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Title: Learning Fair Models without Sensitive Attributes: A Generative Approach Abstract: Most existing fair classifiers rely on sensitive attributes to achieve fairness. However, for many scenarios, we cannot obtain sensitive attributes due to privacy and legal issues. The lack of sensitive attributes challenges many ... |
Title: Low-complexity Near-optimum Symbol Detection Based on Neural Enhancement of Factor Graphs Abstract: We consider the application of the factor graph framework for symbol detection on linear inter-symbol interference channels. Based on the Ungerboeck observation model, a detection algorithm with appealing complexi... |
Title: Intelligent Blockage Prediction and Proactive Handover for Seamless Connectivity in Vision-Aided 5G/6G UDNs Abstract: The upsurge in wireless devices and real-time service demands force the move to a higher frequency spectrum. Millimetre-wave (mmWave) and terahertz (THz) bands combined with the beamforming techn... |
Title: TubeDETR: Spatio-Temporal Video Grounding with Transformers Abstract: We consider the problem of localizing a spatio-temporal tube in a video corresponding to a given text query. This is a challenging task that requires the joint and efficient modeling of temporal, spatial and multi-modal interactions. To addres... |
Title: Weakly supervised causal representation learning Abstract: Learning high-level causal representations together with a causal model from unstructured low-level data such as pixels is impossible from observational data alone. We prove under mild assumptions that this representation is however identifiable in a wea... |
Title: Reliability and Validity of the Polar V800 Sports Watch for Estimating Vertical Jump Height Abstract: This study aimed to assess the reliability and validity of the Polar V800 to measure vertical jump height. Twenty-two physically active healthy men (age: 22.89 +- 4.23 years; body mass: 70.74 +- 8.04 kg; height:... |
Title: AI Gone Astray: Technical Supplement Abstract: This study is a technical supplement to "AI gone astray: How subtle shifts in patient data send popular algorithms reeling, undermining patient safety." from STAT News, which investigates the effect of time drift on clinically deployed machine learning models. We us... |
Title: Explicitising The Implicit Intrepretability of Deep Neural Networks Via Duality Abstract: Recent work by Lakshminarayanan and Singh [2020] provided a dual view for fully connected deep neural networks (DNNs) with rectified linear units (ReLU). It was shown that (i) the information in the gates is analytically ch... |
Title: Convergence of gradient descent for deep neural networks Abstract: Optimization by gradient descent has been one of main drivers of the "deep learning revolution". Yet, despite some recent progress for extremely wide networks, it remains an open problem to understand why gradient descent often converges to globa... |
Title: Perfectly Accurate Membership Inference by a Dishonest Central Server in Federated Learning Abstract: Federated Learning is expected to provide strong privacy guarantees, as only gradients or model parameters but no plain text training data is ever exchanged either between the clients or between the clients and ... |
Title: Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning Abstract: Artificial intelligence, particularly through recent advancements in deep learning, has achieved exceptional performances in many tasks in fields such as natural language processing and computer vision. In addit... |
Title: Remember to correct the bias when using deep learning for regression! Abstract: When training deep learning models for least-squares regression, we cannot expect that the training error residuals of the final model, selected after a fixed training time or based on performance on a hold-out data set, sum to zero.... |
Title: Zero Shot Crosslingual Eye-Tracking Data Prediction using Multilingual Transformer Models Abstract: Eye tracking data during reading is a useful source of information to understand the cognitive processes that take place during language comprehension processes. Different languages account for different brain tri... |
Title: ConceptEvo: Interpreting Concept Evolution in Deep Learning Training Abstract: Deep neural networks (DNNs) have been widely used for decision making, prompting a surge of interest in interpreting how these complex models work. Recent literature on DNN interpretation has revolved around already-trained models; ho... |
Title: On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification Abstract: Aleatoric uncertainty captures the inherent randomness of the data, such as measurement noise. In Bayesian regression, we often use a Gaussian observation model, where we control the level of aleatoric uncertainty with a noise ... |
Title: Lossless Speedup of Autoregressive Translation with Generalized Aggressive Decoding Abstract: Different from previous work accelerating translation at the cost of quality loss, we propose Generalized Aggressive Decoding (GAD) -- a novel decoding paradigm for lossless speedup of autoregressive translation, throug... |
Title: Generative Spoken Dialogue Language Modeling Abstract: We introduce dGSLM, the first "textless" model able to generate audio samples of naturalistic spoken dialogues. It uses recent work on unsupervised spoken unit discovery coupled with a dual-tower transformer architecture with cross-attention trained on 2000 ... |
Title: An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection Abstract: Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor mask-wearin... |
Title: Collaborative Transformers for Grounded Situation Recognition Abstract: Grounded situation recognition is the task of predicting the main activity, entities playing certain roles within the activity, and bounding-box groundings of the entities in the given image. To effectively deal with this challenging task, w... |
Title: Parallel framework for Dynamic Domain Decomposition of Data Assimilation problems a case study on Kalman Filter algorithm Abstract: We focus on Partial Differential Equation (PDE) based Data Assimilatio problems (DA) solved by means of variational approaches and Kalman filter algorithm. Recently, we presented a ... |
Title: Recent improvements of ASR models in the face of adversarial attacks Abstract: Like many other tasks involving neural networks, Speech Recognition models are vulnerable to adversarial attacks. However recent research has pointed out differences between attacks and defenses on ASR models compared to image models.... |
Title: Efficient Localness Transformer for Smart Sensor-Based Energy Disaggregation Abstract: Modern smart sensor-based energy management systems leverage non-intrusive load monitoring (NILM) to predict and optimize appliance load distribution in real-time. NILM, or energy disaggregation, refers to the decomposition of... |
Title: Machine Learning Approaches for Non-Intrusive Home Absence Detection Based on Appliance Electrical Use Abstract: Home absence detection is an emerging field on smart home installations. Identifying whether or not the residents of the house are present, is important in numerous scenarios. Possible scenarios inclu... |
Title: Identification of diffracted vortex beams at different propagation distances using deep learning Abstract: Orbital angular momentum of light is regarded as a valuable resource in quantum technology, especially in quantum communication and quantum sensing and ranging. However, the OAM state of light is susceptibl... |
Title: Mind the gap: Challenges of deep learning approaches to Theory of Mind Abstract: Theory of Mind is an essential ability of humans to infer the mental states of others. Here we provide a coherent summary of the potential, current progress, and problems of deep learning approaches to Theory of Mind. We highlight t... |
Title: Generating Scientific Articles with Machine Learning Abstract: In recent years, the field of machine learning has seen rapid growth, with applications in a variety of domains, including image recognition, natural language processing, and predictive modeling. In this paper, we explore the application of machine l... |
Title: Graph Refinement for Coreference Resolution Abstract: The state-of-the-art models for coreference resolution are based on independent mention pair-wise decisions. We propose a modelling approach that learns coreference at the document-level and takes global decisions. For this purpose, we model coreference links... |
Title: Calibrating constitutive models with full-field data via physics informed neural networks Abstract: The calibration of solid constitutive models with full-field experimental data is a long-standing challenge, especially in materials which undergo large deformation. In this paper, we propose a physics-informed de... |
Title: Factored Adaptation for Non-Stationary Reinforcement Learning Abstract: Dealing with non-stationarity in environments (i.e., transition dynamics) and objectives (i.e., reward functions) is a challenging problem that is crucial in real-world applications of reinforcement learning (RL). Most existing approaches on... |
Title: Spatially Adaptive Online Prediction of Piecewise Regular Functions Abstract: We consider the problem of estimating piecewise regular functions in an online setting, i.e., the data arrive sequentially and at any round our task is to predict the value of the true function at the next revealed point using the avai... |
Title: Constrained Few-shot Class-incremental Learning Abstract: Continually learning new classes from fresh data without forgetting previous knowledge of old classes is a very challenging research problem. Moreover, it is imperative that such learning must respect certain memory and computational constraints such as (... |
Title: A Fast and Convergent Proximal Algorithm for Regularized Nonconvex and Nonsmooth Bi-level Optimization Abstract: Many important machine learning applications involve regularized nonconvex bi-level optimization. However, the existing gradient-based bi-level optimization algorithms cannot handle nonconvex or nonsm... |
Title: Federated Learning for the Classification of Tumor Infiltrating Lymphocytes Abstract: We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as the example use case, on quantifiying the distr... |
Title: Physics-constrained Unsupervised Learning of Partial Differential Equations using Meshes Abstract: Enhancing neural networks with knowledge of physical equations has become an efficient way of solving various physics problems, from fluid flow to electromagnetism. Graph neural networks show promise in accurately ... |
Title: Transformer Language Models without Positional Encodings Still Learn Positional Information Abstract: Transformers typically require some form of positional encoding, such as positional embeddings, to process natural language sequences. Surprisingly, we find that transformer language models without any explicit ... |
Title: Hybrid Handcrafted and Learnable Audio Representation for Analysis of Speech Under Cognitive and Physical Load Abstract: As a neurophysiological response to threat or adverse conditions, stress can affect cognition, emotion and behaviour with potentially detrimental effects on health in the case of sustained exp... |
Title: FALCON: Fast Visual Concept Learning by Integrating Images, Linguistic descriptions, and Conceptual Relations Abstract: We present a meta-learning framework for learning new visual concepts quickly, from just one or a few examples, guided by multiple naturally occurring data streams: simultaneously looking at im... |
Title: Data-driven Prediction of Relevant Scenarios for Robust Optimization Abstract: In this work we study robust one- and two-stage problems with discrete uncertainty sets which are known to be hard to solve even if the underlying deterministic problem is easy. Popular solution methods iteratively generate scenario c... |
Title: Generation of Speaker Representations Using Heterogeneous Training Batch Assembly Abstract: In traditional speaker diarization systems, a well-trained speaker model is a key component to extract representations from consecutive and partially overlapping segments in a long speech session. To be more consistent wi... |
Title: Predicting Winners of the Reality TV Dating Show $\textit{The Bachelor}$ Using Machine Learning Algorithms Abstract: $\textit{The Bachelor}$ is a reality TV dating show in which a single bachelor selects his wife from a pool of approximately 30 female contestants over eight weeks of filming (American Broadcastin... |
Title: Overcoming challenges in leveraging GANs for few-shot data augmentation Abstract: In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform an exploration into how a GAN can be fine-tuned for such a task (one of which is in a ... |
Title: Flexible and Efficient Contextual Bandits with Heterogeneous Treatment Effect Oracle Abstract: Many popular contextual bandit algorithms estimate reward models to inform decision making. However, true rewards can contain action-independent redundancies that are not relevant for decision making and only increase ... |
Title: System Identification via Nuclear Norm Regularization Abstract: This paper studies the problem of identifying low-order linear systems via Hankel nuclear norm regularization. Hankel regularization encourages the low-rankness of the Hankel matrix, which maps to the low-orderness of the system. We provide novel st... |
Title: Learning the Effect of Registration Hyperparameters with HyperMorph Abstract: We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformati... |
Title: To Find Waldo You Need Contextual Cues: Debiasing Who's Waldo Abstract: We present a debiased dataset for the Person-centric Visual Grounding (PCVG) task first proposed by Cui et al. (2021) in the Who's Waldo dataset. Given an image and a caption, PCVG requires pairing up a person's name mentioned in a caption w... |
Title: Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations Abstract: Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observed properties. Binary population synthesis with full stellar-structure and evolution simulation... |
Title: Quasi-orthogonality and intrinsic dimensions as measures of learning and generalisation Abstract: Finding best architectures of learning machines, such as deep neural networks, is a well-known technical and theoretical challenge. Recent work by Mellor et al (2021) showed that there may exist correlations between... |
Title: MAE-AST: Masked Autoencoding Audio Spectrogram Transformer Abstract: In this paper, we propose a simple yet powerful improvement over the recent Self-Supervised Audio Spectrogram Transformer (SSAST) model for speech and audio classification. Specifically, we leverage the insight that the SSAST uses a very high m... |
Title: Towards Differential Relational Privacy and its use in Question Answering Abstract: Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) to understand, quantify and control this phenomeno... |
Title: Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits Abstract: Variational quantum algorithms stand at the forefront of simulations on near-term and future fault-tolerant quantum devices. While most variational quantum algorithms involve only continuous optimization variables, the re... |
Title: Task Adaptive Parameter Sharing for Multi-Task Learning Abstract: Adapting pre-trained models with broad capabilities has become standard practice for learning a wide range of downstream tasks. The typical approach of fine-tuning different models for each task is performant, but incurs a substantial memory cost.... |
Title: An analytic theory for the dynamics of wide quantum neural networks Abstract: Parametrized quantum circuits can be used as quantum neural networks and have the potential to outperform their classical counterparts when trained for addressing learning problems. To date, much of the results on their performance on ... |
Title: Exploiting Explainable Metrics for Augmented SGD Abstract: Explaining the generalization characteristics of deep learning is an emerging topic in advanced machine learning. There are several unanswered questions about how learning under stochastic optimization really works and why certain strategies are better t... |
Title: SpecGrad: Diffusion Probabilistic Model based Neural Vocoder with Adaptive Noise Spectral Shaping Abstract: Neural vocoder using denoising diffusion probabilistic model (DDPM) has been improved by adaptation of the diffusion noise distribution to given acoustic features. In this study, we propose SpecGrad that a... |
Title: An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers Abstract: Learning knowledge representation of scientific paper data is a problem to be solved, and how to learn the representation of paper nodes in scientific paper heterogeneous network is... |
Title: An Exploration of Prompt Tuning on Generative Spoken Language Model for Speech Processing Tasks Abstract: Speech representations learned from Self-supervised learning (SSL) models can benefit various speech processing tasks. However, utilizing SSL representations usually requires fine-tuning the pre-trained mode... |
Title: Bangla hate speech detection on social media using attention-based recurrent neural network Abstract: Hate speech has spread more rapidly through the daily use of technology and, most notably, by sharing your opinions or feelings on social media in a negative aspect. Although numerous works have been carried out... |
Title: An Empirical Study of Language Model Integration for Transducer based Speech Recognition Abstract: Utilizing text-only data with an external language model (LM) in end-to-end RNN-Transducer (RNN-T) for speech recognition is challenging. Recently, a class of methods such as density ratio (DR) and ILM estimation (... |
Title: Mask Atari for Deep Reinforcement Learning as POMDP Benchmarks Abstract: We present Mask Atari, a new benchmark to help solve partially observable Markov decision process (POMDP) problems with Deep Reinforcement Learning (DRL)-based approaches. To achieve a simulation environment for the POMDP problems, Mask Ata... |
Title: ESGBERT: Language Model to Help with Classification Tasks Related to Companies Environmental, Social, and Governance Practices Abstract: Environmental, Social, and Governance (ESG) are non-financial factors that are garnering attention from investors as they increasingly look to apply these as part of their anal... |
Title: When Physics Meets Machine Learning: A Survey of Physics-Informed Machine Learning Abstract: Physics-informed machine learning (PIML), referring to the combination of prior knowledge of physics, which is the high level abstraction of natural phenomenons and human behaviours in the long history, with data-driven ... |
Title: Ternary and Binary Quantization for Improved Classification Abstract: Dimension reduction and data quantization are two important methods for reducing data complexity. In the paper, we study the methodology of first reducing data dimension by random projection and then quantizing the projections to ternary or bi... |
Title: Robust Meta-Reinforcement Learning with Curriculum-Based Task Sampling Abstract: Meta-reinforcement learning (meta-RL) acquires meta-policies that show good performance for tasks in a wide task distribution. However, conventional meta-RL, which learns meta-policies by randomly sampling tasks, has been reported t... |
Title: Adaptive Estimation of Random Vectors with Bandit Feedback Abstract: We consider the problem of sequentially learning to estimate, in the mean squared error (MSE) sense, a Gaussian $K$-vector of unknown covariance by observing only $m < K$ of its entries in each round. This reduces to learning an optimal subset ... |
Title: How Does Pre-trained Wav2Vec2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control Communications Abstract: Recent work on self-supervised pre-training focus on leveraging large-scale unlabeled speech data to build robust end-to-end (E2E) acoustic models (AM) that can be later fine-tune... |
Title: Towards Driving-Oriented Metric for Lane Detection Models Abstract: After the 2017 TuSimple Lane Detection Challenge, its dataset and evaluation based on accuracy and F1 score have become the de facto standard to measure the performance of lane detection methods. While they have played a major role in improving ... |
Title: JETS: Jointly Training FastSpeech2 and HiFi-GAN for End to End Text to Speech Abstract: In neural text-to-speech (TTS), two-stage system or a cascade of separately learned models have shown synthesis quality close to human speech. For example, FastSpeech2 transforms an input text to a mel-spectrogram and then Hi... |
Title: Ransomware Detection using Process Memory Abstract: Ransomware attacks have increased significantly in recent years, causing great destruction and damage to critical systems and business operations. Attackers are unfailingly finding innovative ways to bypass detection mechanisms, whichencouraged the adoption of ... |
Title: An Optimal Control Method to Compute the Most Likely Transition Path for Stochastic Dynamical Systems with Jumps Abstract: Many complex real world phenomena exhibit abrupt, intermittent or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian L\'evy no... |
Title: Mutual information estimation for graph convolutional neural networks Abstract: Measuring model performance is a key issue for deep learning practitioners. However, we often lack the ability to explain why a specific architecture attains superior predictive accuracy for a given data set. Often, validation accura... |
Title: A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences Abstract: Some exciting new approaches to neural architectures for the analysis of conversation have been introduced over the past couple of years. These include neural architectures for dete... |
Title: MBORE: Multi-objective Bayesian Optimisation by Density-Ratio Estimation Abstract: Optimisation problems often have multiple conflicting objectives that can be computationally and/or financially expensive. Mono-surrogate Bayesian optimisation (BO) is a popular model-based approach for optimising such black-box f... |
Title: Assessing the risk of re-identification arising from an attack on anonymised data Abstract: Objective: The use of routinely-acquired medical data for research purposes requires the protection of patient confidentiality via data anonymisation. The objective of this work is to calculate the risk of re-identificati... |
Title: Neural Architecture Search for Speech Emotion Recognition Abstract: Deep neural networks have brought significant advancements to speech emotion recognition (SER). However, the architecture design in SER is mainly based on expert knowledge and empirical (trial-and-error) evaluations, which is time-consuming and ... |
Title: WavThruVec: Latent speech representation as intermediate features for neural speech synthesis Abstract: Recent advances in neural text-to-speech research have been dominated by two-stage pipelines utilizing low-level intermediate speech representation such as mel-spectrograms. However, such predetermined feature... |
Title: Learning from few examples with nonlinear feature maps Abstract: In this work we consider the problem of data classification in post-classical settings were the number of training examples consists of mere few data points. We explore the phenomenon and reveal key relationships between dimensionality of AI model'... |
Title: HiFi-VC: High Quality ASR-Based Voice Conversion Abstract: The goal of voice conversion (VC) is to convert input voice to match the target speaker's voice while keeping text and prosody intact. VC is usually used in entertainment and speaking-aid systems, as well as applied for speech data generation and augment... |
Title: Hypergraph Convolutional Networks via Equivalency between Hypergraphs and Undirected Graphs Abstract: As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity from the graph learning community. However, commonly used frameworks in deep hypergraph learning focus on hypergraphs wit... |
Title: Direction of Arrival Estimation of Sound Sources Using Icosahedral CNNs Abstract: In this paper, we present a new model for Direction of Arrival (DOA) estimation of sound sources based on an Icosahedral Convolutional Neural Network (CNN) applied over SRP-PHAT power maps computed from the signals received by a mi... |
Title: A unified theory of learning Abstract: Recently machine learning using neural networks (NN) has been developed, and many new methods have been suggested. These methods are optimized for the type of input data and work very effectively, but they cannot be used with any kind of input data universally. On the other... |
Title: A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor Abstract: In the present paper a new data-driven model to close and increase accuracy of RANS equations is proposed. It is based on the direct approximation of the divergence of the Reynolds Stress Tensor (RST) ... |
Title: Multimodal Fusion Transformer for Remote Sensing Image Classification Abstract: Vision transformer (ViT) has been trending in image classification tasks due to its promising performance when compared to convolutional neural networks (CNNs). As a result, many researchers have tried to incorporate ViT models in hy... |
Title: PADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech Representations Abstract: While self-supervised speech representation learning (SSL) models serve a variety of downstream tasks, these models have been observed to overfit to the domain from which the unlabelled data originates. To alleviate thi... |
Title: Acoustic-Net: A Novel Neural Network for Sound Localization and Quantification Abstract: Acoustic source localization has been applied in different fields, such as aeronautics and ocean science, generally using multiple microphones array data to reconstruct the source location. However, the model-based beamformi... |
Title: Message Passing Neural Networks for Hypergraphs Abstract: Hypergraph representations are both more efficient and better suited to describe data characterized by relations between two or more objects. In this work, we present a new graph neural network based on message passing capable of processing hypergraph-str... |
Title: SingAug: Data Augmentation for Singing Voice Synthesis with Cycle-consistent Training Strategy Abstract: Deep learning based singing voice synthesis (SVS) systems have been demonstrated to flexibly generate singing with better qualities, compared to conventional statistical parametric based methods. However, neu... |
Title: Conditional Autoregressors are Interpretable Classifiers Abstract: We explore the use of class-conditional autoregressive (CA) models to perform image classification on MNIST-10. Autoregressive models assign probability to an entire input by combining probabilities from each individual feature; hence classificat... |
Title: Equivariant Diffusion for Molecule Generation in 3D Abstract: This work introduces a diffusion model for molecule generation in 3D that is equivariant to Euclidean transformations. Our E(3) Equivariant Diffusion Model (EDM) learns to denoise a diffusion process with an equivariant network that jointly operates o... |
Title: Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain Abstract: Score-based generative models (SGMs) have recently shown impressive results for difficult generative tasks such as the unconditional and conditional generation of natural images and audio signals. In this work, we extend t... |
Title: It's All In the Teacher: Zero-Shot Quantization Brought Closer to the Teacher Abstract: Model quantization is considered as a promising method to greatly reduce the resource requirements of deep neural networks. To deal with the performance drop induced by quantization errors, a popular method is to use training... |
Title: A Temporal-oriented Broadcast ResNet for COVID-19 Detection Abstract: Detecting COVID-19 from audio signals, such as breathing and coughing, can be used as a fast and efficient pre-testing method to reduce the virus transmission. Due to the promising results of deep learning networks in modelling time sequences,... |
Title: DeepFry: Identifying Vocal Fry Using Deep Neural Networks Abstract: Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. It occurs in diverse languages and is prevalent in American English, where it is used not only to mark phrase finality, but also sociol... |
Title: CTA-RNN: Channel and Temporal-wise Attention RNN Leveraging Pre-trained ASR Embeddings for Speech Emotion Recognition Abstract: Previous research has looked into ways to improve speech emotion recognition (SER) by utilizing both acoustic and linguistic cues of speech. However, the potential association between s... |
Title: Flat-topped Probability Density Functions for Mixture Models Abstract: This paper investigates probability density functions (PDFs) that are continuous everywhere, nearly uniform around the mode of distribution, and adaptable to a variety of distribution shapes ranging from bell-shaped to rectangular. From the v... |
Title: Differentially Private Federated Learning via Reconfigurable Intelligent Surface Abstract: Federated learning (FL), as a disruptive machine learning paradigm, enables the collaborative training of a global model over decentralized local datasets without sharing them. It spans a wide scope of applications from In... |
Title: Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks Abstract: New classes arise frequently in our ever-changing world, e.g., emerging topics in social media and new types of products in e-commerce. A model should recognize new classes and meanwhile maintain discriminability over old classes. Under ... |
Title: Training strategy for a lightweight countermeasure model for automatic speaker verification Abstract: The countermeasure (CM) model is developed to protect Automatic Speaker Verification (ASV) systems from spoof attacks and prevent resulting personal information leakage. Based on practicality and security consid... |
Title: Certified machine learning: A posteriori error estimation for physics-informed neural networks Abstract: Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge about physical systems into the learning framework. PINNs are known to be robust for smaller training sets, ... |
Title: Wind Farm Layout Optimisation using Set Based Multi-objective Bayesian Optimisation Abstract: Wind energy is one of the cleanest renewable electricity sources and can help in addressing the challenge of climate change. One of the drawbacks of wind-generated energy is the large space necessary to install a wind f... |
Title: Cross-modal Learning of Graph Representations using Radar Point Cloud for Long-Range Gesture Recognition Abstract: Gesture recognition is one of the most intuitive ways of interaction and has gathered particular attention for human computer interaction. Radar sensors possess multiple intrinsic properties, such a... |
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