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Medium
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Latest Research on Ising Models part2(Machine Learning 2024)
- Researchers have conducted a study on the classical Glauber dynamics for sampling from Ising models with sparse random interactions.
- The study focuses on the Viana — Bray spin glass, where the interactions are supported on the Erdős — Rényi random graph G(n,d/n) and randomly assigned ±β.
- The researchers prove that Glauber dynamics mixes in n1+o(1) time with high probability as long as β≤O(1/d−−√).
- The study also extends its results to random graphs drawn according to the 2-community stochastic block model and the inference problem in community detection.
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Medium
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150
New updates on Gibbs sampling part10(Machine Learning future)
- Particle Markov chain Monte Carlo is a viable approach for Bayesian inference in state-space models.
- Particle Gibbs and particle Gibbs with ancestor sampling improve the performance of the underlying Gibbs sampler.
- Marginalizing out one or more parameters yields a non-Markovian model for inference.
- Advances in probabilistic programming have automated the implementation of marginalization.
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Medium
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289
New updates on Gibbs sampling part8(Machine Learning future)
- The combinatorial sequential Monte Carlo (CSMC) is proposed as an efficient method for Bayesian phylogenetic tree inference.
- The particle Gibbs (PG) sampler is combined with CSMC to estimate phylogenetic trees and evolutionary parameters.
- A new CSMC method with an efficient proposal distribution is introduced, improving the mixing of the particle Gibbs sampler.
- The developed CSMC algorithm can sample trees more efficiently and can be parallelized for faster computation.
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Medium
1d
231
New updates on Gibbs sampling part7(Machine Learning future)
- Researchers propose a new idea for Transfer Learning based on Gibbs Sampling.
- Gibbs sampling is used to transfer instances between domains based on a probability distribution.
- They utilize the Restricted Boltzmann Machine (RBM) to represent the data distribution and perform Gibbs sampling.
- The proposed method shows successful enhancement of target classification without requiring target data during model training.
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Medium
1d
50
New updates on Gibbs sampling part6(Machine Learning future)
- We present two novel algorithms for simulated tempering simulations that break detailed balance condition (DBC) but satisfy the skewed detailed balance.
- The irreversible methods are based on Gibbs sampling and focus on breaking DBC at the update scheme of temperature swaps.
- Tests conducted on different systems, including a simple system, the Ising model, and MD simulations on Alanine pentapeptide (ALA5), demonstrate improved sampling efficiency compared to conventional Gibbs sampling and simulated tempering with Metropolis-Hastings (MH) scheme.
- The algorithms are particularly advantageous for simulations of large systems with many temperature ladders and can be easily adapted for other dynamical variables to flatten rugged free energy landscapes.
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Medium
1d
107
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Ridge Regression: Step by step introduction with example
- Ridge regression is a variation of linear regression, specifically designed to address multicollinearity in the dataset.
- Ridge regression introduces a regularization term that penalizes large coefficients, helping to stabilize the model and prevent overfitting.
- The shrinkage penalty in ridge regression refers to the regularization term added to the linear regression equation to prevent overfitting and address multicollinearity.
- To this, a penalty term is added, which is proportional to the square of the magnitude of the coefficients.
- Ridge regression proves valuable in improving the robustness and performance of linear regression models, especially in situations with multicollinearity.
- Scaling predictors matters; before applying ridge regression, predictors are standardized to be on the same scale.
- Ridge regression introduces a regularization parameter, denoted as 𝜆, which controls the extent of shrinkage applied to the regression coefficients.
- As the value of 𝜆 increases, the model’s flexibility in fitting the data diminishes.
- The superiority of ridge regression compared to the method of least squares arises from the inherent trade-off between variance and bias.
- To build a ridge regression model essentially means to find the coefficients, and the intercept term does not get affected by the shrinkage penalty.
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Marktechpost
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204
The Pursuit of the Platonic Representation: AI’s Quest for a Unified Model of Reality
- As AI systems advance, their representations of data seem to converge towards a unified model of reality.
- Researchers propose the Platonic Representation Hypothesis, suggesting that AI models strive to capture a unified representation of the underlying reality.
- This convergence is evident across different architectures, training objectives, and data modalities.
- Scaling models and incorporating diverse data could lead to more accurate representations of reality.
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Medium
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313
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Introduction to What is Artificial intelligence (AI)
- Artificial intelligence (AI) is an ongoing reality affecting our day-to-day routines through independent vehicles, menial helpers, and proposal frameworks.
- AI started with Alan Turing's inquiry, "Can machines think?" This prompted the Turing Test and the resulting improvement of computational machines that could imitate mental capacities.
- Artificial intelligence can be classified into Narrow AI, General AI, and Superintelligent AI.
- Narrow AI succeeds in unambiguous tasks, while General AI has the capacity to understand, learn, and apply its insight extensively and deftly, and Superintelligent AI outperforms human intelligence and capabilities.
- Artificial intelligence benefits include efficiency, healthcare, business, and scientific exploration. It is important to resolve ethical issues, and regulation and cautious oversight are necessary to address concerns about biases, security intrusion, and job displacement.
- Generative AI, Multimodal AI, Edge Computing, Deep Learning, Reinforcement Learning, Geospatial AI, AI in Physical Robotics and Advanced Mechanics, AI for Environment and Natural Monitoring, and Quantum Computing and AI are the latest advancements in AI.
- AI is changing industries such as healthcare, autonomous driving, customer service, entertainment, retail, and marketing.
- The AI industry offers a plethora of career options, including AI Researcher, AI Programming Engineer, Data Scientist, and Robotics Specialist.
- As AI continues to progress, staying updated and prepared is important for utilizing its potential responsibly.
- The future is all AI, and we must adopt and shape it.
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Medium
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262
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Reinforcement Learning in Real-World Applications: A Practical Guide for 2024
- Reinforcement Learning (RL) is a subset of machine learning where agents learn to make decisions by interacting with their environment.
- RL has seen significant advancements and real-world applications in areas such as robotics, gaming, and resource management.
- In this article, we explore the basics of RL and implement a practical project using RL for a real-world problem.
- We develop an RL agent for autonomous navigation in a grid environment, simulating applications like warehouse robotics or autonomous driving.
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Medium
1d
84
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Unlock the Future of Wealth with the Intelligent Cryptocurrency VIP Digital Membership
- Exclusive access to top-tier cryptocurrency expertise
- Comprehensive library of educational resources
- Real-time market alerts for timely investment decisions
- Networking opportunities with like-minded investors
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Medium
1d
3
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How to fine-tune LLMs on custom datasets at Scale using Qwak and CometML
- The LLM Twin course teaches you how to design, train, and deploy a production-ready LLM twin.
- To achieve this, the course covers the full LLM system's creation, starting from data collection to deployment.
- The course has 11 hands-on written lessons, including open-source code you can access on GitHub.
- Lesson 7 is all about building the fine-tuning pipeline and deploying it on Qwak [2] to train the model.
- The lesson covers model selection, PEFT and QLoRA configs, LLM special tokens, and the overall model training process.
- Students will learn about Fine-tuning, PEFT, and QLoRA, among other things.
- The article also introduces Qwak, an ML engineering platform that simplifies the process of building, deploying, and monitoring machine learning models.
- To access the platform, you need to create an account on Qwak, install qwak-sdk, and configure the Qwak workspace.
- The authors then move on to cover the build lifecycle, cost system, and model blueprint.
- Additionally, they provide a system design and build workflow for training the LLM model.
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Medium
1d
108
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Ensuring Fairness in AI: A Practical Guide to Detecting and Mitigating Bias in Machine Learning…
- Ensuring fairness in AI is crucial for ethical AI deployment and maintaining public trust.
- AI systems can perpetuate and amplify biases, leading to unfair outcomes.
- Key principles of AI ethics include transparency, accountability, and fairness.
- The project demonstrates how to detect and mitigate bias in machine learning models using the AIF360 library.
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Medium
1d
65
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New updates on Gibbs sampling part4(Machine Learning future)
- Gibbs sampling is widely used in various fields due to its simplicity and scalability.
- A study focuses on the implementation details of Gibbs sampling for labeled random finite sets filters.
- They propose a multi-simulation sample generation technique and heuristic early termination criteria.
- The benefits of the proposed Gibbs samplers are demonstrated through Monte Carlo simulations.
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Medium
1d
38
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New updates on Gibbs sampling part3(Machine Learning future)
- L1-ball-type priors are a recent generalization of the spike-and-slab priors, providing flexibility in choosing precursor and threshold distributions to specify models under structured sparsity.
- A new data augmentation technique called "anti-correlation Gaussian" is proposed to accelerate posterior computation and improve mixing of Markov chains in block Gibbs sampling algorithm.
- A study explores the threshold at which sampling the Gibbs measure in continuous random energy model becomes algorithmically hard and presents a recursive sampling algorithm based on a renormalized tree for concave covariance functions.
- A method is proposed to sample from the posterior distribution of parameters conditioned on robust and inefficient statistics, leveraging a Gibbs sampler and simulating latent augmented data.
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Medium
1d
243
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Unlocking the Future of Banking with Artificial Intelligence
- AI in banking goes beyond simple automation and aims to enhance every aspect of banking operations.
- Supervised machine learning enables banks to make informed lending decisions, detect fraud, and provide personalized services.
- Unsupervised learning helps in discovering hidden patterns and identifying new customer segments and investment opportunities.
- Generative AI is used for financial product development, stress testing, and advanced chatbots.
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