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Found 21 papers, 11 papers with code
GLIMMER: Incorporating Graph and Lexical Features in Unsupervised Multi-Document Summarization
Pre-trained language models are increasingly being used in multi-document summarization tasks.
Model-driven Heart Rate Estimation and Heart Murmur Detection based on Phonocardiogram
Acoustic signals are crucial for health monitoring, particularly heart sounds which provide essential data like heart rate and detect cardiac anomalies such as murmurs.
A Survey on the Real Power of ChatGPT
ChatGPT has changed the AI community and an active research line is the performance evaluation of ChatGPT.
Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance
Classification models are expected to perform equally well for different classes, yet in practice, there are often large gaps in their performance.
Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignals
To achieve effective pretraining in the presence of potential distributional shifts, we propose a frequency-aware masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the representation of biosignals in the frequency space.
LatentDR: Improving Model Generalization Through Sample-Aware Latent Degradation and Restoration
To address this challenge, we propose a novel approach for distribution-aware latent augmentation that leverages the relationships across samples to guide the augmentation procedure.
Half-Hop: A graph upsampling approach for slowing down message passing
Message passing neural networks have shown a lot of success on graph-structured data.
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A Feature Set of Small Size for the PDF Malware Detection
Machine learning (ML)-based malware detection systems are becoming increasingly important as malware threats increase and get more sophisticated.
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?
With the increasing number and sophistication of malware attacks, malware detection systems based on machine learning (ML) grow in importance.
MTNeuro: A Benchmark for Evaluating Representations of Brain Structure Across Multiple Levels of Abstraction
To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image.
Clustering and Analysis of GPS Trajectory Data using Distance-based Features
We propose a new mobility metric, Daily Characteristic Distance, and use it to generate features for each user together with Origin-Destination matrix features.
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers
By enabling flexible pre-training that can be transferred to neural recordings of different size and order, our work provides a first step towards creating a foundation model for neural decoding.
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task Transfer
Based upon this insight, we propose See-SPOT-Run (SSR), a new computational approach to robot learning that enables a robot to complete a variety of real robot tasks in novel problem domains without task-specific training.
Dual Band GNSS Antenna Phase Center Characterization for Automotive Applications
In this paper, a low-cost small size dual-band ceramic GNSS patch antenna is presented from design to real sample.
Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity
Our approach combines a generative modeling framework with an instance-specific alignment loss that tries to maximize the representational similarity between transformed views of the input (brain state).
Relative Localization of Mobile Robots with Multiple Ultra-WideBand Ranging Measurements
We propose an approach to estimate the relative pose between a group of robots by equipping each robot with multiple UWB ranging nodes.
Offline reinforcement learning with uncertainty for treatment strategies in sepsis
Guideline-based treatment for sepsis and septic shock is difficult because sepsis is a disparate range of life-threatening organ dysfunctions whose pathophysiology is not fully understood.
Understand and Improve Contrastive Learning Methods for Visual Representation: A Review
Traditional supervised learning methods are hitting a bottleneck because of their dependency on expensive manually labeled data and their weaknesses such as limited generalization ability and vulnerability to adversarial attacks.
WiFi Fingerprint Clustering for Urban Mobility Analysis
We propose a system architecture to scan the surrounding WiFi AP, and perform unsupervised learning to demonstrate that it is possible to identify three major insights, namely the indoor POI within a building, neighbourhood activity, and micro-mobility of the users.
Mine Your Own vieW: Self-Supervised Learning Through Across-Sample Prediction
State-of-the-art methods for self-supervised learning (SSL) build representations by maximizing the similarity between different transformed "views" of a sample.
A Physiology-Driven Computational Model for Post-Cardiac Arrest Outcome Prediction
Combined EHR-PTS24 models had higher discrimination (area under the receiver operating characteristic curve [AUC]) than models which used either EHR or PTS24 alone, for the prediction of survival (AUC 0. 85, 0. 80 and 0. 68 respectively) and neurological outcome (0. 87, 0. 83 and 0. 78).
