World PulseNow
Newsletter

Amortized Variational Inference for Partial-Label Learning: A Probabilistic Approach to Label Disambiguation

arXiv — cs.LG•Monday, October 27, 2025 at 4:00:00 AM
PositiveArtificial Intelligence
A new study introduces Amortized Variational Inference for Partial-Label Learning, a method designed to tackle the challenges of noisy and ambiguous data in real-world scenarios. This approach is particularly relevant in crowdsourcing, where conflicting labels can arise from human annotators. By improving the efficiency of training classifiers with candidate labels, this research not only enhances the accuracy of machine learning models but also opens up new avenues for handling complex data, making it a significant advancement in the field.
— via World Pulse Now AI Editorial System
Read Original

Was this article worth reading? Share it

Recommended apps based on your readingExplore all apps

LucidQuery AI

Combines diffusion reasoning with autoregressive LLM for advanced AI analysis.

AI & DataView app details

Supametas.AI

Extract and structure unstructured data for seamless LLM RAG integration.

AI & DataView app details

The Visualizer

Transform complex topics into clear, visual explanations for effortless learning.

AI & DataView app details

Sellm

Track brand mentions across ChatGPT, Perplexity, and other AI platforms.

Marketing & CommerceView app details

Dubsmart LLC

Multilingual AI dubbing and voice cloning for global video content localization.

AI & DataView app details

GPTHumanizer

Bypass AI detection with guaranteed undetectable content generation.

AI & DataView app details
Continue Readings
ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning
arXiv — cs.CV2 days ago
ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning
PositiveArtificial Intelligence
A new deep learning model named ISLA (Ischemic Stroke Lesion Analyzer) has been introduced for the segmentation of acute ischemic stroke lesions in MRI scans. This model leverages the U-Net architecture and incorporates deep supervision, attention mechanisms, and domain adaptation, trained on over 1500 participants from multiple centers.
Read full article
via arXiv — cs.CV
Are Emotions Arranged in a Circle? Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning
arXiv — cs.CL2 days ago
Are Emotions Arranged in a Circle? Geometric Analysis of Emotion Representations via Hyperspherical Contrastive Learning
NeutralArtificial Intelligence
A recent study titled 'Are Emotions Arranged in a Circle?' explores the geometric analysis of emotion representations through hyperspherical contrastive learning, proposing a method to align emotions in a circular format within language model embeddings. This approach aims to enhance interpretability and robustness against dimensionality reduction, although it shows limitations in high-dimensional settings and fine-grained classification tasks.
Read full article
via arXiv — cs.CL
Decoder Generates Manufacturable Structures: A Framework for 3D-Printable Object Synthesis
arXiv — cs.CV2 days ago
Decoder Generates Manufacturable Structures: A Framework for 3D-Printable Object Synthesis
PositiveArtificial Intelligence
A novel decoder-based approach has been introduced for generating manufacturable 3D structures optimized for additive manufacturing, utilizing a deep learning framework that decodes latent representations into geometrically valid, printable objects. This methodology respects manufacturing constraints and demonstrates improved manufacturability over traditional generation methods.
Read full article
via arXiv — cs.CV
AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions
arXiv — cs.CV2 days ago
AIMC-Spec: A Benchmark Dataset for Automatic Intrapulse Modulation Classification under Variable Noise Conditions
NeutralArtificial Intelligence
A new benchmark dataset named AIMC-Spec has been introduced to enhance automatic intrapulse modulation classification (AIMC) in radar signal analysis, particularly under varying noise conditions. This dataset includes 33 modulation types across 13 signal-to-noise ratio levels, addressing a significant gap in standardized datasets for this critical task.
Read full article
via arXiv — cs.CV
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
arXiv — cs.LG2 days ago
Interpretability and Individuality in Knee MRI: Patient-Specific Radiomic Fingerprint with Reconstructed Healthy Personas
PositiveArtificial Intelligence
A recent study has introduced a novel approach to knee MRI analysis, emphasizing the importance of both interpretability and individuality through patient-specific radiomic fingerprints and reconstructed healthy personas. This method aims to enhance automated assessments by dynamically selecting features relevant to individual patients rather than relying on uniform population-level signatures.
Read full article
via arXiv — cs.LG
Gradient flow in parameter space is equivalent to linear interpolation in output space
arXiv — stat.ML2 days ago
Gradient flow in parameter space is equivalent to linear interpolation in output space
NeutralArtificial Intelligence
Recent research has demonstrated that the conventional gradient flow in parameter space, which is foundational to many deep learning training algorithms, can be transformed into an adapted gradient flow that results in Euclidean gradient flow in output space. This finding indicates that under certain conditions, such as having a full-rank Jacobian for the L2 loss, the flow can simplify to linear interpolation, leading to a global minimum.
Read full article
via arXiv — stat.ML
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
arXiv — cs.LG2 days ago
A Comparative Study of Traditional Machine Learning, Deep Learning, and Large Language Models for Mental Health Forecasting using Smartphone Sensing Data
PositiveArtificial Intelligence
A comprehensive benchmarking study has been conducted to compare traditional machine learning, deep learning, and large language models for forecasting mental health using smartphone sensing data, specifically the College Experience Sensing dataset. This research highlights the potential of these technologies to proactively support mental health interventions by tracking behavioral changes that precede symptoms of stress, anxiety, or depression.
Read full article
via arXiv — cs.LG
Deep Exploration of Epoch-wise Double Descent in Noisy Data: Signal Separation, Large Activation, and Benign Overfitting
arXiv — stat.ML2 days ago
Deep Exploration of Epoch-wise Double Descent in Noisy Data: Signal Separation, Large Activation, and Benign Overfitting
NeutralArtificial Intelligence
A recent study has empirically investigated epoch-wise double descent in deep learning, particularly focusing on the effects of noisy data on model generalization. Using fully connected neural networks trained on the CIFAR-10 dataset with 30% label noise, the research revealed that models can achieve strong re-generalization even after overfitting to noisy data, indicating a state of benign overfitting.
Read full article
via arXiv — stat.ML

Ready to build your own newsroom?

Subscribe to unlock a personalised feed, podcasts, newsletters, and notifications tailored to the topics you actually care about