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ML News
Medium
4d
254
Image Credit: Medium
The invisible pixels: When technology seems to read our minds
- Tracking pixels, tiny images that load when you visit a website or open an application, are used to track user activity, unlike traditional cookies.
- Machine learning and data science analyze the data collected by pixels to create accurate predictive profiles and personalize recommendations on platforms like Amazon and Mercado Libre.
- Although technically not spying on private conversations, the sophisticated tracking and interpretation of digital footprints through predictive analytics techniques can create a sense of surveillance.
- There is a growing awareness about digital privacy, leading to emerging trends to mitigate the impact of invisible tracking and give users more control.
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Medium
4d
181
Image Credit: Medium
Transforming Healthcare with AI: A Comprehensive Guide
- AI technologies in healthcare cover a wide range of tasks, from patient registration to image recognition and data analysis, improving efficiency and patient satisfaction.
- AI applications include analyzing MRI/CT scan results, automated patient reminders, and high-risk patient identification for proactive care plans.
- Machine learning and deep learning assist in various healthcare areas, such as diagnostics and treatment, with different AI types serving specific functions.
- AI systems in radiology, cardiology, and neurology accelerate disease detection and treatment decisions, impacting patient outcomes.
- Integrated AI platforms in hospitals optimize workflows, reducing diagnostic times and improving overall patient care.
- AI adoption leads to reduced costs, improved diagnostic accuracy, and enhanced patient satisfaction, transforming healthcare services.
- Alignment between technology and personnel is crucial for successful AI integration in healthcare institutions.
- Generative AI advancements enhance automation and decision-making in areas like drug discovery and telemedicine, requiring close human oversight for accuracy.
- AI platforms unify data and workflows, streamlining processes across medical specialties for more efficient and accurate care.
- Appropriate AI implementation involves defining goals, selecting reliable vendors, staff training, regular audits, and prioritizing data privacy.
- AI can elevate healthcare standards by optimizing diagnoses, reducing administrative burdens, and fostering a culture of digital literacy among staff.
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Pymnts
4d
117
Image Credit: Pymnts
Acquirers Step Up Battle Against Fraud With GenAI, Biometrics Push
- Acquirers are stepping up their battle against fraud by leveraging emerging technologies and offering biometric authentication to their merchant clients.
- Fraudsters are using AI, cyberattacks, and data breaches to create new avenues of attack, leading acquirers to invest in AI, data-driven analytics, and other technologies to strengthen their fraud defenses.
- A significant increase in fraud rates has been reported by smaller acquirers, while larger acquirers have experienced a less dramatic rise.
- While most acquirers already provide fraud prevention technologies such as consumer transaction alerts, automated fraud responses, AI or ML systems, and fraud prevention APIs, there is a growing demand for advanced technologies like biometric authentication and GenAI.
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Medium
4d
340
Image Credit: Medium
The Intelligence Cure: How AI is Reshaping Medicine and Diagnostics
- AI is reshaping medicine and diagnostics, particularly in medical imaging analysis, pathology and lab diagnostics, predictive analytics, personalized treatment plans, and virtual health assistants.
- AI models have shown higher accuracy and consistency in identifying breast cancer in mammograms and diagnosing skin conditions. These advancements are being tested in low-resource settings where access to specialists is limited.
- However, there are challenges in terms of regulatory constraints, interpretability of AI models, and ethical concerns regarding patient consent, data privacy, and accountability in medical decisions.
- To realize the full potential of AI in healthcare, interdisciplinary solutions incorporating computer science, medicine, law, and ethics are necessary.
- Investment in infrastructure, internet connectivity, and training is crucial for achieving global equity in AI access.
- The impact of AI in healthcare will depend on our values, policies, and how we choose to utilize it, which will shape the future of human health.
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Arxiv
4d
316
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Compositional Flows for 3D Molecule and Synthesis Pathway Co-design
- Compositional Generative Flows (CGFlow) is a framework introduced to generate objects in compositional steps while modeling continuous states.
- It is an extension of flow matching interpolation process and utilizes the theoretical foundations of generative flow networks (GFlowNets).
- CGFlow is applied in synthesizable drug design to jointly design the molecule's synthetic pathway with its 3D binding pose.
- The method achieves state-of-the-art binding affinity and improved sampling efficiency compared to a 2D synthesis-based baseline.
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Arxiv
4d
180
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Deep Reinforcement Learning for Day-to-day Dynamic Tolling in Tradable Credit Schemes
- Tradable credit schemes (TCS) are an alternative to congestion pricing, addressing equity and revenue neutrality.
- This paper focuses on the day-to-day dynamic tolling problem under TCS using reinforcement learning (RL).
- RL algorithms achieve comparable travel times and social welfare, even with varying capacities and demand levels.
- Challenges include scaling to large networks, but transfer learning can improve computational efficiency.
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Arxiv
4d
67
Image Credit: Arxiv
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
- Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science.
- Graph Neural Networks (GNNs) are widely used for atomistic materials modeling due to their ability to capture complex relational structures.
- To address the gap in size and performance compared to large language models, a foundational GNN model with billions of parameters was developed and trained on terabyte-scale datasets.
- The study explores the scaling laws for GNNs, provides insights into the relationship between model size, dataset volume, and accuracy, and enhances the GNN codebase with advanced training techniques.
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Arxiv
4d
219
Image Credit: Arxiv
Between Linear and Sinusoidal: Rethinking the Time Encoder in Dynamic Graph Learning
- Dynamic graph learning requires effective modeling of temporal relationships in applications involving temporal networks.
- This paper explores the use of linear time encoder as an alternative to sinusoidal time encoder.
- The linear time encoder avoids temporal information loss caused by sinusoidal functions and reduces the need for high dimensional time encoders.
- Experimental results show that the linear time encoder improves the performance of existing models and leads to significant parameter savings.
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Arxiv
4d
154
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Beyond Feature Importance: Feature Interactions in Predicting Post-Stroke Rigidity with Graph Explainable AI
- This study focuses on predicting post-stroke rigidity using graph-based explainable AI.
- Graph-based models like Graphormer and Graph Attention Network outperform traditional approaches.
- Key predictors such as NIH Stroke Scale and APR-DRG mortality risk scores are identified.
- Graph-based XAI has the potential to guide early identification and personalized rehabilitation strategies.
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9 Likes
Arxiv
4d
158
Image Credit: Arxiv
Adaptive Bounded Exploration and Intermediate Actions for Data Debiasing
- The performance of algorithmic decision rules is largely dependent on the quality of training datasets available to them.
- Biases in these datasets can raise economic and ethical concerns due to the resulting algorithms' disparate treatment of different groups.
- The paper proposes algorithms for sequentially debiasing the training dataset through adaptive and bounded exploration.
- The algorithms aim to mitigate the impacts of data biases and achieve more accurate and fairer decisions.
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Arxiv
4d
284
Image Credit: Arxiv
Rethinking the Foundations for Continual Reinforcement Learning
- Algorithms and approaches for continual reinforcement learning have gained increasing attention.
- Traditional foundations of reinforcement learning may not be well-suited for continual reinforcement learning.
- Four foundations of traditional RL are identified as antithetical to the goals of continual reinforcement learning.
- Rethinking and proposing alternative foundations is necessary for the development of better-suited algorithms and approaches.
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17 Likes
Arxiv
4d
316
Image Credit: Arxiv
On the Practice of Deep Hierarchical Ensemble Network for Ad Conversion Rate Prediction
- A Deep Hierarchical Ensemble Network (DHEN) has been proposed to integrate multiple feature crossing modules and has achieved great success in CTR prediction.
- The performance of DHEN in CVR prediction is unclear in the conversion ads setting.
- The paper proposes a multitask learning framework with DHEN as the single backbone model architecture to predict all CVR tasks.
- The method achieves state-of-the-art performance compared to previous single feature crossing modules with pre-trained user personalization features.
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Arxiv
4d
138
Image Credit: Arxiv
Detecting Credit Card Fraud via Heterogeneous Graph Neural Networks with Graph Attention
- This study proposes a credit card fraud detection method based on Heterogeneous Graph Neural Network (HGNN) to address fraud in complex transaction networks.
- The approach constructs heterogeneous transaction graphs incorporating multiple node types: users, merchants, and transactions.
- The model uses graph neural networks to capture higher-order transaction relationships and employs a Graph Attention Mechanism to assign weights dynamically.
- The proposed method outperforms existing GNN models on the IEEE-CIS Fraud Detection dataset, achieving notable improvements in accuracy and OC-ROC.
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8 Likes
Arxiv
4d
259
Image Credit: Arxiv
SAEs $\textit{Can}$ Improve Unlearning: Dynamic Sparse Autoencoder Guardrails for Precision Unlearning in LLMs
- Machine unlearning is a promising approach to improve LLM safety.
- Sparse Autoencoders (SAEs) can significantly improve unlearning when employed dynamically.
- The proposed method, Dynamic DAE Guardrails (DSG), outperforms leading unlearning methods.
- DSG addresses key drawbacks of gradient-based approaches offering enhanced computational efficiency and stability, robust performance in sequential unlearning, and better data efficiency.
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15 Likes
Arxiv
4d
286
Image Credit: Arxiv
The More is not the Merrier: Investigating the Effect of Client Size on Federated Learning
- Federated Learning (FL) allows training a shared machine learning model while keeping data local to clients.
- The widely used FedAvg algorithm shows a decrease in learning accuracy as the number of clients increases.
- To address this issue, a method called Knowledgeable Client Insertion (KCI) is proposed, which introduces a small number of knowledgeable clients with large sets of data samples.
- The KCI approach improves the learning accuracy of FL even with the normal FedAvg aggregation technique, providing privacy protection for clients against security attacks.
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