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Mantis Biotech is making 'digital twins' of humans to help solve medicine's data availability problem | TechCrunch
Data Science

Mantis Biotech is making 'digital twins' of humans to help solve medicine's data availability problem | TechCrunch

Mantis takes disparate sources of data to make synthetic datasets that can be used to build so-called "digital twins" of the human body, ...

TechCrunch - AI · 6 min ·
Nlp

[P] Using YouTube as a data source (lessons from building a coffee domain dataset)

I started working on a small coffee coaching app recently - something that could answer questions around brew methods, grind size, extrac...

Reddit - Machine Learning · 1 min ·
UMKC Announces New Master of Science in Artificial Intelligence
Ai Infrastructure

UMKC Announces New Master of Science in Artificial Intelligence

UMKC announces a new Master of Science in Artificial Intelligence program aimed at addressing workforce demand for AI expertise, set to l...

AI News - General · 4 min ·

All Content

[2602.23121] Automated Vulnerability Detection in Source Code Using Deep Representation Learning
Machine Learning

[2602.23121] Automated Vulnerability Detection in Source Code Using Deep Representation Learning

This article presents a convolutional neural network model designed to automate the detection of vulnerabilities in C source code, achiev...

arXiv - AI · 4 min ·
[2505.08371] Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data
Machine Learning

[2505.08371] Density Ratio-based Causal Discovery from Bivariate Continuous-Discrete Data

This paper presents a novel method for determining causal direction between continuous and discrete variables using density ratios, demon...

arXiv - Machine Learning · 4 min ·
[2602.23073] Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds
Robotics

[2602.23073] Accelerated Online Risk-Averse Policy Evaluation in POMDPs with Theoretical Guarantees and Novel CVaR Bounds

This paper presents a theoretical framework for accelerating risk-averse policy evaluation in partially observable Markov decision proces...

arXiv - AI · 4 min ·
[2505.04733] Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting
Machine Learning

[2505.04733] Conformal Prediction with Corrupted Labels: Uncertain Imputation and Robust Re-weighting

This paper presents a framework for robust uncertainty quantification in machine learning when training data is corrupted. It introduces ...

arXiv - Machine Learning · 4 min ·
[2602.23070] Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment
Ai Safety

[2602.23070] Make It Hard to Hear, Easy to Learn: Long-Form Bengali ASR and Speaker Diarization via Extreme Augmentation and Perfect Alignment

This paper presents a novel approach to long-form Bengali Automatic Speech Recognition (ASR) and speaker diarization, introducing a compr...

arXiv - AI · 4 min ·
[2503.19874] Extensions of the regret-minimization algorithm for optimal design
Machine Learning

[2503.19874] Extensions of the regret-minimization algorithm for optimal design

This article presents extensions to the regret-minimization algorithm aimed at optimal design for multiclass classification, proposing a ...

arXiv - Machine Learning · 3 min ·
[2503.10503] Sample Compression for Self Certified Continual Learning
Machine Learning

[2503.10503] Sample Compression for Self Certified Continual Learning

The paper introduces Continual Pick-to-Learn (CoP2L), a method for continual learning that uses sample compression to mitigate catastroph...

arXiv - Machine Learning · 3 min ·
[2503.05560] Global graph features unveiled by unsupervised geometric deep learning
Machine Learning

[2503.05560] Global graph features unveiled by unsupervised geometric deep learning

The paper introduces GAUDI, an unsupervised geometric deep learning framework that captures global graph features, enhancing analysis and...

arXiv - Machine Learning · 4 min ·
[2602.23003] Scattering Transform for Auditory Attention Decoding
Machine Learning

[2602.23003] Scattering Transform for Auditory Attention Decoding

This paper explores the use of a scattering transform for auditory attention decoding, comparing its effectiveness against traditional pr...

arXiv - AI · 4 min ·
[2502.11816] Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting
Machine Learning

[2502.11816] Mixing It Up: Exploring Mixer Networks for Irregular Multivariate Time Series Forecasting

This paper introduces IMTS-Mixer, a novel architecture for forecasting irregular multivariate time series with missing values, achieving ...

arXiv - Machine Learning · 4 min ·
[2502.06051] Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits
Nlp

[2502.06051] Towards a Sharp Analysis of Offline Policy Learning for $f$-Divergence-Regularized Contextual Bandits

This paper presents a detailed analysis of offline policy learning in contextual bandits, focusing on $f$-divergence regularization and i...

arXiv - Machine Learning · 4 min ·
[2602.22967] Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression
Llms

[2602.22967] Discovery of Interpretable Physical Laws in Materials via Language-Model-Guided Symbolic Regression

This paper presents a novel framework that utilizes language models to guide symbolic regression in discovering interpretable physical la...

arXiv - AI · 3 min ·
[2410.12439] Beyond Attribution: Unified Concept-Level Explanations
Machine Learning

[2410.12439] Beyond Attribution: Unified Concept-Level Explanations

The paper presents UnCLE, a framework that enhances model-agnostic explanation techniques by integrating concept-based approaches, offeri...

arXiv - Machine Learning · 3 min ·
[2602.22955] MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Machine Learning

[2602.22955] MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis

The article presents MM-NeuroOnco, a comprehensive dataset aimed at improving MRI-based brain tumor diagnosis through multimodal instruct...

arXiv - AI · 4 min ·
[2410.10922] Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure
Machine Learning

[2410.10922] Towards Privacy-Guaranteed Label Unlearning in Vertical Federated Learning: Few-Shot Forgetting without Disclosure

This paper introduces a novel method for label unlearning in Vertical Federated Learning (VFL), addressing privacy concerns while maintai...

arXiv - Machine Learning · 4 min ·
[2408.01503] Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs
Machine Learning

[2408.01503] Efficient Graph Coloring with Neural Networks: A Physics-Inspired Approach for Large Graphs

This article presents a novel physics-inspired neural network approach for efficiently solving large-scale graph coloring problems, a key...

arXiv - Machine Learning · 4 min ·
[2404.01877] Procedural Fairness in Machine Learning
Machine Learning

[2404.01877] Procedural Fairness in Machine Learning

This paper explores procedural fairness in machine learning, proposing a new metric for evaluation and methods to enhance fairness withou...

arXiv - Machine Learning · 4 min ·
[2407.17120] Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective
Machine Learning

[2407.17120] Parameter-Efficient Fine-Tuning for Continual Learning: A Neural Tangent Kernel Perspective

This article explores Parameter-Efficient Fine-Tuning for Continual Learning (PEFT-CL) using Neural Tangent Kernel (NTK) theory, addressi...

arXiv - Machine Learning · 4 min ·
[2602.22873] Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases
Nlp

[2602.22873] Learning Tangent Bundles and Characteristic Classes with Autoencoder Atlases

This paper introduces a framework connecting multi-chart autoencoders with vector bundles and characteristic classes, enhancing manifold ...

arXiv - AI · 3 min ·
[2602.23321] Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays
Machine Learning

[2602.23321] Deep ensemble graph neural networks for probabilistic cosmic-ray direction and energy reconstruction in autonomous radio arrays

This paper presents a novel method using deep ensemble graph neural networks to accurately reconstruct the direction and energy of cosmic...

arXiv - Machine Learning · 4 min ·
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