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Title: GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records Abstract: Objective: To develop a large pretrained clinical language model from scratch using transformer architecture; systematically examine how transformer models of different sizes could help ... |
Title: Double-Barreled Question Detection at Momentive Abstract: Momentive offers solutions in market research, customer experience, and enterprise feedback. The technology is gleaned from the billions of real responses to questions asked on the platform. However, people may create biased questions. A double-barreled q... |
Title: Assessment of contextualised representations in detecting outcome phrases in clinical trials Abstract: Automating the recognition of outcomes reported in clinical trials using machine learning has a huge potential of speeding up access to evidence necessary in healthcare decision-making. Prior research has howev... |
Title: Semi-supervised Nonnegative Matrix Factorization for Document Classification Abstract: We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators. The proposed SSNMF models simultaneously prov... |
Title: Automated Single-Label Patent Classification using Ensemble Classifiers Abstract: Many thousands of patent applications arrive at patent offices around the world every day. One important subtask when a patent application is submitted is to assign one or more classification codes from the complex and hierarchical... |
Title: Quantum Deep Learning for Mutant COVID-19 Strain Prediction Abstract: New COVID-19 epidemic strains like Delta and Omicron with increased transmissibility and pathogenicity emerge and spread across the whole world rapidly while causing high mortality during the pandemic period. Early prediction of possible varia... |
Title: Targeted Data Poisoning Attack on News Recommendation System by Content Perturbation Abstract: News Recommendation System(NRS) has become a fundamental technology to many online news services. Meanwhile, several studies show that recommendation systems(RS) are vulnerable to data poisoning attacks, and the attack... |
Title: TIGGER: Scalable Generative Modelling for Temporal Interaction Graphs Abstract: There has been a recent surge in learning generative models for graphs. While impressive progress has been made on static graphs, work on generative modeling of temporal graphs is at a nascent stage with significant scope for improve... |
Title: Improved Search of Relevant Points for Nearest-Neighbor Classification Abstract: Given a training set $P \subset \mathbb{R}^d$, the nearest-neighbor classifier assigns any query point $q \in \mathbb{R}^d$ to the class of its closest point in $P$. To answer these classification queries, some training points are m... |
Title: Kubric: A scalable dataset generator Abstract: Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details. But collecting, processing and annotating real data at scale is difficult, ... |
Title: Domain Adaptation of Automated Treatment Planning from Computed Tomography to Magnetic Resonance Abstract: Objective: Machine learning (ML) based radiation treatment (RT) planning addresses the iterative and time-consuming nature of conventional inverse planning. Given the rising importance of Magnetic resonance... |
Title: The Unsurprising Effectiveness of Pre-Trained Vision Models for Control Abstract: Recent years have seen the emergence of pre-trained representations as a powerful abstraction for AI applications in computer vision, natural language, and speech. However, policy learning for control is still dominated by a tabula... |
Title: Quantum Local Differential Privacy and Quantum Statistical Query Model Abstract: The problem of private learning has been extensively studied in classical computer science. Notably, a striking equivalence between local differentially private learning and statistical query learning has been shown. In addition, th... |
Title: Continual and Sliding Window Release for Private Empirical Risk Minimization Abstract: It is difficult to continually update private machine learning models with new data while maintaining privacy. Data incur increasing privacy loss -- as measured by differential privacy -- when they are used in repeated computa... |
Title: Fast rates for noisy interpolation require rethinking the effects of inductive bias Abstract: Good generalization performance on high-dimensional data crucially hinges on a simple structure of the ground truth and a corresponding strong inductive bias of the estimator. Even though this intuition is valid for reg... |
Title: Differential Privacy Amplification in Quantum and Quantum-inspired Algorithms Abstract: Differential privacy provides a theoretical framework for processing a dataset about $n$ users, in a way that the output reveals a minimal information about any single user. Such notion of privacy is usually ensured by noise-... |
Title: I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through Islandization Abstract: Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning modalities, high-performance hardware acceleration of GCNs is as critic... |
Title: ZippyPoint: Fast Interest Point Detection, Description, and Matching through Mixed Precision Discretization Abstract: The design of more complex and powerful neural network models has significantly advanced the state-of-the-art in local feature detection and description. These advances can be attributed to deepe... |
Title: Responsible AI in Healthcare Abstract: This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of Informatics, Systems, and Com... |
Title: Mammograms Classification: A Review Abstract: An advanced reliable low-cost form of screening method, Digital mammography has been used as an effective imaging method for breast cancer detection. With an increased focus on technologies to aid healthcare, Mammogram images have been utilized in developing computer... |
Title: Clustering and classification of low-dimensional data in explicit feature map domain: intraoperative pixel-wise diagnosis of adenocarcinoma of a colon in a liver Abstract: Application of artificial intelligence in medicine brings in highly accurate predictions achieved by complex models, the reasoning of which i... |
Title: Unsupervised Image Registration Towards Enhancing Performance and Explainability in Cardiac And Brain Image Analysis Abstract: Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical ... |
Title: Unsupervised Domain Adaptation with Contrastive Learning for OCT Segmentation Abstract: Accurate segmentation of retinal fluids in 3D Optical Coherence Tomography images is key for diagnosis and personalized treatment of eye diseases. While deep learning has been successful at this task, trained supervised model... |
Title: A Typology to Explore and Guide Explanatory Interactive Machine Learning Abstract: Recently, more and more eXplanatory Interactive machine Learning (XIL) methods have been proposed with the goal of extending a model's learning process by integrating human user supervision on the model's explanations. These metho... |
Title: AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators Abstract: We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt can be used to determi... |
Title: Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets Abstract: We study a Markov matching market involving a planner and a set of strategic agents on the two sides of the market. At each step, the agents are presented with a dynamical context, where the contexts determine the utilitie... |
Title: WaveMix: Resource-efficient Token Mixing for Images Abstract: Although certain vision transformer (ViT) and CNN architectures generalize well on vision tasks, it is often impractical to use them on green, edge, or desktop computing due to their computational requirements for training and even testing. We present... |
Title: HyperMixer: An MLP-based Green AI Alternative to Transformers Abstract: Transformer-based architectures are the model of choice for natural language understanding, but they come at a significant cost, as they have quadratic complexity in the input length and can be difficult to tune. In the pursuit of Green AI, ... |
Title: Low-Loss Subspace Compression for Clean Gains against Multi-Agent Backdoor Attacks Abstract: Recent exploration of the multi-agent backdoor attack demonstrated the backfiring effect, a natural defense against backdoor attacks where backdoored inputs are randomly classified. This yields a side-effect of low accur... |
Title: Learning to Bound: A Generative Cram\'er-Rao Bound Abstract: The Cram\'er-Rao bound (CRB), a well-known lower bound on the performance of any unbiased parameter estimator, has been used to study a wide variety of problems. However, to obtain the CRB, requires an analytical expression for the likelihood of the me... |
Title: Detection of AI Synthesized Hindi Speech Abstract: The recent advancements in generative artificial speech models have made possible the generation of highly realistic speech signals. At first, it seems exciting to obtain these artificially synthesized signals such as speech clones or deep fakes but if left unch... |
Title: A Predictive Model for Student Performance in Classrooms Using Student Interactions With an eTextbook Abstract: With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students' learning. With the careful analysis of this data, educators can... |
Title: Open-Ended Knowledge Tracing Abstract: Knowledge tracing refers to the problem of estimating each student's knowledge component/skill mastery level from their past responses to questions in educational applications. One direct benefit knowledge tracing methods provide is the ability to predict each student's per... |
Title: Biometric recognition: why not massively adopted yet? Abstract: Although there has been a dramatically reduction on the prices of capturing devices and an increase on computing power in the last decade, it seems that biometric systems are still far from massive adoption for civilian applications. This paper deal... |
Title: Cognitive Diagnosis with Explicit Student Vector Estimation and Unsupervised Question Matrix Learning Abstract: Cognitive diagnosis is an essential task in many educational applications. Many solutions have been designed in the literature. The deterministic input, noisy "and" gate (DINA) model is a classical cog... |
Title: A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions Abstract: Despite artificial intelligence (AI) having transformed major aspects of our society, less than a fraction of its potential has been explored, let alone deployed, for education. AI-powered learning can provide millions ... |
Title: Robustness and Usefulness in AI Explanation Methods Abstract: Explainability in machine learning has become incredibly important as machine learning-powered systems become ubiquitous and both regulation and public sentiment begin to demand an understanding of how these systems make decisions. As a result, a numb... |
Title: Provably Accurate and Scalable Linear Classifiers in Hyperbolic Spaces Abstract: Many high-dimensional practical data sets have hierarchical structures induced by graphs or time series. Such data sets are hard to process in Euclidean spaces and one often seeks low-dimensional embeddings in other space forms to p... |
Title: A Push-Relabel Based Additive Approximation for Optimal Transport Abstract: Optimal Transport is a popular distance metric for measuring similarity between distributions. Exact algorithms for computing Optimal Transport can be slow, which has motivated the development of approximate numerical solvers (e.g. Sinkh... |
Title: Flat minima generalize for low-rank matrix recovery Abstract: Empirical evidence suggests that for a variety of overparameterized nonlinear models, most notably in neural network training, the growth of the loss around a minimizer strongly impacts its performance. Flat minima -- those around which the loss grows... |
Title: The Fundamental Price of Secure Aggregation in Differentially Private Federated Learning Abstract: We consider the problem of training a $d$ dimensional model with distributed differential privacy (DP) where secure aggregation (SecAgg) is used to ensure that the server only sees the noisy sum of $n$ model update... |
Title: Defending Graph Convolutional Networks against Dynamic Graph Perturbations via Bayesian Self-supervision Abstract: In recent years, plentiful evidence illustrates that Graph Convolutional Networks (GCNs) achieve extraordinary accomplishments on the node classification task. However, GCNs may be vulnerable to adv... |
Title: Static Prediction of Runtime Errors by Learning to Execute Programs with External Resource Descriptions Abstract: The execution behavior of a program often depends on external resources, such as program inputs or file contents, and so cannot be run in isolation. Nevertheless, software developers benefit from fas... |
Title: Zero-delay Consistent and Smooth Trainable Interpolation Abstract: The question of how to produce a smooth interpolating curve from a stream of data points is addressed in this paper. To this end, we formalize the concept of real-time interpolator (RTI): a trainable unit that recovers smooth signals that are con... |
Title: Data adaptive RKHS Tikhonov regularization for learning kernels in operators Abstract: We present DARTR: a Data Adaptive RKHS Tikhonov Regularization method for the linear inverse problem of nonparametric learning of function parameters in operators. A key ingredient is a system intrinsic data-adaptive (SIDA) RK... |
Title: YONO: Modeling Multiple Heterogeneous Neural Networks on Microcontrollers Abstract: With the advancement of Deep Neural Networks (DNN) and large amounts of sensor data from Internet of Things (IoT) systems, the research community has worked to reduce the computational and resource demands of DNN to compute on lo... |
Title: Learning Sensorimotor Primitives of Sequential Manipulation Tasks from Visual Demonstrations Abstract: This work aims to learn how to perform complex robot manipulation tasks that are composed of several, consecutively executed low-level sub-tasks, given as input a few visual demonstrations of the tasks performe... |
Title: New Insights on Reducing Abrupt Representation Change in Online Continual Learning Abstract: In the online continual learning paradigm, agents must learn from a changing distribution while respecting memory and compute constraints. Experience Replay (ER), where a small subset of past data is stored and replayed ... |
Title: A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data Abstract: Nonnegative (linear) least square problems are a fundamental class of problems that is well-studied in statistical learning and for which solvers have been implemented in many of the standard programming languages us... |
Title: Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation Abstract: The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models. In this paper, we mainly focus on designing an efficient data-centric scheme t... |
Title: Generating 3D Bio-Printable Patches Using Wound Segmentation and Reconstruction to Treat Diabetic Foot Ulcers Abstract: We introduce AiD Regen, a novel system that generates 3D wound models combining 2D semantic segmentation with 3D reconstruction so that they can be printed via 3D bio-printers during the surger... |
Title: Informative Planning for Worst-Case Error Minimisation in Sparse Gaussian Process Regression Abstract: We present a planning framework for minimising the deterministic worst-case error in sparse Gaussian process (GP) regression. We first derive a universal worst-case error bound for sparse GP regression with bou... |
Title: Multi-Scale Self-Contrastive Learning with Hard Negative Mining for Weakly-Supervised Query-based Video Grounding Abstract: Query-based video grounding is an important yet challenging task in video understanding, which aims to localize the target segment in an untrimmed video according to a sentence query. Most ... |
Title: Occupancy Flow Fields for Motion Forecasting in Autonomous Driving Abstract: We propose Occupancy Flow Fields, a new representation for motion forecasting of multiple agents, an important task in autonomous driving. Our representation is a spatio-temporal grid with each grid cell containing both the probability ... |
Title: High-order Order Proximity-Incorporated, Symmetry and Graph-Regularized Nonnegative Matrix Factorization for Community Detection Abstract: Community describes the functional mechanism of a network, making community detection serve as a fundamental graph tool for various real applications like discovery of social... |
Title: Multi-Modal Mixup for Robust Fine-tuning Abstract: Pre-trained large-scale models provide a transferable embedding, and they show comparable performance on the diverse downstream task. However, the transferability of multi-modal learning is restricted, and the analysis of learned embedding has not been explored ... |
Title: Noisy Low-rank Matrix Optimization: Geometry of Local Minima and Convergence Rate Abstract: This paper is concerned with low-rank matrix optimization, which has found a wide range of applications in machine learning. This problem in the special case of matrix sense has been studied extensively through the notion... |
Title: Graph Reinforcement Learning for Predictive Power Allocation to Mobile Users Abstract: Allocating resources with future channels can save resource to ensure quality-of-service of video streaming. In this paper, we optimize predictive power allocation to minimize the energy consumed at distributed units (DUs) by ... |
Title: Estimating the average causal effect of intervention in continuous variables using machine learning Abstract: The most widely discussed methods for estimating the Average Causal Effect/Average Treatment Effect are those for intervention in discrete binary variables whose value represents intervention/non-interve... |
Title: An Analysis of Measure-Valued Derivatives for Policy Gradients Abstract: Reinforcement learning methods for robotics are increasingly successful due to the constant development of better policy gradient techniques. A precise (low variance) and accurate (low bias) gradient estimator is crucial to face increasingl... |
Title: Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks Abstract: The wide adoption and application of Masked language models~(MLMs) on sensitive data (from legal to medical) necessitates a thorough quantitative investigation into their privacy vulnerabilities -- to what extent do ... |
Title: Digital Speech Algorithms for Speaker De-Identification Abstract: The present work is based on the COST Action IC1206 for De-identification in multimedia content. It was performed to test four algorithms of voice modifications on a speech gender recognizer to find the degree of modification of pitch when the spe... |
Title: A Preliminary Study on Aging Examining Online Handwriting Abstract: In order to develop infocommunications devices so that the capabilities of the human brain may interact with the capabilities of any artificially cognitive system a deeper knowledge of aging is necessary. Especially if society does not want to e... |
Title: Nonlinear Isometric Manifold Learning for Injective Normalizing Flows Abstract: To model manifold data using normalizing flows, we propose to employ the isometric autoencoder to design nonlinear encodings with explicit inverses. The isometry allows us to separate manifold learning and density estimation and trai... |
Title: Few-Shot Traffic Prediction with Graph Networks using Locale as Relational Inductive Biases Abstract: Accurate short-term traffic prediction plays a pivotal role in various smart mobility operation and management systems. Currently, most of the state-of-the-art prediction models are based on graph neural network... |
Title: On Generalizing Beyond Domains in Cross-Domain Continual Learning Abstract: Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks often suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Many recent methods focus o... |
Title: Contrastive Conditional Neural Processes Abstract: Conditional Neural Processes~(CNPs) bridge neural networks with probabilistic inference to approximate functions of Stochastic Processes under meta-learning settings. Given a batch of non-{\it i.i.d} function instantiations, CNPs are jointly optimized for in-ins... |
Title: Online Weak-form Sparse Identification of Partial Differential Equations Abstract: This paper presents an online algorithm for identification of partial differential equations (PDEs) based on the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy). The algorithm is online in a sense that if ... |
Title: End-to-end Multiple Instance Learning with Gradient Accumulation Abstract: Being able to learn on weakly labeled data, and provide interpretability, are two of the main reasons why attention-based deep multiple instance learning (ABMIL) methods have become particularly popular for classification of histopatholog... |
Title: Adaptor: Objective-Centric Adaptation Framework for Language Models Abstract: Progress in natural language processing research is catalyzed by the possibilities given by the widespread software frameworks. This paper introduces Adaptor library that transposes the traditional model-centric approach composed of pr... |
Title: Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series Abstract: We propose an end-to-end architecture for multivariate time-series prediction that integrates a spatial-temporal graph neural network with a matrix filtering module. This module generates filtered (inverse) correlation gr... |
Title: DeltaCNN: End-to-End CNN Inference of Sparse Frame Differences in Videos Abstract: Convolutional neural network inference on video data requires powerful hardware for real-time processing. Given the inherent coherence across consecutive frames, large parts of a video typically change little. By skipping identica... |
Title: Semi-Random Sparse Recovery in Nearly-Linear Time Abstract: Sparse recovery is one of the most fundamental and well-studied inverse problems. Standard statistical formulations of the problem are provably solved by general convex programming techniques and more practical, fast (nearly-linear time) iterative metho... |
Title: A Compilation Flow for the Generation of CNN Inference Accelerators on FPGAs Abstract: We present a compilation flow for the generation of CNN inference accelerators on FPGAs. The flow translates a frozen model into OpenCL kernels with the TVM compiler and uses the Intel OpenCL SDK to compile to an FPGA bitstrea... |
Title: Data augmentation with mixtures of max-entropy transformations for filling-level classification Abstract: We address the problem of distribution shifts in test-time data with a principled data augmentation scheme for the task of content-level classification. In such a task, properties such as shape or transparen... |
Title: Bayesian Optimisation-Assisted Neural Network Training Technique for Radio Localisation Abstract: Radio signal-based (indoor) localisation technique is important for IoT applications such as smart factory and warehouse. Through machine learning, especially neural networks methods, more accurate mapping from sign... |
Title: Robot Learning of Mobile Manipulation with Reachability Behavior Priors Abstract: Mobile Manipulation (MM) systems are ideal candidates for taking up the role of a personal assistant in unstructured real-world environments. Among other challenges, MM requires effective coordination of the robot's embodiments for... |
Title: AdaPT: Fast Emulation of Approximate DNN Accelerators in PyTorch Abstract: Current state-of-the-art employs approximate multipliers to address the highly increased power demands of DNN accelerators. However, evaluating the accuracy of approximate DNNs is cumbersome due to the lack of adequate support for approxi... |
Title: Obstacle Aware Sampling for Path Planning Abstract: Many path planning algorithms are based on sampling the state space. While this approach is very simple, it can become costly when the obstacles are unknown, since samples hitting these obstacles are wasted. The goal of this paper is to efficiently identify obs... |
Title: The role of alcohol outlet visits derived from mobile phone location data in enhancing domestic violence prediction at the neighborhood level Abstract: Domestic violence (DV) is a serious public health issue, with 1 in 3 women and 1 in 4 men experiencing some form of partner-related violence every year. Existing... |
Title: COLA: Consistent Learning with Opponent-Learning Awareness Abstract: Learning in general-sum games can be unstable and often leads to socially undesirable, Pareto-dominated outcomes. To mitigate this, Learning with Opponent-Learning Awareness (LOLA) introduced opponent shaping to this setting, by accounting for ... |
Title: VoViT: Low Latency Graph-based Audio-Visual Voice Separation Transformer Abstract: This paper presents an audio-visual approach for voice separation which outperforms state-of-the-art methods at a low latency in two scenarios: speech and singing voice. The model is based on a two-stage network. Motion cues are o... |
Title: Comparing representations of biological data learned with different AI paradigms, augmenting and cropping strategies Abstract: Recent advances in computer vision and robotics enabled automated large-scale biological image analysis. Various machine learning approaches have been successfully applied to phenotypic ... |
Title: Explaining Classifiers by Constructing Familiar Concepts Abstract: Interpreting a large number of neurons in deep learning is difficult. Our proposed `CLAssifier-DECoder' architecture (ClaDec) facilitates the understanding of the output of an arbitrary layer of neurons or subsets thereof. It uses a decoder that ... |
Title: Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation Abstract: This paper describes our submission to SemEval-2022 Task 6 on sarcasm detection and its five subtasks for English and Arabic. Sarcasm conveys a meaning which contradi... |
Title: Biological Sequence Design with GFlowNets Abstract: Design of de novo biological sequences with desired properties, like protein and DNA sequences, often involves an active loop with several rounds of molecule ideation and expensive wet-lab evaluations. These experiments can consist of multiple stages, with incr... |
Title: Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery Abstract: Robot assembly discovery is a challenging problem that lives at the intersection of resource allocation and motion planning. The goal is to combine a predefined set of objects to form somethin... |
Title: Motron: Multimodal Probabilistic Human Motion Forecasting Abstract: Autonomous systems and humans are increasingly sharing the same space. Robots work side by side or even hand in hand with humans to balance each other's limitations. Such cooperative interactions are ever more sophisticated. Thus, the ability to... |
Title: Robustly-reliable learners under poisoning attacks Abstract: Data poisoning attacks, in which an adversary corrupts a training set with the goal of inducing specific desired mistakes, have raised substantial concern: even just the possibility of such an attack can make a user no longer trust the results of a lea... |
Title: Variational methods for simulation-based inference Abstract: We present Sequential Neural Variational Inference (SNVI), an approach to perform Bayesian inference in models with intractable likelihoods. SNVI combines likelihood-estimation (or likelihood-ratio-estimation) with variational inference to achieve a sc... |
Title: Selective-Supervised Contrastive Learning with Noisy Labels Abstract: Deep networks have strong capacities of embedding data into latent representations and finishing following tasks. However, the capacities largely come from high-quality annotated labels, which are expensive to collect. Noisy labels are more af... |
Title: Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset Abstract: Modeling biological soft tissue is complex in part due to material heterogeneity. Microstructural patterns, which play a major role in defining the mechanical behavior of these tissues, are both challenging to char... |
Title: Reward-Biased Maximum Likelihood Estimation for Neural Contextual Bandits Abstract: Reward-biased maximum likelihood estimation (RBMLE) is a classic principle in the adaptive control literature for tackling explore-exploit trade-offs. This paper studies the stochastic contextual bandit problem with general bound... |
Title: A Gating Model for Bias Calibration in Generalized Zero-shot Learning Abstract: Generalized zero-shot learning (GZSL) aims at training a model that can generalize to unseen class data by only using auxiliary information. One of the main challenges in GZSL is a biased model prediction toward seen classes caused b... |
Title: Locate This, Not That: Class-Conditioned Sound Event DOA Estimation Abstract: Existing systems for sound event localization and detection (SELD) typically operate by estimating a source location for all classes at every time instant. In this paper, we propose an alternative class-conditioned SELD model for situa... |
Title: Trustable Co-label Learning from Multiple Noisy Annotators Abstract: Supervised deep learning depends on massive accurately annotated examples, which is usually impractical in many real-world scenarios. A typical alternative is learning from multiple noisy annotators. Numerous earlier works assume that all label... |
Title: Follow the Water: Finding Water, Snow and Clouds on Terrestrial Exoplanets with Photometry and Machine Learning Abstract: All life on Earth needs water. NASA's quest to follow the water links water to the search for life in the cosmos. Telescopes like JWST and mission concepts like HabEx, LUVOIR and Origins are ... |
Title: Learning Bidirectional Translation between Descriptions and Actions with Small Paired Data Abstract: This study achieved bidirectional translation between descriptions and actions using small paired data. The ability to mutually generate descriptions and actions is essential for robots to collaborate with humans... |
Title: Learning based Age of Information Minimization in UAV-relayed IoT Networks Abstract: Unmanned Aerial Vehicles (UAVs) are used as aerial base-stations to relay time-sensitive packets from IoT devices to the nearby terrestrial base-station (TBS). Scheduling of packets in such UAV-relayed IoT-networks to ensure fre... |
Title: Adaptative Perturbation Patterns: Realistic Adversarial Learning for Robust Intrusion Detection Abstract: Adversarial attacks pose a major threat to machine learning and to the systems that rely on it. In the cybersecurity domain, adversarial cyber-attack examples capable of evading detection are especially conc... |
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