are framed as natural language prompts, generating a response indicating the ... |
Title: Implicit Regularization Properties of Variance Reduced Stochastic Mirror Descent Abstract: In machine learning and statistical data analysis, we often run into objective function that is a summation: the number of terms in the summation possibly is equal to the sample size, which can be enormous. In such a setti... |
Title: The Directional Bias Helps Stochastic Gradient Descent to Generalize in Kernel Regression Models Abstract: We study the Stochastic Gradient Descent (SGD) algorithm in nonparametric statistics: kernel regression in particular. The directional bias property of SGD, which is known in the linear regression setting, ... |
Title: Doubting AI Predictions: Influence-Driven Second Opinion Recommendation Abstract: Effective human-AI collaboration requires a system design that provides humans with meaningful ways to make sense of and critically evaluate algorithmic recommendations. In this paper, we propose a way to augment human-AI collabora... |
Title: Infusing Linguistic Knowledge of SMILES into Chemical Language Models Abstract: The simplified molecular-input line-entry system (SMILES) is the most popular representation of chemical compounds. Therefore, many SMILES-based molecular property prediction models have been developed. In particular, transformer-bas... |
Title: Bridging Differential Privacy and Byzantine-Robustness via Model Aggregation Abstract: This paper aims at jointly addressing two seemly conflicting issues in federated learning: differential privacy (DP) and Byzantine-robustness, which are particularly challenging when the distributed data are non-i.i.d. (indepe... |
Title: Gaze-enhanced Crossmodal Embeddings for Emotion Recognition Abstract: Emotional expressions are inherently multimodal -- integrating facial behavior, speech, and gaze -- but their automatic recognition is often limited to a single modality, e.g. speech during a phone call. While previous work proposed crossmodal... |
Title: ExSum: From Local Explanations to Model Understanding Abstract: Interpretability methods are developed to understand the working mechanisms of black-box models, which is crucial to their responsible deployment. Fulfilling this goal requires both that the explanations generated by these methods are correct and th... |
Title: Identification of Physical Processes and Unknown Parameters of 3D Groundwater Contaminant Problems via Theory-guided U-net Abstract: Identification of unknown physical processes and parameters of groundwater contaminant sources is a challenging task due to their ill-posed and non-unique nature. Numerous works ha... |
Title: Multimodal Representation Learning With Text and Images Abstract: In recent years, multimodal AI has seen an upward trend as researchers are integrating data of different types such as text, images, speech into modelling to get the best results. This project leverages multimodal AI and matrix factorization techn... |
Title: Operational Adaptation of DNN Classifiers using Elastic Weight Consolidation Abstract: Autonomous systems (AS) often use Deep Neural Network (DNN) classifiers to allow them to operate in complex, high dimensional, non-linear, and dynamically changing environments. Due to the complexity of these environments, DNN... |
Title: Deep Ensemble as a Gaussian Process Approximate Posterior Abstract: Deep Ensemble (DE) is an effective alternative to Bayesian neural networks for uncertainty quantification in deep learning. The uncertainty of DE is usually conveyed by the functional inconsistency among the ensemble members, say, the disagreeme... |
Title: NeuralEF: Deconstructing Kernels by Deep Neural Networks Abstract: Learning the principal eigenfunctions of an integral operator defined by a kernel and a data distribution is at the core of many machine learning problems. Traditional nonparametric solutions based on the Nystr{\"o}m formula suffer from scalabili... |
Title: An Initial Look at Self-Reprogramming Artificial Intelligence Abstract: Rapid progress in deep learning research has greatly extended the capabilities of artificial intelligence technology. Conventional AI models are constrained to explicit human-designed algorithms, although a growing body of work in meta-learn... |
Title: FEDIC: Federated Learning on Non-IID and Long-Tailed Data via Calibrated Distillation Abstract: Federated learning provides a privacy guarantee for generating good deep learning models on distributed clients with different kinds of data. Nevertheless, dealing with non-IID data is one of the most challenging prob... |
Title: Cracking White-box DNN Watermarks via Invariant Neuron Transforms Abstract: Recently, how to protect the Intellectual Property (IP) of deep neural networks (DNN) becomes a major concern for the AI industry. To combat potential model piracy, recent works explore various watermarking strategies to embed secret ide... |
Title: Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamics Abstract: The trustworthiness of neural networks is often challenged because they lack the ability to express uncertainty and explain their skill. This can be problematic given the increasing use o... |
Title: Software Testing for Machine Learning Abstract: Machine learning has become prevalent across a wide variety of applications. Unfortunately, machine learning has also shown to be susceptible to deception, leading to errors, and even fatal failures. This circumstance calls into question the widespread use of machi... |
Title: StorSeismic: A new paradigm in deep learning for seismic processing Abstract: Machine learned tasks on seismic data are often trained sequentially and separately, even though they utilize the same features (i.e. geometrical) of the data. We present StorSeismic, as a framework for seismic data processing, which c... |
Title: Loss Function Entropy Regularization for Diverse Decision Boundaries Abstract: Is it possible to train several classifiers to perform meaningful crowd-sourcing to produce a better prediction label set without ground-truth annotation? This paper will modify the contrastive learning objectives to automatically tra... |
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