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Title: Eliciting Best Practices for Collaboration with Computational Notebooks Abstract: Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative ... |
Title: Learning to Solve Routing Problems via Distributionally Robust Optimization Abstract: Recent deep models for solving routing problems always assume a single distribution of nodes for training, which severely impairs their cross-distribution generalization ability. In this paper, we exploit group distributionally... |
Title: Neural Architecture Search for Dense Prediction Tasks in Computer Vision Abstract: The success of deep learning in recent years has lead to a rising demand for neural network architecture engineering. As a consequence, neural architecture search (NAS), which aims at automatically designing neural network archite... |
Title: Explaining Reject Options of Learning Vector Quantization Classifiers Abstract: While machine learning models are usually assumed to always output a prediction, there also exist extensions in the form of reject options which allow the model to reject inputs where only a prediction with an unacceptably low certai... |
Title: Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation Abstract: Social recommendation has shown promising improvements over traditional systems since it leverages social correlation data as an additional input. Most existing work assumes that all data are available to the recommendation platfor... |
Title: REPID: Regional Effect Plots with implicit Interaction Detection Abstract: Machine learning models can automatically learn complex relationships, such as non-linear and interaction effects. Interpretable machine learning methods such as partial dependence plots visualize marginal feature effects but may lead to ... |
Title: Federated Graph Neural Networks: Overview, Techniques and Challenges Abstract: With its powerful capability to deal with graph data widely found in practical applications, graph neural networks (GNNs) have received significant research attention. However, as societies become increasingly concerned with data priv... |
Title: Accelerating Non-Negative and Bounded-Variable Linear Regression Algorithms with Safe Screening Abstract: Non-negative and bounded-variable linear regression problems arise in a variety of applications in machine learning and signal processing. In this paper, we propose a technique to accelerate existing solvers... |
Title: Learning Disentangled Behaviour Patterns for Wearable-based Human Activity Recognition Abstract: In wearable-based human activity recognition (HAR) research, one of the major challenges is the large intra-class variability problem. The collected activity signal is often, if not always, coupled with noises or bia... |
Title: Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods Abstract: Stochastic Gradient Descent-Ascent (SGDA) is one of the most prominent algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks. The success of the met... |
Title: Convolutional Network Fabric Pruning With Label Noise Abstract: This paper presents an iterative pruning strategy for Convolutional Network Fabrics (CNF) in presence of noisy training and testing data. With the continuous increase in size of neural network models, various authors have developed pruning approache... |
Title: SpeechPainter: Text-conditioned Speech Inpainting Abstract: We propose SpeechPainter, a model for filling in gaps of up to one second in speech samples by leveraging an auxiliary textual input. We demonstrate that the model performs speech inpainting with the appropriate content, while maintaining speaker identi... |
Title: HiMA: A Fast and Scalable History-based Memory Access Engine for Differentiable Neural Computer Abstract: Memory-augmented neural networks (MANNs) provide better inference performance in many tasks with the help of an external memory. The recently developed differentiable neural computer (DNC) is a MANN that has... |
Title: Adaptive Conformal Predictions for Time Series Abstract: Uncertainty quantification of predictive models is crucial in decision-making problems. Conformal prediction is a general and theoretically sound answer. However, it requires exchangeable data, excluding time series. While recent works tackled this issue, ... |
Title: Don't stop the training: continuously-updating self-supervised algorithms best account for auditory responses in the cortex Abstract: Over the last decade, numerous studies have shown that deep neural networks exhibit sensory representations similar to those of the mammalian brain, in that their activations line... |
Title: Contextual Importance and Utility: aTheoretical Foundation Abstract: This paper provides new theory to support to the eXplainable AI (XAI) method Contextual Importance and Utility (CIU). CIU arithmetic is based on the concepts of Multi-Attribute Utility Theory, which gives CIU a solid theoretical foundation. The... |
Title: User-Oriented Robust Reinforcement Learning Abstract: Recently, improving the robustness of policies across different environments attracts increasing attention in the reinforcement learning (RL) community. Existing robust RL methods mostly aim to achieve the max-min robustness by optimizing the policy's perform... |
Title: XAI for Transformers: Better Explanations through Conservative Propagation Abstract: Transformers have become an important workhorse of machine learning, with numerous applications. This necessitates the development of reliable methods for increasing their transparency. Multiple interpretability methods, often b... |
Title: An algorithmic solution to the Blotto game using multi-marginal couplings Abstract: We describe an efficient algorithm to compute solutions for the general two-player Blotto game on n battlefields with heterogeneous values. While explicit constructions for such solutions have been limited to specific, largely sy... |
Title: Unreasonable Effectiveness of Last Hidden Layer Activations for Adversarial Robustness Abstract: In standard Deep Neural Network (DNN) based classifiers, the general convention is to omit the activation function in the last (output) layer and directly apply the softmax function on the logits to get the probabili... |
Title: Generalisation and the Risk--Entropy Curve Abstract: In this paper we show that the expected generalisation performance of a learning machine is determined by the distribution of risks or equivalently its logarithm -- a quantity we term the risk entropy -- and the fluctuations in a quantity we call the training ... |
Title: Realistic Counterfactual Explanations with Learned Relations Abstract: Many existing methods of counterfactual explanations ignore the intrinsic relationships between data attributes and thus fail to generate realistic counterfactuals. Moreover, the existing models that account for relationships require domain k... |
Title: A Unified Framework for Masked and Mask-Free Face Recognition via Feature Rectification Abstract: Face recognition under ideal conditions is now considered a well-solved problem with advances in deep learning. Recognizing faces under occlusion, however, still remains a challenge. Existing techniques often fail t... |
Title: textless-lib: a Library for Textless Spoken Language Processing Abstract: Textless spoken language processing research aims to extend the applicability of standard NLP toolset onto spoken language and languages with few or no textual resources. In this paper, we introduce textless-lib, a PyTorch-based library ai... |
Title: Zero-Shot Assistance in Novel Decision Problems Abstract: We consider the problem of creating assistants that can help agents - often humans - solve novel sequential decision problems, assuming the agent is not able to specify the reward function explicitly to the assistant. Instead of aiming to automate, and ac... |
Title: A Statistical Learning View of Simple Kriging Abstract: In the Big Data era, with the ubiquity of geolocation sensors in particular, massive datasets exhibiting a possibly complex spatial dependence structure are becoming increasingly available. In this context, the standard probabilistic theory of statistical l... |
Title: Personalized Prompt Learning for Explainable Recommendation Abstract: Providing user-understandable explanations to justify recommendations could help users better understand the recommended items, increase the system's ease of use, and gain users' trust. A typical approach to realize it is natural language gene... |
Title: Exploring Deep Reinforcement Learning-Assisted Federated Learning for Online Resource Allocation in Privacy-Persevering EdgeIoT Abstract: Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile edge computing-based Internet of Thing (EdgeIoT... |
Title: Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods Abstract: When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by ... |
Title: Interpretable Reinforcement Learning with Multilevel Subgoal Discovery Abstract: We propose a novel Reinforcement Learning model for discrete environments, which is inherently interpretable and supports the discovery of deep subgoal hierarchies. In the model, an agent learns information about environment in the ... |
Title: NeuPL: Neural Population Learning Abstract: Learning in strategy games (e.g. StarCraft, poker) requires the discovery of diverse policies. This is often achieved by iteratively training new policies against existing ones, growing a policy population that is robust to exploit. This iterative approach suffers from... |
Title: Adversarial Attacks and Defense Methods for Power Quality Recognition Abstract: Vulnerability of various machine learning methods to adversarial examples has been recently explored in the literature. Power systems which use these vulnerable methods face a huge threat against adversarial examples. To this end, we... |
Title: Explainable COVID-19 Infections Identification and Delineation Using Calibrated Pseudo Labels Abstract: The upheaval brought by the arrival of the COVID-19 pandemic has continued to bring fresh challenges over the past two years. During this COVID-19 pandemic, there has been a need for rapid identification of in... |
Title: DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis Abstract: Survival analysis (SA) is an active field of research that is concerned with time-to-event outcomes and is prevalent in many domains, particularly biomedical applications. Despite its importanc... |
Title: Algebraic function based Banach space valued ordinary and fractional neural network approximations Abstract: Here we research the univariate quantitative approximation, ordinary and fractional, of Banach space valued continuous functions on a compact interval or all the real line by quasi-interpolation Banach sp... |
Title: Artificial Intelligence-Based Analytics for Impacts of COVID-19 and Online Learning on College Students' Mental Health Abstract: COVID-19, the disease caused by the novel coronavirus (SARS-CoV-2), was first found in Wuhan, China late in the December of 2019. Not long after that the virus spread worldwide and was... |
Title: Vau da muntanialas: Energy-efficient multi-die scalable acceleration of RNN inference Abstract: Recurrent neural networks such as Long Short-Term Memories (LSTMs) learn temporal dependencies by keeping an internal state, making them ideal for time-series problems such as speech recognition. However, the output-t... |
Title: Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products Abstract: Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the s... |
Title: DeepSensor: Deep Learning Testing Framework Based on Neuron Sensitivity Abstract: Despite impressive capabilities and outstanding performance, deep neural network(DNN) has captured increasing public concern for its security problem, due to frequent occurrence of erroneous behaviors. Therefore, it is necessary to... |
Title: Federated Contrastive Learning for Dermatological Disease Diagnosis via On-device Learning Abstract: Deep learning models have been deployed in an increasing number of edge and mobile devices to provide healthcare. These models rely on training with a tremendous amount of labeled data to achieve high accuracy. H... |
Title: SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation Abstract: Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and c... |
Title: Sequential Bayesian experimental designs via reinforcement learning Abstract: Bayesian experimental design (BED) has been used as a method for conducting efficient experiments based on Bayesian inference. The existing methods, however, mostly focus on maximizing the expected information gain (EIG); the cost of e... |
Title: MGCVAE: Multi-objective Inverse Design via Molecular Graph Conditional Variational Autoencoder Abstract: The ultimate goal of various fields is to directly generate molecules with desired properties, such as finding water-soluble molecules in drug development and finding molecules suitable for organic light-emit... |
Title: Understanding DDPM Latent Codes Through Optimal Transport Abstract: Diffusion models have recently outperformed alternative approaches to model the distribution of natural images, such as GANs. Such diffusion models allow for deterministic sampling via the probability flow ODE, giving rise to a latent space and ... |
Title: DualConv: Dual Convolutional Kernels for Lightweight Deep Neural Networks Abstract: CNN architectures are generally heavy on memory and computational requirements which makes them infeasible for embedded systems with limited hardware resources. We propose dual convolutional kernels (DualConv) for constructing li... |
Title: Beyond the Policy Gradient Theorem for Efficient Policy Updates in Actor-Critic Algorithms Abstract: In Reinforcement Learning, the optimal action at a given state is dependent on policy decisions at subsequent states. As a consequence, the learning targets evolve with time and the policy optimization process mu... |
Title: BED: A Real-Time Object Detection System for Edge Devices Abstract: Deploying machine learning models to edge devices has many real-world applications, especially for the scenarios that demand low latency, low power, or data privacy. However, it requires substantial research and engineering efforts due to the li... |
Title: Confidence Threshold Neural Diving Abstract: Finding a better feasible solution in a shorter time is an integral part of solving Mixed Integer Programs. We present a post-hoc method based on Neural Diving to build heuristics more flexibly. We hypothesize that variables with higher confidence scores are more defi... |
Title: Pessimistic Minimax Value Iteration: Provably Efficient Equilibrium Learning from Offline Datasets Abstract: We study episodic two-player zero-sum Markov games (MGs) in the offline setting, where the goal is to find an approximate Nash equilibrium (NE) policy pair based on a dataset collected a priori. When the ... |
Title: Optimal Algorithms for Stochastic Multi-Level Compositional Optimization Abstract: In this paper, we investigate the problem of stochastic multi-level compositional optimization, where the objective function is a composition of multiple smooth but possibly non-convex functions. Existing methods for solving this ... |
Title: Closing the Management Gap for Satellite-Integrated Community Networks: A Hierarchical Approach to Self-Maintenance Abstract: Community networks (CNs) have become an important paradigm for providing essential Internet connectivity in unserved and underserved areas across the world. However, an indispensable part... |
Title: Information-Theoretic Analysis of Minimax Excess Risk Abstract: Two main concepts studied in machine learning theory are generalization gap (difference between train and test error) and excess risk (difference between test error and the minimum possible error). While information-theoretic tools have been used ex... |
Title: Robust Multi-Objective Bayesian Optimization Under Input Noise Abstract: Bayesian optimization (BO) is a sample-efficient approach for tuning design parameters to optimize expensive-to-evaluate, black-box performance metrics. In many manufacturing processes, the design parameters are subject to random input nois... |
Title: A Theory of PAC Learnability under Transformation Invariances Abstract: Transformation invariances are present in many real-world problems. For example, image classification is usually invariant to rotation and color transformation: a rotated car in a different color is still identified as a car. Data augmentati... |
Title: Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness Abstract: Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamental interest to get a ... |
Title: Unsupervised Learning of Group Invariant and Equivariant Representations Abstract: Equivariant neural networks, whose hidden features transform according to representations of a group G acting on the data, exhibit training efficiency and an improved generalisation performance. In this work, we extend group invar... |
Title: CUP: A Conservative Update Policy Algorithm for Safe Reinforcement Learning Abstract: Safe reinforcement learning (RL) is still very challenging since it requires the agent to consider both return maximization and safe exploration. In this paper, we propose CUP, a Conservative Update Policy algorithm with a theo... |
Title: StratDef: a strategic defense against adversarial attacks in malware detection Abstract: Over the years, most research towards defenses against adversarial attacks on machine learning models has been in the image processing domain. The malware detection domain has received less attention despite its importance. ... |
Title: Damped Online Newton Step for Portfolio Selection Abstract: We revisit the classic online portfolio selection problem, where at each round a learner selects a distribution over a set of portfolios to allocate its wealth. It is known that for this problem a logarithmic regret with respect to Cover's loss is achie... |
Title: Forecasting Global Weather with Graph Neural Networks Abstract: We present a data-driven approach for forecasting global weather using graph neural networks. The system learns to step forward the current 3D atmospheric state by six hours, and multiple steps are chained together to produce skillful forecasts goin... |
Title: Weighted Programming Abstract: We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weig... |
Title: Multi-class granular approximation by means of disjoint and adjacent fuzzy granules Abstract: In granular computing, fuzzy sets can be approximated by granularly representable sets that are as close as possible to the original fuzzy set w.r.t. a given closeness measure. Such sets are called granular approximatio... |
Title: Deep Generative model with Hierarchical Latent Factors for Time Series Anomaly Detection Abstract: Multivariate time series anomaly detection has become an active area of research in recent years, with Deep Learning models outperforming previous approaches on benchmark datasets. Among reconstruction-based models... |
Title: Identifying equivalent Calabi--Yau topologies: A discrete challenge from math and physics for machine learning Abstract: We review briefly the characteristic topological data of Calabi--Yau threefolds and focus on the question of when two threefolds are equivalent through related topological data. This provides ... |
Title: Deep Convolutional Autoencoder for Assessment of Anomalies in Multi-stream Sensor Data Abstract: A fully convolutional autoencoder is developed for the detection of anomalies in multi-sensor vehicle drive-cycle data from the powertrain domain. Preliminary results collected on real-world powertrain data show that... |
Title: Bayesian Optimisation for Active Monitoring of Air Pollution Abstract: Air pollution is one of the leading causes of mortality globally, resulting in millions of deaths each year. Efficient monitoring is important to measure exposure and enforce legal limits. New low-cost sensors can be deployed in greater numbe... |
Title: Bayesian Imitation Learning for End-to-End Mobile Manipulation Abstract: In this work we investigate and demonstrate benefits of a Bayesian approach to imitation learning from multiple sensor inputs, as applied to the task of opening office doors with a mobile manipulator. Augmenting policies with additional sen... |
Title: UserBERT: Modeling Long- and Short-Term User Preferences via Self-Supervision Abstract: E-commerce platforms generate vast amounts of customer behavior data, such as clicks and purchases, from millions of unique users every day. However, effectively using this data for behavior understanding tasks is challenging... |
Title: Defending against Reconstruction Attacks with R\'enyi Differential Privacy Abstract: Reconstruction attacks allow an adversary to regenerate data samples of the training set using access to only a trained model. It has been recently shown that simple heuristics can reconstruct data samples from language models, ... |
Title: Random Feature Amplification: Feature Learning and Generalization in Neural Networks Abstract: In this work, we provide a characterization of the feature-learning process in two-layer ReLU networks trained by gradient descent on the logistic loss following random initialization. We consider data with binary labe... |
Title: Lie Point Symmetry Data Augmentation for Neural PDE Solvers Abstract: Neural networks are increasingly being used to solve partial differential equations (PDEs), replacing slower numerical solvers. However, a critical issue is that neural PDE solvers require high-quality ground truth data, which usually must com... |
Title: Quantifying Memorization Across Neural Language Models Abstract: Large language models (LMs) have been shown to memorize parts of their training data, and when prompted appropriately, they will emit the memorized training data verbatim. This is undesirable because memorization violates privacy (exposing user dat... |
Title: EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs Abstract: How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)? TKGs represent facts about entities and their relations, where each fact is associated with a timestamp. Reasoning over TKG... |
Title: Conformal Prediction Sets with Limited False Positives Abstract: We develop a new approach to multi-label conformal prediction in which we aim to output a precise set of promising prediction candidates with a bounded number of incorrect answers. Standard conformal prediction provides the ability to adapt to mode... |
Title: Predicting on the Edge: Identifying Where a Larger Model Does Better Abstract: Much effort has been devoted to making large and more accurate models, but relatively little has been put into understanding which examples are benefiting from the added complexity. In this paper, we demonstrate and analyze the surpri... |
Title: Tomayto, Tomahto. Beyond Token-level Answer Equivalence for Question Answering Evaluation Abstract: The predictions of question answering (QA) systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating the tru... |
Title: Taking a Step Back with KCal: Multi-Class Kernel-Based Calibration for Deep Neural Networks Abstract: Deep neural network (DNN) classifiers are often overconfident, producing miscalibrated class probabilities. Most existing calibration methods either lack theoretical guarantees for producing calibrated outputs o... |
Title: Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset Abstract: Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation. However, cy... |
Title: On the Role of Channel Capacity in Learning Gaussian Mixture Models Abstract: This paper studies the sample complexity of learning the $k$ unknown centers of a balanced Gaussian mixture model (GMM) in $\mathbb{R}^d$ with spherical covariance matrix $\sigma^2\mathbf{I}$. In particular, we are interested in the fo... |
Title: Active Uncertainty Reduction for Human-Robot Interaction: An Implicit Dual Control Approach Abstract: The ability to accurately predict human behavior is central to the safety and efficiency of robot autonomy in interactive settings. Unfortunately, robots often lack access to key information on which these predi... |
Title: A Light-Weight Multi-Objective Asynchronous Hyper-Parameter Optimizer Abstract: We describe a light-weight yet performant system for hyper-parameter optimization that approximately minimizes an overall scalar cost function that is obtained by combining multiple performance objectives using a target-priority-limi... |
Title: Architecture Agnostic Federated Learning for Neural Networks Abstract: With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(FL) has become an important learning paradigm. However, jointly learning a deep neural network model in a FL setting proves to be a non-trivial... |
Title: General-purpose, long-context autoregressive modeling with Perceiver AR Abstract: Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly used autoregressive models, Transformers, are prohib... |
Title: LIMREF: Local Interpretable Model Agnostic Rule-based Explanations for Forecasting, with an Application to Electricity Smart Meter Data Abstract: Accurate electricity demand forecasts play a crucial role in sustainable power systems. To enable better decision-making especially for demand flexibility of the end-u... |
Title: The efficacy and generalizability of conditional GANs for posterior inference in physics-based inverse problems Abstract: In this work, we train conditional Wasserstein generative adversarial networks to effectively sample from the posterior of physics-based Bayesian inference problems. The generator is construc... |
Title: Beyond Deterministic Translation for Unsupervised Domain Adaptation Abstract: In this work we challenge the common approach of using a one-to-one mapping ('translation') between the source and target domains in unsupervised domain adaptation (UDA). Instead, we rely on stochastic translation to capture inherent t... |
Title: Binary Classification for High Dimensional Data using Supervised Non-Parametric Ensemble Method Abstract: Medical Research data used for prognostication deals with binary classification problems in most of the cases. The endocrinological disorders have data available and it can be leveraged using Machine Learnin... |
Title: Trustworthy Anomaly Detection: A Survey Abstract: Anomaly detection has a wide range of real-world applications, such as bank fraud detection and cyber intrusion detection. In the past decade, a variety of anomaly detection models have been developed, which lead to big progress towards accurately detecting vario... |
Title: Safe Reinforcement Learning by Imagining the Near Future Abstract: Safe reinforcement learning is a promising path toward applying reinforcement learning algorithms to real-world problems, where suboptimal behaviors may lead to actual negative consequences. In this work, we focus on the setting where unsafe stat... |
Title: Speech Denoising in the Waveform Domain with Self-Attention Abstract: In this work, we present CleanUNet, a causal speech denoising model on the raw waveform. The proposed model is based on an encoder-decoder architecture combined with several self-attention blocks to refine its bottleneck representations, which... |
Title: Low Latency Real-Time Seizure Detection Using Transfer Deep Learning Abstract: Scalp electroencephalogram (EEG) signals inherently have a low signal-to-noise ratio due to the way the signal is electrically transduced. Temporal and spatial information must be exploited to achieve accurate detection of seizure eve... |
Title: BB-ML: Basic Block Performance Prediction using Machine Learning Techniques Abstract: Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction a... |
Title: Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations Abstract: Vision Transformers (ViTs) take all the image patches as tokens and construct multi-head self-attention (MHSA) among them. Complete leverage of these image tokens brings redundant computations since not all the ... |
Title: Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing Abstract: Mobile Crowdsensing systems are vulnerable to various attacks as they build on non-dedicated and ubiquitous properties. Machine learning (ML)-based approaches are widely investigated to build attack detection sy... |
Title: Policy Learning and Evaluation with Randomized Quasi-Monte Carlo Abstract: Reinforcement learning constantly deals with hard integrals, for example when computing expectations in policy evaluation and policy iteration. These integrals are rarely analytically solvable and typically estimated with the Monte Carlo ... |
Title: Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars Abstract: Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images when small perturbations are present. With the increasing prevalence of CNNs in self-driving cars, it is vital to ensure these ... |
Title: Reducing Overconfidence Predictions for Autonomous Driving Perception Abstract: In state-of-the-art deep learning for object recognition, SoftMax and Sigmoid functions are most commonly employed as the predictor outputs. Such layers often produce overconfident predictions rather than proper probabilistic scores,... |
Title: CenGCN: Centralized Convolutional Networks with Vertex Imbalance for Scale-Free Graphs Abstract: Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all in... |
Title: Spatial Transformer K-Means Abstract: K-means defines one of the most employed centroid-based clustering algorithms with performances tied to the data's embedding. Intricate data embeddings have been designed to push $K$-means performances at the cost of reduced theoretical guarantees and interpretability of the... |
Title: CycleGAN for Undamaged-to-Damaged Domain Translation for Structural Health Monitoring and Damage Detection Abstract: The recent advances in the data science field in the last few decades have benefitted many other fields including Structural Health Monitoring (SHM). Particularly, Artificial Intelligence (AI) suc... |
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