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Arxiv
4d
336
Image Credit: Arxiv
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning
- ProtoECGNet is a prototype-based deep learning model for interpretable, multi-label ECG classification.
- It employs a structured, multi-branch architecture integrating different CNNs with global and time-localized prototypes for rhythm classification, morphology-based reasoning, and diffuse abnormalities.
- The model is trained using a prototype loss designed for multi-label learning, combining clustering, separation, diversity, and contrastive loss.
- ProtoECGNet demonstrates competitive performance compared to state-of-the-art models and provides faithful, case-based explanations, making it a practical solution for transparent and trustworthy deep learning models in clinical decision support.
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Arxiv
4d
49
Image Credit: Arxiv
Surrogate-based optimization of system architectures subject to hidden constraints
- Exploration of novel architectures using physics-based simulation presents challenges for optimization algorithms.
- Surrogate-Based Optimization (SBO) algorithms, specifically Bayesian Optimization (BO) using Gaussian Process (GP) models, address the challenges.
- Strategies are investigated for satisfying hidden constraints in BO algorithms, including rejection of failed points, replacing failed points, and predicting the failure region.
- A mixed-discrete GP is found to achieve the best performance in predicting the Probability of Viability (PoV), demonstrated in solving a jet engine architecture problem.
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Arxiv
4d
12
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EquiNO: A Physics-Informed Neural Operator for Multiscale Simulations
- Multiscale problems in physics often require computationally expensive high-resolution simulations.
- Data-driven surrogate models have been used as a faster alternative, but struggle with incorporating microscale physical constraints.
- Equilibrium Neural Operator (EquiNO) is proposed as a complementary physics-informed surrogate that can predict microscale physics.
- The EquiNO framework integrates the finite element method with operator learning and achieves significant speedup compared to traditional methods.
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Arxiv
4d
143
Image Credit: Arxiv
mixEEG: Enhancing EEG Federated Learning for Cross-subject EEG Classification with Tailored mixup
- The cross-subject electroencephalography (EEG) classification presents challenges due to the diversity between subjects.
- To address data sharing limitations, researchers propose mixEEG, a framework for cross-subject EEG classification in federated learning.
- mixEEG tailors the mixup technique to better preserve privacy and offers an averaged label as pseudo-label.
- Experiments show that mixEEG enhances the transferability of the global model for cross-subject EEG classification across different datasets and model architectures.
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Arxiv
4d
239
Image Credit: Arxiv
Comparative analysis of Realistic EMF Exposure Estimation from Low Density Sensor Network by Finite & Infinite Neural Networks
- Understanding the spatial and temporal patterns of environmental exposure to radio-frequency electromagnetic fields is crucial for risk assessments.
- A comparative analysis of finite and infinite-width convolutional network-based methods for estimating and assessing RF-EMF exposure levels was conducted.
- Real-world datasets from 70 sensors in Lille, France, were used for the analysis.
- The evaluation criterion, Root Mean Square Error (RMSE), was used to compare the performance of the deep learning models.
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Arxiv
4d
251
Image Credit: Arxiv
Towards Simple Machine Learning Baselines for GNSS RFI Detection
- Machine learning research in GNSS radio frequency interference (RFI) detection often lacks justification for decisions made in deep learning-based model architectures.
- This paper challenges the status quo in machine learning approaches for GNSS RFI detection and advocates for a shift in focus to simpler and more interpretable machine learning baselines.
- The findings suggest the need for the development of simple and interpretable machine learning methods and demonstrate the effectiveness of a simple baseline for GNSS RFI detection.
- The results show that the simple baseline outperforms complex deep learning architectures with 91% accuracy in detecting potential GNSS RFI.
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Arxiv
4d
263
Image Credit: Arxiv
Fusing Global and Local: Transformer-CNN Synergy for Next-Gen Current Estimation
- This paper presents a hybrid model combining Transformer and CNN to predict current waveforms in signal lines.
- The model does not rely on fixed simplified models and replaces the complex process used in traditional SPICE simulations.
- The hybrid architecture combines the global feature-capturing ability of Transformers with the local feature extraction advantages of CNNs.
- Experimental results demonstrate that the proposed algorithm achieves an error of only 0.0098, improving the accuracy of current waveform predictions.
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Arxiv
4d
90
Image Credit: Arxiv
Neuron-level Balance between Stability and Plasticity in Deep Reinforcement Learning
- A new method called Neuron-level Balance between Stability and Plasticity (NBSP) has been proposed for deep reinforcement learning.
- NBSP focuses on the trade-off between retaining existing skills (stability) and learning new knowledge (plasticity).
- The method identifies RL skill neurons crucial for knowledge retention and introduces a framework to target these neurons for preserving existing skills while enabling adaptation to new tasks.
- Experimental results on Meta-World and Atari benchmarks show that NBSP outperforms existing approaches in balancing stability and plasticity.
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Arxiv
4d
215
Image Credit: Arxiv
Self-Bootstrapping for Versatile Test-Time Adaptation
- This paper introduces a self-bootstrapping scheme for versatile test-time adaptation (TTA) objective in various tasks including image classification, regression, object-level predictions, and pixel-level predictions.
- The scheme optimizes prediction consistency between the test image and its deteriorated view.
- The paper addresses challenges related to preserving geometric information and providing sufficient learning signals for TTA.
- Experiments demonstrate superior results of the proposed method across different tasks.
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Arxiv
4d
341
Image Credit: Arxiv
Vector Quantized-Elites: Unsupervised and Problem-Agnostic Quality-Diversity Optimization
- Vector Quantized-Elites is a novel Quality-Diversity algorithm that constructs a structured behavioral space grid.
- It eliminates the need for prior task-specific knowledge by using unsupervised learning.
- The integration of Vector Quantized Variational Autoencoders enables dynamic learning of behavioral descriptors.
- VQ-Elites demonstrates its ability to efficiently generate diverse, high-quality solutions in robotic arm and mobile robot tasks.
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Arxiv
4d
56
Image Credit: Arxiv
The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic Tree Search
- The AI Scientist-v2 is an agentic system capable of producing AI-generated peer-review-accepted workshop papers.
- This system formulates hypotheses, conducts experiments, analyzes data, and authors scientific manuscripts autonomously.
- Compared to its predecessor, The AI Scientist-v2 eliminates reliance on human-authored code templates and generalizes effectively.
- One AI-generated manuscript successfully navigated peer review, demonstrating AI's capability in scientific research.
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Arxiv
4d
178
Image Credit: Arxiv
Programs as Singularities
- Researchers have developed a correspondence between the structure of Turing machines and the structure of singularities of real analytic functions.
- The correspondence is based on connecting the Ehrhard-Regnier derivative from linear logic with the role of geometry in Watanabe's singular learning theory.
- By embedding ordinary (discrete) Turing machine codes into a family of noisy codes, a smooth parameter space is formed.
- The structure of the Turing machine and its associated singularity is related to Bayesian inference, implying that the Bayesian posterior can discriminate between different algorithmic implementations.
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Arxiv
4d
346
Image Credit: Arxiv
Multi-view autoencoders for Fake News Detection
- Automatic fake news detection has become a highly important task due to the volume and speed at which fake news spreads across social media.
- This paper proposes using multi-view autoencoders to generate a joint feature representation for fake news detection.
- Experiments on fake news datasets show a significant improvement in classification performance compared to individual views.
- Selecting a subset of views instead of composing a latent space with all views can be advantageous in terms of accuracy and computational effort.
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Arxiv
4d
73
Image Credit: Arxiv
RL-based Control of UAS Subject to Significant Disturbance
- This paper presents a Reinforcement Learning (RL)-based control framework for position and attitude control of Unmanned Aerial Systems (UAS) subjected to significant disturbances.
- The proposed method enables the system to anticipate and counteract disturbances by learning the relationship between the trigger signal and disturbance force.
- Three policies were trained and evaluated: a baseline policy without exposure to disturbances, a reactive policy trained with disturbances but without the trigger signal, and a predictive policy that incorporates the trigger signal as an observation and is exposed to disturbances during training.
- Simulation results demonstrate that the predictive policy outperforms the other policies by proactively correcting position deviations and improving UAS performance.
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Arxiv
4d
77
Image Credit: Arxiv
Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) Algorithms
- Anomaly detection is a key research challenge in computer vision and machine learning with applications in many fields from quality control to radar imaging.
- In radar imaging, specifically synthetic aperture radar (SAR), anomaly detection can be used for the classification, detection, and segmentation of objects of interest.
- To address the lack of method for developing and benchmarking SAR imagery anomaly detection methods, the Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) suite is introduced.
- SARIAD integrates multiple SAR datasets, various anomaly detection algorithms, and provides metric evaluation and visualization tools for benchmarking SAR models and datasets.
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