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Arxiv
1d
275
Image Credit: Arxiv
Data-driven decision-making under uncertainty with entropic risk measure
- The entropic risk measure is commonly used in high-stakes decision making to account for uncertain losses.
- An empirical entropic risk estimator is often biased and underestimates the true risk with limited data.
- A bootstrapping procedure is proposed to debias the empirical entropic risk estimator, improving risk estimation.
- The approach is applied to distributionally robust entropic risk minimization and insurance contract design problems.
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Arxiv
1d
230
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TableRAG: Million-Token Table Understanding with Language Models
- Recent advancements in language models have enhanced their ability to reason with tabular data.
- TableRAG is a Retrieval-Augmented Generation (RAG) framework designed for LM-based table understanding.
- TableRAG leverages query expansion and schema/cell retrieval for efficient data encoding and precise retrieval.
- TableRAG achieves the highest retrieval quality and state-of-the-art performance on large-scale table understanding.
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Arxiv
1d
245
Image Credit: Arxiv
Variational Diffusion Posterior Sampling with Midpoint Guidance
- Diffusion models have shown potential in solving Bayesian inverse problems as priors.
- Sampling from denoising posterior distributions in diffusion models is challenging due to intractable terms.
- A novel approach is proposed that allows a trade-off between complexity of the intractable guidance term and prior transitions.
- The proposed approach is validated through experiments on inverse problems and applied to cardiovascular disease diagnosis.
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Arxiv
1d
56
Image Credit: Arxiv
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
- Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology.
- Most studies have not considered the skewed degree distribution of genes, which complicates the application of directed graph embedding methods.
- To address this issue, the Cross-Attention Complex Dual Graph Embedding Model (XATGRN) is proposed.
- XATGRN effectively captures intricate gene interactions and accurately predicts regulatory relationships and their directionality.
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Arxiv
1d
86
Image Credit: Arxiv
Integrating Random Effects in Variational Autoencoders for Dimensionality Reduction of Correlated Data
- Variational Autoencoders (VAE) are widely used for dimensionality reduction of large-scale tabular and image datasets.
- The proposed model, LMMVAE, separates the VAE latent model into fixed and random parts to account for correlated data observations.
- LMMVAE improves squared reconstruction error and negative likelihood loss on unseen data.
- It also enhances performance in downstream tasks such as supervised classification.
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Arxiv
1d
336
Image Credit: Arxiv
Distilled Decoding 1: One-step Sampling of Image Auto-regressive Models with Flow Matching
- Autoregressive (AR) models have achieved state-of-the-art performance in text and image generation.
- Existing methods to speed up AR generation by generating multiple tokens at once are limited in capturing the output distribution due to token dependencies.
- Distilled Decoding (DD) uses flow matching to create a deterministic mapping from Gaussian distribution to the output distribution, enabling few-step generation.
- DD achieves promising results on ImageNet-256, enabling one-step generation with a speed-up of 6.3x for VAR and 217.8x for LlamaGen.
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Arxiv
1d
328
Image Credit: Arxiv
The Potential of Convolutional Neural Networks for Cancer Detection
- Convolutional Neural Networks (CNNs) have emerged as a powerful tool for analyzing and classifying medical images in cancer detection.
- This paper reviews recent studies on CNN models for detecting ten different types of cancer using diverse datasets.
- The paper compares and analyzes the performance and strengths of different CNN architectures in improving early detection.
- The study explores the feasibility of integrating CNNs into clinical settings as an early detection tool for cancer.
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Arxiv
1d
290
Image Credit: Arxiv
Towards An Unsupervised Learning Scheme for Efficiently Solving Parameterized Mixed-Integer Programs
- Researchers have proposed a novel unsupervised learning scheme for accelerating the solution of mixed integer programming (MIP) problems.
- The scheme involves training an autoencoder (AE) in an unsupervised learning fashion using historical instances of optimal solutions to a parametric family of MIPs.
- By designing the AE architecture and utilizing its statistical implications, the researchers construct cutting plane constraints from the decoder parameters. These constraints improve the efficiency of solving new problem instances.
- The proposed approach demonstrates significant reduction in computational cost for solving mixed integer linear programming (MILP) problems, while maintaining high solution quality.
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Medium
1d
34
Image Credit: Medium
How Google’s Dominance Challenges OpenAI and Sora Without Breaking a Sweat
- Google's dominance in the AI industry poses challenges for competitors like OpenAI and Sora.
- Google's ecosystem and vast resources give it an edge in terms of data, infrastructure, and scale.
- The seamless integration of AI into everyday tools strengthens Google's position.
- Google's AI initiatives are built on years of experience and technological leadership, making it appear effortless.
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Medium
1d
45
Image Credit: Medium
Exploring Quantum Computers:
- Quantum computers operate using qubits, which can exist in multiple states simultaneously.
- Two key phenomena, superposition and entanglement, make quantum computers unique.
- Potential applications of quantum computers include breaking encryption, simulating molecular structures, analyzing climate changes, and enhancing AI systems.
- Quantum computers face challenges such as the need for stable environments, high costs, and limited accessibility.
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Marktechpost
1d
197
This AI Paper by The Data Provenance Initiative Team Highlights Challenges in Multimodal Dataset Provenance, Licensing, Representation, and Transparency for Responsible Development
- The advancement of artificial intelligence hinges on the availability and quality of training data, particularly as multimodal foundation models grow in prominence.
- Web-based or synthetic sources like YouTube and Wikipedia account for a significant share of speech, text, and video datasets, leading to data not representing underrepresented languages and regions adequately.
- Nearly 80% of widely used datasets carry some form of implicit restrictions despite only 33% being explicitly licensed for non-commercial use, creating legal ambiguities and ethical challenges.
- There is an urgent need for a systematic audit of multimodal datasets that holistically considers their sourcing, licensing, and representation to enable the development of unbiased and legally sound technologies.
- Researchers from the Data Provenance Initiative conducted the largest longitudinal audit of multimodal datasets revealing the dominance of web-crawled data and providing valuable insights for developers and policymakers.
- The lack of transparency and persistent Western-centric biases call for more rigorous audits and equitable practices in dataset curation and prioritizing transparency in data provenance.
- The study highlights the significant inconsistencies in how data is licensed and documented and reveals stark geographical imbalances with North American and European organizations dominating and African and South American organizations lagging behind.
- The research provides a roadmap for creating more transparent, equitable, and responsible AI systems and underscores the need for continued vigilance and measures.
- The audit showed that over 70% of speech and video datasets are derived from web platforms, while synthetic sources are becoming increasingly popular, accounting for nearly 10% of all text data tokens.
- The lack of transparency and persistent Western-centric biases call for more rigorous audits and equitable practices in dataset curation and prioritizing transparency in data provenance.
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Medium
1d
232
Image Credit: Medium
CART (Classification and Regression Trees)
- The structure of a simple decision tree can be explained as follows
- In a CART structure, the variable at the top of the tree is the most important independent variable
- Decision trees can also be visualized on a two-dimensional axis
- For regression problems, the CART algorithm optimizes splits to minimize the Cost Function
- Hyperparameters in CART (Classification and Regression Trees) significantly impact the model’s performance
- Max_depth, min_samples_split, min_samples_leaf, max_features, criterion, and random_state are some of the hyperparameters of CART
- Hyperparameter optimization can be performed using tools like GridSearchCV
- CART (Classification and Regression Trees) systematically splits datasets into subgroups
- This process is valuable in data analysis and modeling as it provides clarity and predictability
- The success of CART lies in its cost function, which minimizes prediction errors by determining the correct split points
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Medium
1d
129
Image Credit: Medium
From Blank Canvas to Finished Product: How LLMs Transform Software Development — and the Golden…
- LLMs transform software development by automating routine work and fast prototyping through automated project structure, documentation, logging, and test skeletons.
- The AI-generated boilerplate code can be created for REST APIs, database integrations, or CLI tools, and the system progresses from the first pull request to deployment.
- To ensure quality automation, guidelines such as the use of user stories or acceptance criteria, and maintaining an architecture that is already defined by base templates for Clean Architecture or hexagonal structures must be followed.
- The bedrock of TDD (test-driven development) is essential to provide a safety net that evaluates the amount of generated code, further leading to secure and efficient implementation solutions.
- Manual code reviews, security scans, and linters also play a part within the LLM system to provide feedback and maintain a human touch which allows the identification of potential mistakes and provides clearer visibility for each project.
- Maintaining a good documentation system and logging interface is critical for project transparency and maintaining knowledge for future projects.
- Automated continuous learning and feedback must also be implemented to ensure better outcomes and refined solutions for subsequent projects.
- Following well-defined phases and structures with LLMs means less time setting up groundworks and more time focusing on the creative and problem-solving components of your application.
- Overall LLMs provide the potential for streamlined processes, faster iterations, and better-defined outcomes, all whilst remaining under your control as the architect behind the vision.
- Use LLMs in software development to build better, faster, and smarter. Happy developing!
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Medium
1d
302
Image Credit: Medium
The Rise of Artificial Intelligence: Transforming the Future
- AI refers to systems or machines that simulate human intelligence to perform tasks and iteratively improve based on data.
- Transformative applications of AI include healthcare, finance, education, and creative industries.
- Ethical and social challenges associated with AI include bias in decision-making, privacy concerns, and job displacement.
- The future of AI lies in creating intelligent systems that are ethical, transparent, and aligned with human values.
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Marktechpost
1d
15
Salesforce AI Research Introduces AGUVIS: A Unified Pure Vision Framework Transforming Autonomous GUI Interaction Across Platforms
- Salesforce Research, in collaboration with researchers from The University of Hong Kong, has introduced AGUVIS, an autonomous GUI interaction framework transforming workflows across platforms.
- Current GUI automation tools suffer due to the discrepancy between natural language instructions and the visual representations of GUIs.
- Existing tools often depend on closed-source models for reasoning and planning capabilities, leading to information loss.
- AGUVIS utilizes a pure-vision approach, leveraging image-based input, increasing the accuracy of decision-making and reducing token costs.
- The AGUVIS Collection unifies and augments existing datasets with synthetic data to train robust and adaptable models.
- The two-stage process of AGUVIS focuses on grounding and planning, allowing the model to perform single and multi-step tasks effectively.
- AGUVIS demonstrated remarkable results in GUI grounding benchmarks, with accuracy rates of 88.3%, 85.7%, and 81.8% on web, mobile, and desktop platforms.
- The system can generalize across platforms and handle platform-specific actions, such as swiping on mobile devices.
- AGUVIS achieved efficient results, reducing USD inference costs by 93% as compared to existing models.
- The vision-based AGUVIS framework provides an efficient and capable solution for autonomous GUI tasks.
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