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ML News
Amazon
2d
299
Image Credit: Amazon
Context extraction from image files in Amazon Q Business using LLMs
- Organizations rely on visual docs, but info in visuals often remains inaccessible.
- Amazon Q Business's custom document enrichment processes standalone image files effectively.
- Implementation steps for CDE feature in Amazon Q Business application are outlined.
- Enables natural language queries against visualizations, bridging gap between text and visuals.
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Amazon
2d
106
Image Credit: Amazon
Build AWS architecture diagrams using Amazon Q CLI and MCP
- Creating AWS architecture diagrams is crucial for technical teams and stakeholders.
- Amazon Q CLI with MCP streamlines diagram creation, reducing time and learning curve.
- Detailed instructions on setting up environment, using MCP servers, generating diagrams.
- Integration with AI tools like Amazon Q CLI enhances diagram accuracy and efficiency.
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Medium
2d
23
Title: ML in Social Media Trend Analysis — A Game Changer by Brandnity, the Best Website Designing…
- Machine Learning (ML) is utilized by Brandnity for social media trend analysis to track content trends on various platforms.
- ML models can analyze large volumes of unstructured data such as posts, comments, images, and videos to identify trends.
- ML algorithms help determine the emotional tone of posts, identify trending hashtags and keywords, and detect visual trends through computer vision.
- Brandnity, as a website designing company, integrates ML-driven social media insights with website performance to create impactful solutions for clients.
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Medium
2d
3
Image Credit: Medium
Introducing the Tiny Language Model (TLM): A Mathematical Exposition
- The Tiny Language Model (TLM) employs a multi-layered LSTM network.
- LSTMs mitigate vanishing/explosive gradient challenges, aiding long-term dependency learning.
- TLM uses gates and cell states to control and propagate information effectively.
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Marktechpost
2d
316
MDM-Prime: A generalized Masked Diffusion Models (MDMs) Framework that Enables Partially Unmasked Tokens during Sampling
- Masked Diffusion Models (MDMs) are powerful tools for generating discrete data by gradually unmasking tokens over time, but inefficiencies result in wasted computation due to unchanged sequences in many steps.
- Recent enhancements in MDMs include refining training objectives, blending autoregressive methods, guiding sampling with energy-based models, and introducing Prime, a method that allows tokens to assume intermediate states by masking sub-parts of their encoded form.
- MDM-Prime, an enhanced model utilizing the Prime method, achieves lower perplexity on text and competitive FID scores on image tasks, outperforming previous MDMs and autoregressive models.
- MDM-Prime's architecture involves sub-token level partial masking, enabling smoother intermediate state generation, improved model efficiency, and stronger performance in text and image generation tasks.
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Medium
2d
248
Image Credit: Medium
AI-Powered Orthopedic Insoles: Revolutionizing Custom Footwear with Machine Learning
- Groundbreaking toolbox using machine learning for custom orthopedic insoles is revolutionizing footwear.
- Digital pedobarography collects foot data, processed through ML for optimal insole 3D modeling.
- Modular architecture enables seamless workflow from data collection to 3D printing for insoles.
- AI predicts parameters, Fusion 360 applies to base model, Orca Slicer prepares for printing.
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Arxiv
2d
321
Image Credit: Arxiv
APO: Enhancing Reasoning Ability of MLLMs via Asymmetric Policy Optimization
- Multimodal Large Language Models (MLLMs) are powerful at integrating diverse data but struggle with complex reasoning.
- Reinforcement learning (RL) can boost reasoning in LLMs, but applying it to MLLMs is challenging due to issues like a drop in performance on general tasks and overthinking reasoning.
- A new approach called Asymmetric Policy Optimization (APO) is proposed to enhance the reasoning abilities of MLLMs by addressing issues related to KL penalty, overthinking, and overly detailed responses.
- The application of APO to a specific MLLM model (View-R1-3B) resulted in a significant 7% gain in reasoning capabilities over the base model and outperformed larger MLLMs on various reasoning benchmarks while maintaining consistency across general tasks.
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Arxiv
2d
226
Image Credit: Arxiv
Unimodal Strategies in Density-Based Clustering
- Density-based clustering methods are more effective than centroid-based ones for data with noise or diverse distributions.
- A recent study introduces a key property related to the number of clusters and core point radius in density-based clustering.
- New strategies for determining radius values more efficiently using the Ternary Search algorithm are proposed based on this property.
- Extensive validation on various high-dimensional data tasks shows the practical effectiveness and robustness of the proposed methodology.
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Arxiv
2d
178
Image Credit: Arxiv
$\textrm{ODE}_t \left(\textrm{ODE}_l \right)$: Shortcutting the Time and Length in Diffusion and Flow Models for Faster Sampling
- Researchers proposed a new method, ODE_t(ODE_l), to enhance sampling efficiency in continuous normalizing flows and diffusion models.
- The method involves controlling the tradeoff between quality and complexity by adjusting time steps and the length of the neural network.
- By using this approach, sampling can be done with varying time steps and transformer blocks, reducing latency and memory usage.
- Experiments on image generation datasets demonstrated up to a 3x latency reduction and a 3.5-point FID score improvement compared to the previous state of the art.
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Arxiv
2d
162
Image Credit: Arxiv
Performance Prediction for Large Systems via Text-to-Text Regression
- Text-to-text regression is proposed as a scalable alternative for predicting metric outcomes of large systems where traditional tabular regression methods struggle.
- A 60M parameter encoder-decoder model trained from random initialization achieves high accuracy in predicting resource efficiency on Google's massive compute cluster scheduling system.
- The text-to-text regression model outperforms tabular approaches with a near-perfect rank correlation and significantly lower mean squared error across the system fleet.
- The model demonstrates adaptability to new tasks with few-shot examples and effectively captures complex outcome distributions, with important implications for universal simulators of real-world outcomes.
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Arxiv
2d
206
Image Credit: Arxiv
Federated Item Response Theory Models
- Federated Item Response Theory (IRT) models integrate federated learning to estimate traditional IRT models with added privacy features.
- This approach allows for distributed estimation without centralized raw response data, addressing privacy concerns and reducing communication costs.
- Numerical experiments show that FedIRT achieves similar accuracy to standard IRT estimation, with the added benefits of privacy protection.
- The framework expands IRT applicability to distributed settings like multi-school assessments and is supported by an open-source R package, FedIRT.
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Arxiv
2d
245
Image Credit: Arxiv
Gradient-Based Neuroplastic Adaptation for Concurrent Optimization of Neuro-Fuzzy Networks
- A new approach, gradient-based neuroplastic adaptation, is proposed for optimizing Neuro-fuzzy networks (NFNs) parameters and structure concurrently.
- NFNs are symbolic function approximations with advantages like transparency and universal function approximation ability.
- The traditional sequential design process for NFNs is inefficient, leading to suboptimal architecture; the new approach addresses this limitation.
- Empirical evidence shows the effectiveness of the new method in training NFNs with online reinforcement learning to excel in vision-based video game scenarios.
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Arxiv
2d
95
Image Credit: Arxiv
Multi-task parallelism for robust pre-training of graph foundation models on multi-source, multi-fidelity atomistic modeling data
- Graph foundation models using graph neural networks are being used for atomistic modeling to handle multi-source, multi-fidelity data during pre-training.
- Recent studies employ multi-task learning where shared layers process atomistic structures initially regardless of the source, routing them to different decoding heads for data-specific predictions.
- A new multi-task parallelism method is proposed to distribute each head across computing resources with GPU acceleration, implemented in the open-source HydraGNN architecture.
- The method was trained on over 24 million structures from five datasets and tested on supercomputers like Perlmutter, Aurora, and Frontier, showing efficient scaling on heterogeneous computing architectures.
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Arxiv
2d
372
Image Credit: Arxiv
Why Neural Network Can Discover Symbolic Structures with Gradient-based Training: An Algebraic and Geometric Foundation for Neurosymbolic Reasoning
- Researchers have developed a theoretical framework explaining how neural networks can naturally discover discrete symbolic structures through gradient-based training.
- By lifting neural parameters to a measure space and utilizing Wasserstein gradient flow, the framework demonstrates the emergence of symbolic phenomena under geometric constraints like group invariance.
- The framework highlights the decoupling of gradient flow into independent optimization trajectories based on potential functions and a reduction in degrees of freedom, leading to the encoding of algebraic constraints relevant to the task.
- The research establishes data scaling laws connecting representational capacity to group invariance, enabling neural networks to transition from high-dimensional exploration to compositional representations aligned with algebraic operations, offering insights for designing neurosymbolic systems.
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Arxiv
2d
91
Image Credit: Arxiv
The Cost of Avoiding Backpropagation
- Forward-mode automatic differentiation (FmAD) and zero-order (ZO) optimization have been proposed as memory-efficient alternatives to backpropagation (BP) for gradient computation.
- A new study presents a comparison of BP, FmAD, and ZO methods, highlighting theoretical and empirical findings.
- Theoretical analysis suggests that FmAD and ZO reduce memory usage but at the cost of accuracy, convergence speed, and computation compared to BP with checkpointing.
- Empirical experiments on large models demonstrate that BP with checkpointing outperforms FmAD and ZO variants, indicating BP with checkpointing as the most effective strategy for model training in memory-constrained settings.
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