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Title: Fundamental limits to learning closed-form mathematical models from data Abstract: Given a finite and noisy dataset generated with a closed-form mathematical model, when is it possible to learn the true generating model from the data alone? This is the question we investigate here. We show that this model-learni... |
Title: Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck Abstract: Adversarial examples, generated by carefully crafted perturbation, have attracted considerable attention in research fields. Recent works have argued that the existence of the robust and non-robust features is a... |
Title: Neural Network-augmented Kalman Filtering for Robust Online Speech Dereverberation in Noisy Reverberant Environments Abstract: In this paper, a neural network-augmented algorithm for noise-robust online dereverberation with a Kalman filtering variant of the weighted prediction error (WPE) method is proposed. The... |
Title: Data-Centric Green AI: An Exploratory Empirical Study Abstract: With the growing availability of large-scale datasets, and the popularization of affordable storage and computational capabilities, the energy consumed by AI is becoming a growing concern. To address this issue, in recent years, studies have focused... |
Title: Walk this Way! Entity Walks and Property Walks for RDF2vec Abstract: RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm word2vec for computing vector representations for entities. In ... |
Title: A Dempster-Shafer approach to trustworthy AI with application to fetal brain MRI segmentation Abstract: Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and for images acquired at different centers than those used for training, with labeling error... |
Title: How Do Graph Networks Generalize to Large and Diverse Molecular Systems? Abstract: The predominant method of demonstrating progress of atomic graph neural networks are benchmarks on small and limited datasets. The implicit hypothesis behind this approach is that progress on these narrow datasets generalize to th... |
Title: Reinforcement Learning Agents in Colonel Blotto Abstract: Models and games are simplified representations of the world. There are many different kinds of models, all differing in complexity and which aspect of the world they allow us to further our understanding of. In this paper we focus on a specific instance ... |
Title: Classification of NEQR Processed Classical Images using Quantum Neural Networks (QNN) Abstract: A quantum neural network (QNN) is interpreted today as any quantum circuit with trainable continuous parameters. This work builds on previous works by the authors and addresses QNN for image classification with Novel ... |
Title: Dimensionality Expansion and Transfer Learning for Next Generation Energy Management Systems Abstract: Electrical management systems (EMS) are playing a central role in enabling energy savings. They can be deployed within an everyday household where they monitor and manage appliances and help residents be more e... |
Title: A Transformer-Based Contrastive Learning Approach for Few-Shot Sign Language Recognition Abstract: Sign language recognition from sequences of monocular images or 2D poses is a challenging field, not only due to the difficulty to infer 3D information from 2D data, but also due to the temporal relationship betwee... |
Title: Federated Self-supervised Speech Representations: Are We There Yet? Abstract: The ubiquity of microphone-enabled devices has lead to large amounts of unlabelled audio data being produced at the edge. The integration of self-supervised learning (SSL) and federated learning (FL) into one coherent system can potent... |
Title: High Probability Bounds for a Class of Nonconvex Algorithms with AdaGrad Stepsize Abstract: In this paper, we propose a new, simplified high probability analysis of AdaGrad for smooth, non-convex problems. More specifically, we focus on a particular accelerated gradient (AGD) template (Lan, 2020), through which ... |
Title: KNN-Diffusion: Image Generation via Large-Scale Retrieval Abstract: While the availability of massive Text-Image datasets is shown to be extremely useful in training large-scale generative models (e.g. DDPMs, Transformers), their output typically depends on the quality of both the input text, as well as the trai... |
Title: PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations Abstract: Deep Reinforcement Learning (DRL) has been a promising solution to many complex decision-making problems. Nevertheless, the notorious weakness in generalization among environments p... |
Title: Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks Abstract: The field of Natural Language Processing has experienced a dramatic leap in capabilities with the recent introduction of huge Language Models. Despite this success, natural language problems that involve several compounded steps are ... |
Title: Efficient Bayesian Network Structure Learning via Parameterized Local Search on Topological Orderings Abstract: In Bayesian Network Structure Learning (BNSL), one is given a variable set and parent scores for each variable and aims to compute a DAG, called Bayesian network, that maximizes the sum of parent score... |
Title: Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask Abstract: Reading is integral to everyday life, and yet learning to read is a struggle for many young learners. During lessons, teachers can use comprehension questions to increase engagement, test reading skills, and improv... |
Title: A survey on recently proposed activation functions for Deep Learning Abstract: Artificial neural networks (ANN), typically referred to as neural networks, are a class of Machine Learning algorithms and have achieved widespread success, having been inspired by the biological structure of the human brain. Neural n... |
Title: Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations Abstract: Neural network classifiers can largely rely on simple spurious features, such as backgrounds, to make predictions. However, even in these cases, we show that they still often learn core features associated with the desired att... |
Title: Marrying Fairness and Explainability in Supervised Learning Abstract: Machine learning algorithms that aid human decision-making may inadvertently discriminate against certain protected groups. We formalize direct discrimination as a direct causal effect of the protected attributes on the decisions, while induce... |
Title: Guaranteed Bounds for Posterior Inference in Universal Probabilistic Programming Abstract: We propose a new method to approximate the posterior distribution of probabilistic programs by means of computing guaranteed bounds. The starting point of our work is an interval-based trace semantics for a recursive, high... |
Title: Simple and Effective Synthesis of Indoor 3D Scenes Abstract: We study the problem of synthesizing immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints, including viewpoints that extrapolate far beyond the input images while maintaining... |
Title: LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification Abstract: We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off. Prior works approach these problems one at a time and o... |
Title: Multi-task nonparallel support vector machine for classification Abstract: Direct multi-task twin support vector machine (DMTSVM) explores the shared information between multiple correlated tasks, then it produces better generalization performance. However, it contains matrix inversion operation when solving the... |
Title: Incremental Unsupervised Feature Selection for Dynamic Incomplete Multi-view Data Abstract: Multi-view unsupervised feature selection has been proven to be efficient in reducing the dimensionality of multi-view unlabeled data with high dimensions. The previous methods assume all of the views are complete. Howeve... |
Title: End-To-End Optimization of Online Neural Network-supported Two-Stage Dereverberation for Hearing Devices Abstract: A two-stage online dereverberation algorithm for hearing devices is presented in this paper. The approach combines a multi-channel multi-frame linear filtering approach with a single-channel single-... |
Title: Federated Learning for Distributed Spectrum Sensing in NextG Communication Networks Abstract: NextG networks are intended to provide the flexibility of sharing the spectrum with incumbent users and support various spectrum monitoring tasks such as anomaly detection, fault diagnostics, user equipment identificati... |
Title: Statistical Model Criticism of Variational Auto-Encoders Abstract: We propose a framework for the statistical evaluation of variational auto-encoders (VAEs) and test two instances of this framework in the context of modelling images of handwritten digits and a corpus of English text. Our take on evaluation is ba... |
Title: SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis Abstract: In this work, we present the SOMOS dataset, the first large-scale mean opinion scores (MOS) dataset consisting of solely neural text-to-speech (TTS) samples. It can be employed to train automatic MOS prediction sy... |
Title: Fusing finetuned models for better pretraining Abstract: Pretrained models are the standard starting point for training. This approach consistently outperforms the use of a random initialization. However, pretraining is a costly endeavour that few can undertake. In this paper, we create better base models at har... |
Title: Perceive, Represent, Generate: Translating Multimodal Information to Robotic Motion Trajectories Abstract: We present Perceive-Represent-Generate (PRG), a novel three-stage framework that maps perceptual information of different modalities (e.g., visual or sound), corresponding to a sequence of instructions, to ... |
Title: Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem Abstract: We propose a train rescheduling algorithm which applies a standardized feature selection based on pairwise conflicts in order to serve as input for the reinforcement learning framework. We implement an ana... |
Title: Graph Neural Networks Designed for Different Graph Types: A Survey Abstract: Graphs are ubiquitous in nature and can therefore serve as models for many practical but also theoretical problems. Based on this, the young research field of Graph Neural Networks (GNNs) has emerged. Despite the youth of the field and ... |
Title: Knowledge Infused Decoding Abstract: Pre-trained language models (LMs) have been shown to memorize a substantial amount of knowledge from the pre-training corpora; however, they are still limited in recalling factually correct knowledge given a certain context. Hence, they tend to suffer from counterfactual or h... |
Title: Data Justice Stories: A Repository of Case Studies Abstract: The idea of "data justice" is of recent academic vintage. It has arisen over the past decade in Anglo-European research institutions as an attempt to bring together a critique of the power dynamics that underlie accelerating trends of datafication with... |
Title: AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesis Abstract: In this paper, we address the problem of texture representation for 3D shapes for the challenging and underexplored tasks of texture transfer and synthesis. Previous works either apply spherical texture maps which may lead to large di... |
Title: Deep transfer learning for system identification using long short-term memory neural networks Abstract: Recurrent neural networks (RNNs) have many advantages over more traditional system identification techniques. They may be applied to linear and nonlinear systems, and they require fewer modeling assumptions. H... |
Title: First-Order Algorithms for Nonlinear Generalized Nash Equilibrium Problems Abstract: We consider the problem of computing an equilibrium in a class of nonlinear generalized Nash equilibrium problems (NGNEPs) in which the strategy sets for each player are defined by equality and inequality constraints that may de... |
Title: DiffCloud: Real-to-Sim from Point Clouds with Differentiable Simulation and Rendering of Deformable Objects Abstract: Research in manipulation of deformable objects is typically conducted on a limited range of scenarios, because handling each scenario on hardware takes significant effort. Realistic simulators wi... |
Title: Learning and Transferring Value Function for Robot Exploration in Subterranean Environments Abstract: In traditional robot exploration methods, the robot usually does not have prior biases about the environment it is exploring. Thus the robot assigns equal importance to the goals which leads to insufficient expl... |
Title: DeepTensor: Low-Rank Tensor Decomposition with Deep Network Priors Abstract: DeepTensor is a computationally efficient framework for low-rank decomposition of matrices and tensors using deep generative networks. We decompose a tensor as the product of low-rank tensor factors (e.g., a matrix as the outer product ... |
Title: Optimization Models and Interpretations for Three Types of Adversarial Perturbations against Support Vector Machines Abstract: Adversarial perturbations have drawn great attentions in various deep neural networks. Most of them are computed by iterations and cannot be interpreted very well. In contrast, little at... |
Title: Enhancement on Model Interpretability and Sleep Stage Scoring Performance with A Novel Pipeline Based on Deep Neural Network Abstract: Considering the natural frequency characteristics in sleep medicine, this paper first proposes a time-frequency framework for the representation learning of the electroencephalog... |
Title: FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvement Abstract: As an emerging technology, federated learning (FL) involves training machine learning models over distributed edge devices, which attracts sustained attention and has been extensively studied. However, the heterogen... |
Title: Distributed Statistical Min-Max Learning in the Presence of Byzantine Agents Abstract: Recent years have witnessed a growing interest in the topic of min-max optimization, owing to its relevance in the context of generative adversarial networks (GANs), robust control and optimization, and reinforcement learning.... |
Title: MultiAuto-DeepONet: A Multi-resolution Autoencoder DeepONet for Nonlinear Dimension Reduction, Uncertainty Quantification and Operator Learning of Forward and Inverse Stochastic Problems Abstract: A new data-driven method for operator learning of stochastic differential equations(SDE) is proposed in this paper. ... |
Title: A Joint Learning Approach for Semi-supervised Neural Topic Modeling Abstract: Topic models are some of the most popular ways to represent textual data in an interpret-able manner. Recently, advances in deep generative models, specifically auto-encoding variational Bayes (AEVB), have led to the introduction of un... |
Title: Transformer-Based Language Models for Software Vulnerability Detection: Performance, Model's Security and Platforms Abstract: The large transformer-based language models demonstrate excellent performance in natural language processing. By considering the closeness of natural languages to the high-level programmi... |
Title: Neural Implicit Flow: a mesh-agnostic dimensionality reduction paradigm of spatio-temporal data Abstract: High-dimensional spatio-temporal dynamics can often be encoded in a low-dimensional subspace. Engineering applications for modeling, characterization, design, and control of such large-scale systems often re... |
Title: DDOS: A MOS Prediction Framework utilizing Domain Adaptive Pre-training and Distribution of Opinion Scores Abstract: Mean opinion score (MOS) is a typical subjective evaluation metric for speech synthesis systems. Since collecting MOS is time-consuming, it would be desirable if there are accurate MOS prediction ... |
Title: Explicit Feature Interaction-aware Graph Neural Networks Abstract: Graph neural networks are powerful methods to handle graph-structured data. However, existing graph neural networks only learn higher-order feature interactions implicitly. Thus, they cannot capture information that occurred in low-order feature ... |
Title: Accelerating Attention through Gradient-Based Learned Runtime Pruning Abstract: Self-attention is a key enabler of state-of-art accuracy for various transformer-based Natural Language Processing models. This attention mechanism calculates a correlation score for each word with respect to the other words in a sen... |
Title: What You See is What You Get: Distributional Generalization for Algorithm Design in Deep Learning Abstract: We investigate and leverage a connection between Differential Privacy (DP) and the recently proposed notion of Distributional Generalization (DG). Applying this connection, we introduce new conceptual tool... |
Title: Learning to Solve Travelling Salesman Problem with Hardness-adaptive Curriculum Abstract: Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learning-based TSP methods adopt a simple setting that the training and ... |
Title: Pretraining Text Encoders with Adversarial Mixture of Training Signal Generators Abstract: We present a new framework AMOS that pretrains text encoders with an Adversarial learning curriculum via a Mixture Of Signals from multiple auxiliary generators. Following ELECTRA-style pretraining, the main encoder is tra... |
Title: Composite Spatial Monte Carlo Integration Based on Generalized Least Squares Abstract: Although evaluation of the expectations on the Ising model is essential in various applications, this is frequently infeasible because of intractable multiple summations (or integrations). Spatial Monte Carlo integration (SMCI... |
Title: mulEEG: A Multi-View Representation Learning on EEG Signals Abstract: Modeling effective representations using multiple views that positively influence each other is challenging, and the existing methods perform poorly on Electroencephalogram (EEG) signals for sleep-staging tasks. In this paper, we propose a nov... |
Title: PALBERT: Teaching ALBERT to Ponder Abstract: Currently, pre-trained models can be considered the default choice for a wide range of NLP tasks. Despite their SoTA results, there is practical evidence that these models may require a different number of computing layers for different input sequences, since evaluati... |
Title: Enhancing Semantic Code Search with Multimodal Contrastive Learning and Soft Data Augmentation Abstract: Code search aims to retrieve the most semantically relevant code snippet for a given natural language query. Recently, large-scale code pre-trained models such as CodeBERT and GraphCodeBERT learn generic repr... |
Title: Federated Learning from Only Unlabeled Data with Class-Conditional-Sharing Clients Abstract: Supervised federated learning (FL) enables multiple clients to share the trained model without sharing their labeled data. However, potential clients might even be reluctant to label their own data, which could limit the... |
Title: MBI-Net: A Non-Intrusive Multi-Branched Speech Intelligibility Prediction Model for Hearing Aids Abstract: Improving the user's hearing ability to understand speech in noisy environments is critical to the development of hearing aid (HA) devices. For this, it is important to derive a metric that can fairly predi... |
Title: MTI-Net: A Multi-Target Speech Intelligibility Prediction Model Abstract: Recently, deep learning (DL)-based non-intrusive speech assessment models have attracted great attention. Many studies report that these DL-based models yield satisfactory assessment performance and good flexibility, but their performance ... |
Title: Using Decision Tree as Local Interpretable Model in Autoencoder-based LIME Abstract: Nowadays, deep neural networks are being used in many domains because of their high accuracy results. However, they are considered as "black box", means that they are not explainable for humans. On the other hand, in some tasks ... |
Title: Multi-Sample $\zeta$-mixup: Richer, More Realistic Synthetic Samples from a $p$-Series Interpolant Abstract: Modern deep learning training procedures rely on model regularization techniques such as data augmentation methods, which generate training samples that increase the diversity of data and richness of labe... |
Title: Enabling Deep Learning for All-in EDGE paradigm Abstract: Deep Learning-based models have been widely investigated, and they have demonstrated significant performance on non-trivial tasks such as speech recognition, image processing, and natural language understanding. However, this is at the cost of substantial... |
Title: Robust and Explainable Autoencoders for Unsupervised Time Series Outlier Detection---Extended Version Abstract: Time series data occurs widely, and outlier detection is a fundamental problem in data mining, which has numerous applications. Existing autoencoder-based approaches deliver state-of-the-art performanc... |
Title: Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift Abstract: The performance of a machine learning model degrades when it is applied to data from a similar but different domain than the data it has initially been trained on. To mitigate this domain shift problem, domain adaptation (DA) ... |
Title: Inference over radiative transfer models using variational and expectation maximization methods Abstract: Earth observation from satellites offers the possibility to monitor our planet with unprecedented accuracy. Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to... |
Title: Offline Reinforcement Learning for Safer Blood Glucose Control in People with Type 1 Diabetes Abstract: Hybrid closed loop systems represent the future of care for people with type 1 diabetes (T1D). These devices usually utilise simple control algorithms to select the optimal insulin dose for maintaining blood g... |
Title: Correcting Misproducted Speech using Spectrogram Inpainting Abstract: Learning a new language involves constantly comparing speech productions with reference productions from the environment. Early in speech acquisition, children make articulatory adjustments to match their caregivers' speech. Grownup learners o... |
Title: Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch Abstract: We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs) in PyTorch, a class of Neural Networks designed specifically for efficient inference in both online an... |
Title: Self supervised learning for robust voice cloning Abstract: Voice cloning is a difficult task which requires robust and informative features incorporated in a high quality TTS system in order to effectively copy an unseen speaker's voice. In our work, we utilize features learned in a self-supervised framework vi... |
Title: Energy-Efficient Adaptive Machine Learning on IoT End-Nodes With Class-Dependent Confidence Abstract: Energy-efficient machine learning models that can run directly on edge devices are of great interest in IoT applications, as they can reduce network pressure and response latency, and improve privacy. An effecti... |
Title: Machine Learning-Enabled IoT Security: Open Issues and Challenges Under Advanced Persistent Threats Abstract: Despite its technological benefits, Internet of Things (IoT) has cyber weaknesses due to the vulnerabilities in the wireless medium. Machine learning (ML)-based methods are widely used against cyber thre... |
Title: Half-sibling regression meets exoplanet imaging: PSF modeling and subtraction using a flexible, domain knowledge-driven, causal framework Abstract: High-contrast imaging of exoplanets hinges on powerful post-processing methods to denoise the data and separate the signal of a companion from its host star, which i... |
Title: Few-Shot Forecasting of Time-Series with Heterogeneous Channels Abstract: Learning complex time series forecasting models usually requires a large amount of data, as each model is trained from scratch for each task/data set. Leveraging learning experience with similar datasets is a well-established technique for... |
Title: Video Diffusion Models Abstract: Generating temporally coherent high fidelity video is an important milestone in generative modeling research. We make progress towards this milestone by proposing a diffusion model for video generation that shows very promising initial results. Our model is a natural extension of... |
Title: BERTuit: Understanding Spanish language in Twitter through a native transformer Abstract: The appearance of complex attention-based language models such as BERT, Roberta or GPT-3 has allowed to address highly complex tasks in a plethora of scenarios. However, when applied to specific domains, these models encoun... |
Title: Jacobian Norm for Unsupervised Source-Free Domain Adaptation Abstract: Unsupervised Source (data) Free domain adaptation (USFDA) aims to transfer knowledge from a well-trained source model to a related but unlabeled target domain. In such a scenario, all conventional adaptation methods that require source data f... |
Title: DynLight: Realize dynamic phase duration with multi-level traffic signal control Abstract: Adopting reinforcement learning (RL) for traffic signal control (TSC) is increasingly popular, and RL has become a promising solution for traffic signal control. However, several challenges still need to be overcome. First... |
Title: Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results Abstract: ImageNet serves as the primary dataset for evaluating the quality of computer-vision models. The common practice today is training each architecture with a tailor-made scheme, designed and tuned by an expert. In this paper, we ... |
Title: Delta Keyword Transformer: Bringing Transformers to the Edge through Dynamically Pruned Multi-Head Self-Attention Abstract: Multi-head self-attention forms the core of Transformer networks. However, their quadratically growing complexity with respect to the input sequence length impedes their deployment on resou... |
Title: Optimizing the Long-Term Behaviour of Deep Reinforcement Learning for Pushing and Grasping Abstract: We investigate the "Visual Pushing for Grasping" (VPG) system by Zeng et al. and the "Hourglass" system by Ewerton et al., an evolution of the former. The focus of our work is the investigation of the capabilitie... |
Title: Position-based Prompting for Health Outcome Generation Abstract: Probing Pre-trained Language Models (PLMs) using prompts has indirectly implied that language models (LMs) can be treated as knowledge bases. To this end, this phenomena has been effective especially when these LMs are fine-tuned towards not just d... |
Title: Covariance matrix preparation for quantum principal component analysis Abstract: Principal component analysis (PCA) is a dimensionality reduction method in data analysis that involves diagonalizing the covariance matrix of the dataset. Recently, quantum algorithms have been formulated for PCA based on diagonaliz... |
Title: Generalised Latent Assimilation in Heterogeneous Reduced Spaces with Machine Learning Surrogate Models Abstract: Reduced-order modelling and low-dimensional surrogate models generated using machine learning algorithms have been widely applied in high-dimensional dynamical systems to improve the algorithmic effic... |
Title: On the Effectiveness of Pretrained Models for API Learning Abstract: Developers frequently use APIs to implement certain functionalities, such as parsing Excel Files, reading and writing text files line by line, etc. Developers can greatly benefit from automatic API usage sequence generation based on natural lan... |
Title: Multi-Task Distributed Learning using Vision Transformer with Random Patch Permutation Abstract: The widespread application of artificial intelligence in health research is currently hampered by limitations in data availability. Distributed learning methods such as federated learning (FL) and shared learning (SL... |
Title: Survey on Automated Short Answer Grading with Deep Learning: from Word Embeddings to Transformers Abstract: Automated short answer grading (ASAG) has gained attention in education as a means to scale educational tasks to the growing number of students. Recent progress in Natural Language Processing and Machine L... |
Title: AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles Abstract: Due to the rapid growth of data transmissions in internet of vehicles (IoV), finding schemes that can effectively alleviate access congestion has become an important issue. Recently, many traffic control schemes have bee... |
Title: Interval Bound Propagation$\unicode{x2013}$aided Few$\unicode{x002d}$shot Learning Abstract: Few-shot learning aims to transfer the knowledge acquired from training on a diverse set of tasks, from a given task distribution, to generalize to unseen tasks, from the same distribution, with a limited amount of label... |
Title: Distributed Reinforcement Learning for Robot Teams: A Review Abstract: Purpose of review: Recent advances in sensing, actuation, and computation have opened the door to multi-robot systems consisting of hundreds/thousands of robots, with promising applications to automated manufacturing, disaster relief, harvest... |
Title: Temporal Alignment for History Representation in Reinforcement Learning Abstract: Environments in Reinforcement Learning are usually only partially observable. To address this problem, a possible solution is to provide the agent with information about the past. However, providing complete observations of numerou... |
Title: Visualizing Deep Neural Networks with Topographic Activation Maps Abstract: Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. The success of DNNs is strongly connected to their high complexity in terms of the number of network la... |
Title: FedADMM: A Robust Federated Deep Learning Framework with Adaptivity to System Heterogeneity Abstract: Federated Learning (FL) is an emerging framework for distributed processing of large data volumes by edge devices subject to limited communication bandwidths, heterogeneity in data distributions and computationa... |
Title: RF Signal Transformation and Classification using Deep Neural Networks Abstract: Deep neural networks (DNNs) designed for computer vision and natural language processing tasks cannot be directly applied to the radio frequency (RF) datasets. To address this challenge, we propose to convert the raw RF data to data... |
Title: Adaptive Spike-Like Representation of EEG Signals for Sleep Stages Scoring Abstract: Recently there has seen promising results on automatic stage scoring by extracting spatio-temporal features from electroencephalogram (EEG). Such methods entail laborious manual feature engineering and domain knowledge. In this ... |
Title: Faster algorithms for learning to link, align sequences, and price two-part tariffs Abstract: Data-driven algorithm configuration is a promising, learning-based approach for beyond worst-case analysis of algorithms with tunable parameters. An important open problem is the design of efficient data-driven algorith... |
Title: Improving Urban Mobility: using artificial intelligence and new technologies to connect supply and demand Abstract: As the demand for mobility in our society seems to increase, the various issues centered on urban mobility are among those that worry most city inhabitants in this planet. For instance, how to go f... |
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