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Title: Image Super-Resolution With Deep Variational Autoencoders Abstract: Image super-resolution (SR) techniques are used to generate a high-resolution image from a low-resolution image. Until now, deep generative models such as autoregressive models and Generative Adversarial Networks (GANs) have proven to be effecti... |
Title: Continual Learning Based on OOD Detection and Task Masking Abstract: Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both. CIL and TIL differ mainly in that the task-id is provided for each test sample during testing for ... |
Title: MolNet: A Chemically Intuitive Graph Neural Network for Prediction of Molecular Properties Abstract: The graph neural network (GNN) has been a powerful deep-learning tool in chemistry domain, due to its close connection with molecular graphs. Most GNN models collect and update atom and molecule features from the... |
Title: Self-Normalized Density Map (SNDM) for Counting Microbiological Objects Abstract: The statistical properties of the density map (DM) approach to counting microbiological objects on images are studied in detail. The DM is given by U$^2$-Net. Two statistical methods for deep neural networks are utilized: the boots... |
Title: Uncertainty with UAV Search of Multiple Goal-oriented Targets Abstract: This paper considers the complex problem of a team of UAVs searching targets under uncertainty. The goal of the UAV team is to find all of the moving targets as quickly as possible before they arrive at their selected goal. The uncertainty c... |
Title: A Decomposition-Based Hybrid Ensemble CNN Framework for Improving Cross-Subject EEG Decoding Performance Abstract: Electroencephalogram (EEG) signals are complex, non-linear, and non-stationary in nature. However, previous studies that applied decomposition to minimize the complexity mainly exploited the hand-en... |
Title: Diffusion Probabilistic Modeling for Video Generation Abstract: Denoising diffusion probabilistic models are a promising new class of generative models that mark a milestone in high-quality image generation. This paper showcases their ability to sequentially generate video, surpassing prior methods in perceptual... |
Title: Defending Against Adversarial Attack in ECG Classification with Adversarial Distillation Training Abstract: In clinics, doctors rely on electrocardiograms (ECGs) to assess severe cardiac disorders. Owing to the development of technology and the increase in health awareness, ECG signals are currently obtained by ... |
Title: Transframer: Arbitrary Frame Prediction with Generative Models Abstract: We present a general-purpose framework for image modelling and vision tasks based on probabilistic frame prediction. Our approach unifies a broad range of tasks, from image segmentation, to novel view synthesis and video interpolation. We p... |
Title: The Frost Hollow Experiments: Pavlovian Signalling as a Path to Coordination and Communication Between Agents Abstract: Learned communication between agents is a powerful tool when approaching decision-making problems that are hard to overcome by any single agent in isolation. However, continual coordination and... |
Title: DetMatch: Two Teachers are Better Than One for Joint 2D and 3D Semi-Supervised Object Detection Abstract: While numerous 3D detection works leverage the complementary relationship between RGB images and point clouds, developments in the broader framework of semi-supervised object recognition remain uninfluenced ... |
Title: On Multi-Domain Long-Tailed Recognition, Generalization and Beyond Abstract: Real-world data often exhibit imbalanced label distributions. Existing studies on data imbalance focus on single-domain settings, i.e., samples are from the same data distribution. However, natural data can originate from distinct domai... |
Title: AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation Abstract: Powerful priors allow us to perform inference with insufficient information. In this paper, we propose an autoregressive prior for 3D shapes to solve multimodal 3D tasks such as shape completion, reconstruction, and generation. We m... |
Title: Efficient Federated Learning on Knowledge Graphs via Privacy-preserving Relation Embedding Aggregation Abstract: Federated Learning (FL) on knowledge graphs (KGs) has yet to be as well studied as other domains, such as computer vision and natural language processing. A recent study FedE first proposes an FL fram... |
Title: On the expressive power of message-passing neural networks as global feature map transformers Abstract: We investigate the power of message-passing neural networks (MPNNs) in their capacity to transform the numerical features stored in the nodes of their input graphs. Our focus is on global expressive power, uni... |
Title: Outcome Assumptions and Duality Theory for Balancing Weights Abstract: We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population. These estimators minimize the worst-case error by making an assumption about the outcome model. In thi... |
Title: Leveraging Adversarial Examples to Quantify Membership Information Leakage Abstract: The use of personal data for training machine learning systems comes with a privacy threat and measuring the level of privacy of a model is one of the major challenges in machine learning today. Identifying training data based o... |
Title: Triangle and Four Cycle Counting with Predictions in Graph Streams Abstract: We propose data-driven one-pass streaming algorithms for estimating the number of triangles and four cycles, two fundamental problems in graph analytics that are widely studied in the graph data stream literature. Recently, (Hsu 2018) a... |
Title: SepTr: Separable Transformer for Audio Spectrogram Processing Abstract: Following the successful application of vision transformers in multiple computer vision tasks, these models have drawn the attention of the signal processing community. This is because signals are often represented as spectrograms (e.g. thro... |
Title: Enhanced Temporal Knowledge Embeddings with Contextualized Language Representations Abstract: Within the emerging research efforts to combine structured and unstructured knowledge, many approaches incorporate factual knowledge, e.g., available in form of structured knowledge graphs (KGs), into pre-trained langua... |
Title: Delta Distillation for Efficient Video Processing Abstract: This paper aims to accelerate video stream processing, such as object detection and semantic segmentation, by leveraging the temporal redundancies that exist between video frames. Instead of propagating and warping features using motion alignment, such ... |
Title: DP-KB: Data Programming with Knowledge Bases Improves Transformer Fine Tuning for Answer Sentence Selection Abstract: While transformers demonstrate impressive performance on many knowledge intensive (KI) tasks, their ability to serve as implicit knowledge bases (KBs) remains limited, as shown on several slot-fi... |
Title: Learning Distributionally Robust Models at Scale via Composite Optimization Abstract: To train machine learning models that are robust to distribution shifts in the data, distributionally robust optimization (DRO) has been proven very effective. However, the existing approaches to learning a distributionally rob... |
Title: STICC: A multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity Abstract: Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity an... |
Title: DeepLSS: breaking parameter degeneracies in large scale structure with deep learning analysis of combined probes Abstract: In classical cosmological analysis of large scale structure surveys with 2-pt functions, the parameter measurement precision is limited by several key degeneracies within the cosmology and a... |
Title: The Analysis of Online Event Streams: Predicting the Next Activity for Anomaly Detection Abstract: Anomaly detection in process mining focuses on identifying anomalous cases or events in process executions. The resulting diagnostics are used to provide measures to prevent fraudulent behavior, as well as to deriv... |
Title: On the Importance of Data Size in Probing Fine-tuned Models Abstract: Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. However, these studies often neglect the role of the size of the dataset on which the model is fine-tuned. In this pape... |
Title: Monotonic Differentiable Sorting Networks Abstract: Differentiable sorting algorithms allow training with sorting and ranking supervision, where only the ordering or ranking of samples is known. Various methods have been proposed to address this challenge, ranging from optimal transport-based differentiable Sink... |
Title: A Learning Framework for Bandwidth-Efficient Distributed Inference in Wireless IoT Abstract: In wireless Internet of things (IoT), the sensors usually have limited bandwidth and power resources. Therefore, in a distributed setup, each sensor should compress and quantize the sensed observations before transmittin... |
Title: Inventing Relational State and Action Abstractions for Effective and Efficient Bilevel Planning Abstract: Effective and efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known. One way to alleviate this challenge is to perform bile... |
Title: Design of Compressed Sensing Systems via Density-Evolution Framework for Structure Recovery in Graphical Models Abstract: It has been shown that the task of learning the structure of Bayesian networks (BN) from observational data is an NP-Hard problem. Although there have been attempts made to tackle this proble... |
Title: Investigating Compounding Prediction Errors in Learned Dynamics Models Abstract: Accurately predicting the consequences of agents' actions is a key prerequisite for planning in robotic control. Model-based reinforcement learning (MBRL) is one paradigm which relies on the iterative learning and prediction of stat... |
Title: Unified Line and Paragraph Detection by Graph Convolutional Networks Abstract: We formulate the task of detecting lines and paragraphs in a document into a unified two-level clustering problem. Given a set of text detection boxes that roughly correspond to words, a text line is a cluster of boxes and a paragraph... |
Title: Generating unrepresented proportions of geological facies using Generative Adversarial Networks Abstract: In this work, we investigate the capacity of Generative Adversarial Networks (GANs) in interpolating and extrapolating facies proportions in a geological dataset. The new generated realizations with unrepres... |
Title: Low-degree learning and the metric entropy of polynomials Abstract: Let $\mathscr{F}_{n,d}$ be the class of all functions $f:\{-1,1\}^n\to[-1,1]$ on the $n$-dimensional discrete hypercube of degree at most $d$. In the first part of this paper, we prove that any (deterministic or randomized) algorithm which learn... |
Title: Meta Reinforcement Learning for Adaptive Control: An Offline Approach Abstract: Meta-learning is a branch of machine learning which trains neural network models to synthesize a wide variety of data in order to rapidly solve new problems. In process control, many systems have similar and well-understood dynamics,... |
Title: An Improved Subject-Independent Stress Detection Model Applied to Consumer-grade Wearable Devices Abstract: Stress is a complex issue with wide-ranging physical and psychological impacts on human daily performance. Specifically, acute stress detection is becoming a valuable application in contextual human unders... |
Title: Emerging Artificial Intelligence Applications in Spatial Transcriptomics Analysis Abstract: Spatial transcriptomics (ST) has advanced significantly in the last few years. Such advancement comes with the urgent need for novel computational methods to handle the unique challenges of ST data analysis. Many artifici... |
Title: Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices Abstract: Identifying stress levels can provide valuable data for mental health analytics as well as labels for annotation systems. Although much research has been conducted into stress detection models using heart rate varia... |
Title: Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computing Abstract: In this paper, we study a new latency optimization problem for Blockchain-based federated learning (BFL) in multi-server edge computing. In this system model, distributed mobile devices (MDs) communicate wit... |
Title: Multi-Modal Causal Inference with Deep Structural Equation Models Abstract: Accounting for the effects of confounders is one of the central challenges in causal inference. Unstructured multi-modal data (images, time series, text) contains valuable information about diverse types of confounders, yet it is typical... |
Title: Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement Abstract: Bayesian coresets approximate a posterior distribution by building a small weighted subset of the data points. Any inference procedure that is too computationally expensive to be run on the full posterior can instead be run inexpensivel... |
Title: Self-Ensemble Adversarial Training for Improved Robustness Abstract: Due to numerous breakthroughs in real-world applications brought by machine intelligence, deep neural networks (DNNs) are widely employed in critical applications. However, predictions of DNNs are easily manipulated with imperceptible adversari... |
Title: LeHDC: Learning-Based Hyperdimensional Computing Classifier Abstract: Thanks to the tiny storage and efficient execution, hyperdimensional Computing (HDC) is emerging as a lightweight learning framework on resource-constrained hardware. Nonetheless, the existing HDC training relies on various heuristic methods, ... |
Title: Generative Principal Component Analysis Abstract: In this paper, we study the problem of principal component analysis with generative modeling assumptions, adopting a general model for the observed matrix that encompasses notable special cases, including spiked matrix recovery and phase retrieval. The key assump... |
Title: Towards Training Billion Parameter Graph Neural Networks for Atomic Simulations Abstract: Recent progress in Graph Neural Networks (GNNs) for modeling atomic simulations has the potential to revolutionize catalyst discovery, which is a key step in making progress towards the energy breakthroughs needed to combat... |
Title: Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey Abstract: Recent advances in electronic devices and communication infrastructure have revolutionized the traditional healthcare system into a smart healthcare system by using IoMT devices. However, due to the centrali... |
Title: Learning Stabilizable Deep Dynamics Models Abstract: When neural networks are used to model dynamics, properties such as stability of the dynamics are generally not guaranteed. In contrast, there is a recent method for learning the dynamics of autonomous systems that guarantees global exponential stability using... |
Title: Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional Interference Cancellation Network Abstract: Wireless links are increasingly used to deliver critical services, while intentional interference (jamming) remains a very serious threat to such services. In this paper, we are concerned with the ... |
Title: Deterministic Bridge Regression for Compressive Classification Abstract: Pattern classification with compact representation is an important component in machine intelligence. In this work, an analytic bridge solution is proposed for compressive classification. The proposal has been based upon solving a penalized... |
Title: DEFORM: A Practical, Universal Deep Beamforming System Abstract: We introduce, design, and evaluate a set of universal receiver beamforming techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX beamforming achieves significant gain for multi antenna RF receivers while being agnostic to the tr... |
Title: PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning Abstract: Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We s... |
Title: Do Deep Networks Transfer Invariances Across Classes? Abstract: To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformations that apply similarly to all classes, such as lighting and backgro... |
Title: Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in Delay-embedded Space Abstract: Here, we propose a new reconstruction method of smooth time-series signals. A key concept of this study is not considering the model in signal space, but in delay-embedded space. In other words, we indirectly repre... |
Title: Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization Abstract: Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, participants have heterogeneous resources due to d... |
Title: Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation Abstract: Level set estimation (LSE) is the problem of identifying regions where an unknown function takes values above or below a specified threshold. Active sampling strategies for efficient LSE have primarily been studied in continuous-valued... |
Title: Distributed Sketching for Randomized Optimization: Exact Characterization, Concentration and Lower Bounds Abstract: We consider distributed optimization methods for problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We leverage randomized sketches for... |
Title: AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack Abstract: Deep neural networks (DNNs) have been proven to be vulnerable to adversarial examples. A special branch of adversarial examples, namely sparse adversarial examples, can fool the target DNNs by perturbing only a few pixels. However, man... |
Title: Speaker Embedding-aware Neural Diarization: an Efficient Framework for Overlapping Speech Diarization in Meeting Scenarios Abstract: Overlapping speech diarization has been traditionally treated as a multi-label classification problem. In this paper, we reformulate this task as a single-label prediction problem ... |
Title: Prototypical Verbalizer for Prompt-based Few-shot Tuning Abstract: Prompt-based tuning for pre-trained language models (PLMs) has shown its effectiveness in few-shot learning. Typically, prompt-based tuning wraps the input text into a cloze question. To make predictions, the model maps the output words to labels... |
Title: Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images Abstract: GAN-generated deepfakes as a genre of digital images are gaining ground as both catalysts of artistic expression and malicious forms of deception, therefore demanding systems to enforce and accredit their ethical ... |
Title: ISDE : Independence Structure Density Estimation Abstract: In this paper, we propose ISDE (Independence Structure Density Estimation), an algorithm designed to estimate a multivariate density under Kullback-Leibler loss and the Independence Structure (IS) model. IS tackles the curse of dimensionality by separati... |
Title: Constitutive model characterization and discovery using physics-informed deep learning Abstract: Classically, the mechanical response of materials is described through constitutive models, often in the form of constrained ordinary differential equations. These models have a very limited number of parameters, yet... |
Title: AdIoTack: Quantifying and Refining Resilience of Decision Tree Ensemble Inference Models against Adversarial Volumetric Attacks on IoT Networks Abstract: Machine Learning-based techniques have shown success in cyber intelligence. However, they are increasingly becoming targets of sophisticated data-driven advers... |
Title: Proximal Policy Optimization with Adaptive Threshold for Symmetric Relative Density Ratio Abstract: Deep reinforcement learning (DRL) is one of the promising approaches for introducing robots into complicated environments. The recent remarkable progress of DRL stands on regularization of policy, which allows the... |
Title: Dencentralized learning in the presence of low-rank noise Abstract: Observations collected by agents in a network may be unreliable due to observation noise or interference. This paper proposes a distributed algorithm that allows each node to improve the reliability of its own observation by relying solely on lo... |
Title: Are You Robert or RoBERTa? Deceiving Online Authorship Attribution Models Using Neural Text Generators Abstract: Recently, there has been a rise in the development of powerful pre-trained natural language models, including GPT-2, Grover, and XLM. These models have shown state-of-the-art capabilities towards a va... |
Title: Cross-Modal Perceptionist: Can Face Geometry be Gleaned from Voices? Abstract: This work digs into a root question in human perception: can face geometry be gleaned from one's voices? Previous works that study this question only adopt developments in image synthesis and convert voices into face images to show co... |
Title: Towards Representative Subset Selection for Self-Supervised Speech Recognition Abstract: Self-supervised speech recognition models require considerable labeled training data for learning high-fidelity representations for Automatic Speech Recognition (ASR), which hinders their application to low-resource language... |
Title: Gender classification by means of online uppercase handwriting: A text-dependent allographic approach Abstract: This paper presents a gender classification schema based on online handwriting. Using samples acquired with a digital tablet that captures the dynamics of the writing, it classifies the writer as a mal... |
Title: Neural Predictor for Black-Box Adversarial Attacks on Speech Recognition Abstract: Recent works have revealed the vulnerability of automatic speech recognition (ASR) models to adversarial examples (AEs), i.e., small perturbations that cause an error in the transcription of the audio signal. Studying audio advers... |
Title: Decision-Making under Miscalibration Abstract: ML-based predictions are used to inform consequential decisions about individuals. How should we use predictions (e.g., risk of heart attack) to inform downstream binary classification decisions (e.g., undergoing a medical procedure)? When the risk estimates are per... |
Title: Finite-sample analysis of identification of switched linear systems with arbitrary or restricted switching Abstract: This work aims to derive a data-independent finite-sample error bound for the least-squares (LS) estimation error of switched linear systems when the state and the switching signal are measured. W... |
Title: Class-wise Classifier Design Capable of Continual Learning using Adaptive Resonance Theory-based Topological Clustering Abstract: This paper proposes a supervised classification algorithm capable of continual learning by utilizing an Adaptive Resonance Theory (ART)-based growing self-organizing clustering algori... |
Title: Identification of Hypokinetic Dysarthria Using Acoustic Analysis of Poem Recitation Abstract: Up to 90 % of patients with Parkinson's disease (PD) suffer from hypokinetic dysarthria (HD). In this work, we analysed the power of conventional speech features quantifying imprecise articulation, dysprosody, speech dy... |
Title: Hypergraph Modeling via Spectral Embedding Connection: Hypergraph Cut, Weighted Kernel $k$-means, and Heat Kernel Abstract: We propose a theoretical framework of multi-way similarity to model real-valued data into hypergraphs for clustering via spectral embedding. For graph cut based spectral clustering, it is c... |
Title: A Lightweight Instrument-Agnostic Model for Polyphonic Note Transcription and Multipitch Estimation Abstract: Automatic Music Transcription (AMT) has been recognized as a key enabling technology with a wide range of applications. Given the task's complexity, best results have typically been reported for systems ... |
Title: Learning to Reduce False Positives in Analytic Bug Detectors Abstract: Due to increasingly complex software design and rapid iterative development, code defects and security vulnerabilities are prevalent in modern software. In response, programmers rely on static analysis tools to regularly scan their codebases ... |
Title: Why we need biased AI -- How including cognitive and ethical machine biases can enhance AI systems Abstract: This paper stresses the importance of biases in the field of artificial intelligence (AI) in two regards. First, in order to foster efficient algorithmic decision-making in complex, unstable, and uncertai... |
Title: Convolutional Simultaneous Sparse Approximation with Applications to RGB-NIR Image Fusion Abstract: Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications i... |
Title: Comparing SONN Types for Efficient Robot Motion Planning in the Configuration Space Abstract: Motion planning in the configuration space (C-space) induces benefits, such as smooth trajectories. It becomes more complex as the degrees of freedom (DOF) increase. This is due to the direct relation between the dimens... |
Title: Revealing Reliable Signatures by Learning Top-Rank Pairs Abstract: Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields. In specific scenarios like confirming financial documents and legal instr... |
Title: Deepfake Style Transfer Mixture: a First Forensic Ballistics Study on Synthetic Images Abstract: Most recent style-transfer techniques based on generative architectures are able to obtain synthetic multimedia contents, or commonly called deepfakes, with almost no artifacts. Researchers already demonstrated that ... |
Title: On the sensitivity of pose estimation neural networks: rotation parameterizations, Lipschitz constants, and provable bounds Abstract: In this paper, we approach the task of determining sensitivity bounds for pose estimation neural networks. This task is particularly challenging as it requires characterizing the ... |
Title: A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection Abstract: In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dynamic feature sets and compare the resulting detec... |
Title: Defending Variational Autoencoders from Adversarial Attacks with MCMC Abstract: Variational autoencoders (VAEs) are deep generative models used in various domains. VAEs can generate complex objects and provide meaningful latent representations, which can be further used in downstream tasks such as classification... |
Title: Training a Tokenizer for Free with Private Federated Learning Abstract: Federated learning with differential privacy, i.e. private federated learning (PFL), makes it possible to train models on private data distributed across users' devices without harming privacy. PFL is efficient for models, such as neural net... |
Title: Neural Enhanced Belief Propagation for Data Association in Multiobject Tracking Abstract: Situation-aware technologies enabled by multiobject tracking (MOT) methods will create new services and applications in fields such as autonomous navigation and applied ocean sciences. Belief propagation (BP) is a state-of-... |
Title: Learning to Optimize Resource Assignment for Task Offloading in Mobile Edge Computing Abstract: In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading. Although the conventional branch and ... |
Title: SS-SAM : Stochastic Scheduled Sharpness-Aware Minimization for Efficiently Training Deep Neural Networks Abstract: By driving optimizers to converge to flat minima, sharpness-aware minimization (SAM) has shown the power to improve the model generalization. However, SAM requires to perform two forward-backward pr... |
Title: Towards Lithuanian grammatical error correction Abstract: Everyone wants to write beautiful and correct text, yet the lack of language skills, experience, or hasty typing can result in errors. By employing the recent advances in transformer architectures, we construct a grammatical error correction model for Lit... |
Title: BIOS: An Algorithmically Generated Biomedical Knowledge Graph Abstract: Biomedical knowledge graphs (BioMedKGs) are essential infrastructures for biomedical and healthcare big data and artificial intelligence (AI), facilitating natural language processing, model development, and data exchange. For decades, these... |
Title: WOODS: Benchmarks for Out-of-Distribution Generalization in Time Series Tasks Abstract: Machine learning models often fail to generalize well under distributional shifts. Understanding and overcoming these failures have led to a research field of Out-of-Distribution (OOD) generalization. Despite being extensivel... |
Title: Image Storage on Synthetic DNA Using Autoencoders Abstract: Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (rarely accessed data), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molec... |
Title: Diffusion and Volume Maximization-Based Clustering of Highly Mixed Hyperspectral Images Abstract: Hyperspectral images of a scene or object are a rich data source, often encoding a hundred or more spectral bands of reflectance at each pixel. Despite being very high-dimensional, these images typically encode late... |
Title: Graph-Text Multi-Modal Pre-training for Medical Representation Learning Abstract: As the volume of Electronic Health Records (EHR) sharply grows, there has been emerging interest in learning the representation of EHR for healthcare applications. Representation learning of EHR requires appropriate modeling of the... |
Title: FORCE: A Framework of Rule-Based Conversational Recommender System Abstract: The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which are largely limited by the requirement of large-scale huma... |
Title: Application of Top-hat Transformation for Enhanced Blood Vessel Extraction Abstract: In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases. Accurate extraction of blood vessels from ... |
Title: Ultra-low Latency Spiking Neural Networks with Spatio-Temporal Compression and Synaptic Convolutional Block Abstract: Spiking neural networks (SNNs), as one of the brain-inspired models, has spatio-temporal information processing capability, low power feature, and high biological plausibility. The effective spat... |
Title: Analyzing EEG Data with Machine and Deep Learning: A Benchmark Abstract: Nowadays, machine and deep learning techniques are widely used in different areas, ranging from economics to biology. In general, these techniques can be used in two ways: trying to adapt well-known models and architectures to the available... |
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