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Title: Multimodal Indoor Localisation for Measuring Mobility in Parkinson's Disease using Transformers Abstract: Parkinson's disease (PD) is a slowly progressive debilitating neurodegenerative disease which is prominently characterised by motor symptoms. Indoor localisation, including number and speed of room to room t... |
Title: Smooth-Reduce: Leveraging Patches for Improved Certified Robustness Abstract: Randomized smoothing (RS) has been shown to be a fast, scalable technique for certifying the robustness of deep neural network classifiers. However, methods based on RS require augmenting data with large amounts of noise, which leads t... |
Title: Neural Network-based OFDM Receiver for Resource Constrained IoT Devices Abstract: Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transce... |
Title: Localized Vision-Language Matching for Open-vocabulary Object Detection Abstract: In this work, we propose an open-world object detection method that, based on image-caption pairs, learns to detect novel object classes along with a given set of known classes. It is a two-stage training approach that first uses a... |
Title: A Generalist Agent Abstract: Inspired by progress in large-scale language modeling, we apply a similar approach towards building a single generalist agent beyond the realm of text outputs. The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same netwo... |
Title: Ensemble Classifier Design Tuned to Dataset Characteristics for Network Intrusion Detection Abstract: Machine Learning-based supervised approaches require highly customized and fine-tuned methodologies to deliver outstanding performance. This paper presents a dataset-driven design and performance evaluation of a... |
Title: Improved Meta Learning for Low Resource Speech Recognition Abstract: We propose a new meta learning based framework for low resource speech recognition that improves the previous model agnostic meta learning (MAML) approach. The MAML is a simple yet powerful meta learning approach. However, the MAML presents som... |
Title: Image Segmentation with Topological Priors Abstract: Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors both before and during the deep neural network training procedure. We compared the results of the two approaches wi... |
Title: Embodied vision for learning object representations Abstract: Recent time-contrastive learning approaches manage to learn invariant object representations without supervision. This is achieved by mapping successive views of an object onto close-by internal representations. When considering this learning approach... |
Title: kNN-Embed: Locally Smoothed Embedding Mixtures For Multi-interest Candidate Retrieval Abstract: Candidate generation is the first stage in recommendation systems, where a light-weight system is used to retrieve potentially relevant items for an input user. These candidate items are then ranked and pruned in late... |
Title: Contingency-constrained economic dispatch with safe reinforcement learning Abstract: Future power systems will rely heavily on micro grids with a high share of decentralised renewable energy sources and energy storage systems. The high complexity and uncertainty in this context might make conventional power disp... |
Title: Exploiting symmetry in variational quantum machine learning Abstract: Variational quantum machine learning is an extensively studied application of near-term quantum computers. The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an ... |
Title: Delving into High-Quality Synthetic Face Occlusion Segmentation Datasets Abstract: This paper performs comprehensive analysis on datasets for occlusion-aware face segmentation, a task that is crucial for many downstream applications. The collection and annotation of such datasets are time-consuming and labor-int... |
Title: The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning Abstract: Recently the surprising discovery of the Bootstrap Your Own Latent (BYOL) method by Grill et al. shows the negative term in contrastive loss can be removed if we add the so-called prediction head to the network. This initiated... |
Title: SIBILA: High-performance computing and interpretable machine learning join efforts toward personalised medicine in a novel decision-making tool Abstract: Background and Objectives: Personalised medicine remains a major challenge for scientists. The rapid growth of Machine learning and Deep learning has made it a... |
Title: ELODI: Ensemble Logit Difference Inhibition for Positive-Congruent Training Abstract: Negative flips are errors introduced in a classification system when a legacy model is replaced with a new one. Existing methods to reduce the negative flip rate (NFR) either do so at the expense of overall accuracy using model... |
Title: Topologically-Aware Deformation Fields for Single-View 3D Reconstruction Abstract: We present a framework for learning 3D object shapes and dense cross-object 3D correspondences from just an unaligned category-specific image collection. The 3D shapes are generated implicitly as deformations to a category-specifi... |
Title: Adaptive Block Floating-Point for Analog Deep Learning Hardware Abstract: Analog mixed-signal (AMS) devices promise faster, more energy-efficient deep neural network (DNN) inference than their digital counterparts. However, recent studies show that DNNs on AMS devices with fixed-point numbers can incur an accura... |
Title: Integrating User and Item Reviews in Deep Cooperative Neural Networks for Movie Ranking Prediction Abstract: User evaluations include a significant quantity of information across online platforms. This information source has been neglected by the majority of existing recommendation systems, despite its potential... |
Title: Improving Sequential Query Recommendation with Immediate User Feedback Abstract: We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequen... |
Title: Using Natural Sentences for Understanding Biases in Language Models Abstract: Evaluation of biases in language models is often limited to synthetically generated datasets. This dependence traces back to the need for a prompt-style dataset to trigger specific behaviors of language models. In this paper, we addres... |
Title: Detailed Balanced Chemical Reaction Networks as Generalized Boltzmann Machines Abstract: Can a micron sized sack of interacting molecules understand, and adapt to a constantly-fluctuating environment? Cellular life provides an existence proof in the affirmative, but the principles that allow for life's existence... |
Title: Multi-Environment Meta-Learning in Stochastic Linear Bandits Abstract: In this work we investigate meta-learning (or learning-to-learn) approaches in multi-task linear stochastic bandit problems that can originate from multiple environments. Inspired by the work of [1] on meta-learning in a sequence of linear ba... |
Title: Collaborative Multi-agent Stochastic Linear Bandits Abstract: We study a collaborative multi-agent stochastic linear bandit setting, where $N$ agents that form a network communicate locally to minimize their overall regret. In this setting, each agent has its own linear bandit problem (its own reward parameter) ... |
Title: Visuomotor Control in Multi-Object Scenes Using Object-Aware Representations Abstract: Perceptual understanding of the scene and the relationship between its different components is important for successful completion of robotic tasks. Representation learning has been shown to be a powerful technique for this, b... |
Title: Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning Abstract: We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). GWI leverages the Wasser... |
Title: Interpretable Climate Change Modeling With Progressive Cascade Networks Abstract: Typical deep learning approaches to modeling high-dimensional data often result in complex models that do not easily reveal a new understanding of the data. Research in the deep learning field is very actively pursuing new methods ... |
Title: Warm-starting DARTS using meta-learning Abstract: Neural architecture search (NAS) has shown great promise in the field of automated machine learning (AutoML). NAS has outperformed hand-designed networks and made a significant step forward in the field of automating the design of deep neural networks, thus furth... |
Title: Deep Learning for Prawn Farming: Forecasting and Anomaly Detection Abstract: We present a decision support system for managing water quality in prawn ponds. The system uses various sources of data and deep learning models in a novel way to provide 24-hour forecasting and anomaly detection of water quality parame... |
Title: How to Combine Membership-Inference Attacks on Multiple Updated Models Abstract: A large body of research has shown that machine learning models are vulnerable to membership inference (MI) attacks that violate the privacy of the participants in the training data. Most MI research focuses on the case of a single ... |
Title: KASAM: Spline Additive Models for Function Approximation Abstract: Neural networks have been criticised for their inability to perform continual learning due to catastrophic forgetting and rapid unlearning of a past concept when a new concept is introduced. Catastrophic forgetting can be alleviated by specifical... |
Title: $\alpha$-GAN: Convergence and Estimation Guarantees Abstract: We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated $f$-divergences. We then focus on $\alpha$-GAN, defined via the $\alpha$-loss, which interpolates several GANs (Hel... |
Title: PoisonedEncoder: Poisoning the Unlabeled Pre-training Data in Contrastive Learning Abstract: Contrastive learning pre-trains an image encoder using a large amount of unlabeled data such that the image encoder can be used as a general-purpose feature extractor for various downstream tasks. In this work, we propos... |
Title: Fast Conditional Network Compression Using Bayesian HyperNetworks Abstract: We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context inv... |
Title: Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review Abstract: Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of... |
Title: Design and Implementation of a Quantum Kernel for Natural Language Processing Abstract: Natural language processing (NLP) is the field that attempts to make human language accessible to computers, and it relies on applying a mathematical model to express the meaning of symbolic language. One such model, DisCoCat... |
Title: Test-time Fourier Style Calibration for Domain Generalization Abstract: The topic of generalizing machine learning models learned on a collection of source domains to unknown target domains is challenging. While many domain generalization (DG) methods have achieved promising results, they primarily rely on the s... |
Title: Modularity in NEAT Reinforcement Learning Networks Abstract: Modularity is essential to many well-performing structured systems, as it is a useful means of managing complexity [8]. An analysis of modularity in neural networks produced by machine learning algorithms can offer valuable insight into the workings of... |
Title: l-Leaks: Membership Inference Attacks with Logits Abstract: Machine Learning (ML) has made unprecedented progress in the past several decades. However, due to the memorability of the training data, ML is susceptible to various attacks, especially Membership Inference Attacks (MIAs), the objective of which is to ... |
Title: Data-Driven Upper Bounds on Channel Capacity Abstract: We consider the problem of estimating an upper bound on the capacity of a memoryless channel with unknown channel law and continuous output alphabet. A novel data-driven algorithm is proposed that exploits the dual representation of capacity where the maximi... |
Title: OFedQIT: Communication-Efficient Online Federated Learning via Quantization and Intermittent Transmission Abstract: Online federated learning (OFL) is a promising framework to collaboratively learn a sequence of non-linear functions (or models) from distributed streaming data incoming to multiple clients while k... |
Title: A hybrid data driven-physics constrained Gaussian process regression framework with deep kernel for uncertainty quantification Abstract: Gaussian process regression (GPR) has been a well-known machine learning method for various applications such as uncertainty quantifications (UQ). However, GPR is inherently a ... |
Title: Deep Reinforcement Learning for Computational Fluid Dynamics on HPC Systems Abstract: Reinforcement learning (RL) is highly suitable for devising control strategies in the context of dynamical systems. A prominent instance of such a dynamical system is the system of equations governing fluid dynamics. Recent res... |
Title: DualCF: Efficient Model Extraction Attack from Counterfactual Explanations Abstract: Cloud service providers have launched Machine-Learning-as-a-Service (MLaaS) platforms to allow users to access large-scale cloudbased models via APIs. In addition to prediction outputs, these APIs can also provide other informat... |
Title: Collaborative Drug Discovery: Inference-level Data Protection Perspective Abstract: Pharmaceutical industry can better leverage its data assets to virtualize drug discovery through a collaborative machine learning platform. On the other hand, there are non-negligible risks stemming from the unintended leakage of... |
Title: Precise Change Point Detection using Spectral Drift Detection Abstract: The notion of concept drift refers to the phenomenon that the data generating distribution changes over time; as a consequence machine learning models may become inaccurate and need adjustment. In this paper we consider the problem of detect... |
Title: Toward A Formalized Approach for Spike Sorting Algorithms and Hardware Evaluation Abstract: Spike sorting algorithms are used to separate extracellular recordings of neuronal populations into single-unit spike activities. The development of customized hardware implementing spike sorting algorithms is burgeoning.... |
Title: Productivity Assessment of Neural Code Completion Abstract: Neural code synthesis has reached a point where snippet generation is accurate enough to be considered for integration into human software development workflows. Commercial products aim to increase programmers' productivity, without being able to measur... |
Title: Accelerometry-based classification of circulatory states during out-of-hospital cardiac arrest Abstract: Objective: During cardiac arrest treatment, a reliable detection of spontaneous circulation, usually performed by manual pulse checks, is both vital for patient survival and practically challenging. Methods: ... |
Title: Uninorm-like parametric activation functions for human-understandable neural models Abstract: We present a deep learning model for finding human-understandable connections between input features. Our approach uses a parameterized, differentiable activation function, based on the theoretical background of nilpote... |
Title: Kronecker Decomposition for Knowledge Graph Embeddings Abstract: Knowledge graph embedding research has mainly focused on learning continuous representations of entities and relations tailored towards the link prediction problem. Recent results indicate an ever increasing predictive ability of current approaches... |
Title: Convergence of Deep Neural Networks with General Activation Functions and Pooling Abstract: Deep neural networks, as a powerful system to represent high dimensional complex functions, play a key role in deep learning. Convergence of deep neural networks is a fundamental issue in building the mathematical foundat... |
Title: Convergence Analysis of Deep Residual Networks Abstract: Various powerful deep neural network architectures have made great contribution to the exciting successes of deep learning in the past two decades. Among them, deep Residual Networks (ResNets) are of particular importance because they demonstrated great us... |
Title: Detecting Rumours with Latency Guarantees using Massive Streaming Data Abstract: Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, wh... |
Title: Upside-Down Reinforcement Learning Can Diverge in Stochastic Environments With Episodic Resets Abstract: Upside-Down Reinforcement Learning (UDRL) is an approach for solving RL problems that does not require value functions and uses only supervised learning, where the targets for given inputs in a dataset do not... |
Title: Improving Contextual Representation with Gloss Regularized Pre-training Abstract: Though achieving impressive results on many NLP tasks, the BERT-like masked language models (MLM) encounter the discrepancy between pre-training and inference. In light of this gap, we investigate the contextual representation of p... |
Title: StyLandGAN: A StyleGAN based Landscape Image Synthesis using Depth-map Abstract: Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations. To address this problem, we propos... |
Title: The Devil is in the Details: On the Pitfalls of Vocabulary Selection in Neural Machine Translation Abstract: Vocabulary selection, or lexical shortlisting, is a well-known technique to improve latency of Neural Machine Translation models by constraining the set of allowed output words during inference. The chose... |
Title: FastSTMF: Efficient tropical matrix factorization algorithm for sparse data Abstract: Matrix factorization, one of the most popular methods in machine learning, has recently benefited from introducing non-linearity in prediction tasks using tropical semiring. The non-linearity enables a better fit to extreme val... |
Title: Analyzing Hate Speech Data along Racial, Gender and Intersectional Axes Abstract: To tackle the rising phenomenon of hate speech, efforts have been made towards data curation and analysis. When it comes to analysis of bias, previous work has focused predominantly on race. In our work, we further investigate bias... |
Title: The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials Abstract: The rapid progress of machine learning interatomic potentials over the past couple of years produced a number of new architectures. Particularly notable among these are the Atomic Cluster Expansion (ACE), which unified many of th... |
Title: NN-EUCLID: deep-learning hyperelasticity without stress data Abstract: We propose a new approach for unsupervised learning of hyperelastic constitutive laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the availability of stress-strain pairs, the approach only u... |
Title: Local Attention Graph-based Transformer for Multi-target Genetic Alteration Prediction Abstract: Classical multiple instance learning (MIL) methods are often based on the identical and independent distributed assumption between instances, hence neglecting the potentially rich contextual information beyond indivi... |
Title: Univariate and Multivariate LSTM Model for Short-Term Stock Market Prediction Abstract: Designing robust and accurate prediction models has been a viable research area since a long time. While proponents of a well-functioning market predictors believe that it is difficult to accurately predict market prices but ... |
Title: Research on the correlation between text emotion mining and stock market based on deep learning Abstract: This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the fin... |
Title: Heavy-Tail Phenomenon in Decentralized SGD Abstract: Recent theoretical studies have shown that heavy-tails can emerge in stochastic optimization due to `multiplicative noise', even under surprisingly simple settings, such as linear regression with Gaussian data. While these studies have uncovered several intere... |
Title: DRBM-ClustNet: A Deep Restricted Boltzmann-Kohonen Architecture for Data Clustering Abstract: A Bayesian Deep Restricted Boltzmann-Kohonen architecture for data clustering termed as DRBM-ClustNet is proposed. This core-clustering engine consists of a Deep Restricted Boltzmann Machine (DRBM) for processing unlabe... |
Title: Learning Keypoints from Synthetic Data for Robotic Cloth Folding Abstract: Robotic cloth manipulation is challenging due to its deformability, which makes determining its full state infeasible. However, for cloth folding, it suffices to know the position of a few semantic keypoints. Convolutional neural networks... |
Title: A Vision Inspired Neural Network for Unsupervised Anomaly Detection in Unordered Data Abstract: A fundamental problem in the field of unsupervised machine learning is the detection of anomalies corresponding to rare and unusual observations of interest; reasons include for their rejection, accommodation or furth... |
Title: On the Importance of Architecture and Feature Selection in Differentially Private Machine Learning Abstract: We study a pitfall in the typical workflow for differentially private machine learning. The use of differentially private learning algorithms in a "drop-in" fashion -- without accounting for the impact of... |
Title: Federated Learning Under Intermittent Client Availability and Time-Varying Communication Constraints Abstract: Federated learning systems facilitate training of global models in settings where potentially heterogeneous data is distributed across a large number of clients. Such systems operate in settings with in... |
Title: Neurochaos Feature Transformation and Classification for Imbalanced Learning Abstract: Learning from limited and imbalanced data is a challenging problem in the Artificial Intelligence community. Real-time scenarios demand decision-making from rare events wherein the data are typically imbalanced. These situatio... |
Title: A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities Abstract: Few-shot learning (FSL) has emerged as an effective learning method and shows great potential. Despite the recent creative works in tackling FSL tasks, learning valid information rapidly from just a few ... |
Title: Provably Safe Reinforcement Learning: A Theoretical and Experimental Comparison Abstract: Ensuring safety of reinforcement learning (RL) algorithms is crucial for many real-world tasks. However, vanilla RL does not guarantee safety for an agent. In recent years, several methods have been proposed to provide safe... |
Title: Interlock-Free Multi-Aspect Rationalization for Text Classification Abstract: Explanation is important for text classification tasks. One prevalent type of explanation is rationales, which are text snippets of input text that suffice to yield the prediction and are meaningful to humans. A lot of research on rati... |
Title: Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks Abstract: Due to the latest advances in technology, telescopes with significant sky coverage will produce millions of astronomical alerts per night that must be classified both rapidly and automatically. ... |
Title: Emergent Bartering Behaviour in Multi-Agent Reinforcement Learning Abstract: Advances in artificial intelligence often stem from the development of new environments that abstract real-world situations into a form where research can be done conveniently. This paper contributes such an environment based on ideas i... |
Title: Exploring the structure-property relations of thin-walled, 2D extruded lattices using neural networks Abstract: This paper investigates the structure-property relations of thin-walled lattices under dynamic longitudinal compression, characterized by their cross-sections and heights. These relations elucidate the... |
Title: EyeDAS: Securing Perception of Autonomous Cars Against the Stereoblindness Syndrome Abstract: The ability to detect whether an object is a 2D or 3D object is extremely important in autonomous driving, since a detection error can have life-threatening consequences, endangering the safety of the driver, passengers... |
Title: Artificial Intelligence-Assisted Optimization and Multiphase Analysis of Polygon PEM Fuel Cells Abstract: This article presents new PEM fuel cell models with hexagonal and pentagonal designs. After observing cell performance improvement in these models, we optimized them. Inlet pressure and temperature were used... |
Title: Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs Abstract: Dual embodied-symbolic concept representations are the foundation for deep learning and symbolic AI integration. We discuss the use of dual embodied-symbolic concept representations for molecular graph representation lear... |
Title: Multiple Domain Causal Networks Abstract: Observational studies are regarded as economic alternatives to randomized trials, often used in their stead to investigate and determine treatment efficacy. Due to lack of sample size, observational studies commonly combine data from multiple sources or different sites/c... |
Title: Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime Abstract: The generalization performance of kernel ridge regression (KRR) exhibits a multi-phased pattern that crucially depends on the scaling relationship between the sample size $n$ and the underlying dimension $d$. This phenomenon is due t... |
Title: The ACM Multimedia 2022 Computational Paralinguistics Challenge: Vocalisations, Stuttering, Activity, & Mosquitoes Abstract: The ACM Multimedia 2022 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Vocalis... |
Title: Distributed Transmission Control for Wireless Networks using Multi-Agent Reinforcement Learning Abstract: We examine the problem of transmission control, i.e., when to transmit, in distributed wireless communications networks through the lens of multi-agent reinforcement learning. Most other works using reinforc... |
Title: Goal-Guided Neural Cellular Automata: Learning to Control Self-Organising Systems Abstract: Inspired by cellular growth and self-organization, Neural Cellular Automata (NCAs) have been capable of "growing" artificial cells into images, 3D structures, and even functional machines. NCAs are flexible and robust com... |
Title: Transformation-Interaction-Rational Representation for Symbolic Regression Abstract: Symbolic Regression searches for a function form that approximates a dataset often using Genetic Programming. Since there is usually no restriction to what form the function can have, Genetic Programming may return a hard to und... |
Title: Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions Abstract: We study the linear contextual bandit problem in the presence of adversarial corruption, where the reward at each round is corrupted by an adversary, and the corruption level (i.e., the sum of corruption magnitudes ove... |
Title: Principal-Agent Hypothesis Testing Abstract: Consider the relationship between the FDA (the principal) and a pharmaceutical company (the agent). The pharmaceutical company wishes to sell a product to make a profit, and the FDA wishes to ensure that only efficacious drugs are released to the public. The efficacy ... |
Title: Multi-variant COVID-19 model with heterogeneous transmission rates using deep neural networks Abstract: Mutating variants of COVID-19 have been reported across many US states since 2021. In the fight against COVID-19, it has become imperative to study the heterogeneity in the time-varying transmission rates for ... |
Title: Optimal Parameter-free Online Learning with Switching Cost Abstract: Parameter-freeness in online learning refers to the adaptivity of an algorithm with respect to the optimal decision in hindsight. In this paper, we design such algorithms in the presence of switching cost - the latter penalizes the optimistic u... |
Title: Physics guided neural networks for modelling of non-linear dynamics Abstract: The success of the current wave of artificial intelligence can be partly attributed to deep neural networks, which have proven to be very effective in learning complex patterns from large datasets with minimal human intervention. Howev... |
Title: From Images to Probabilistic Anatomical Shapes: A Deep Variational Bottleneck Approach Abstract: Statistical shape modeling (SSM) directly from 3D medical images is an underutilized tool for detecting pathology, diagnosing disease, and conducting population-level morphology analysis. Deep learning frameworks hav... |
Title: Sentiment Analysis of Covid-related Reddits Abstract: This paper focuses on Sentiment Analysis of Covid-19 related messages from the r/Canada and r/Unitedkingdom subreddits of Reddit. We apply manual annotation and three Machine Learning algorithms to analyze sentiments conveyed in those messages. We use VADER a... |
Title: A Huber loss-based super learner with applications to healthcare expenditures Abstract: Complex distributions of the healthcare expenditure pose challenges to statistical modeling via a single model. Super learning, an ensemble method that combines a range of candidate models, is a promising alternative for cost... |
Title: AVCAffe: A Large Scale Audio-Visual Dataset of Cognitive Load and Affect for Remote Work Abstract: We introduce AVCAffe, the first Audio-Visual dataset consisting of Cognitive load and Affect attributes. We record AVCAffe by simulating remote work scenarios over a video-conferencing platform, where subjects coll... |
Title: Large-Scale Sequential Learning for Recommender and Engineering Systems Abstract: In this thesis, we focus on the design of an automatic algorithms that provide personalized ranking by adapting to the current conditions. To demonstrate the empirical efficiency of the proposed approaches we investigate their appl... |
Title: Robustness of Control Design via Bayesian Learning Abstract: In the realm of supervised learning, Bayesian learning has shown robust predictive capabilities under input and parameter perturbations. Inspired by these findings, we demonstrate the robustness properties of Bayesian learning in the control search tas... |
Title: Differentiable programming: Generalization, characterization and limitations of deep learning Abstract: In the past years, deep learning models have been successfully applied in several cognitive tasks. Originally inspired by neuroscience, these models are specific examples of differentiable programs. In this pa... |
Title: Universal Post-Training Backdoor Detection Abstract: A Backdoor attack (BA) is an important type of adversarial attack against deep neural network classifiers, wherein test samples from one or more source classes will be (mis)classified to the attacker's target class when a backdoor pattern (BP) is embedded. In ... |
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