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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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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
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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
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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
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314
Image Credit: Medium
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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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
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Image Credit: Medium
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
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321
Working with Scaled Exponential Linear Units part2(Machine Learning future)
- Recently, self-normalizing neural networks (SNNs) have been proposed with the intention to avoid batch or weight normalization.
- In this work, a new activation function called scaled exponentially-regularized linear unit (SERLU) is introduced to break the monotonicity property of SELU while still preserving the self-normalizing property.
- SERLU incorporates a bump-shaped function in the region of negative input by regularizing a linear function with a scaled exponential function. This function helps push the output of SERLU towards zero mean statistically.
- Experimental results on MNIST, CIFAR10 and CIFAR100 demonstrate that SERLU-based neural networks consistently provide promising results compared to other activation functions.
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Medium
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Research on Information bottleneck for Machine Learning part17
- Research on Information bottleneck for Machine Learning part17
- Researchers propose DisTIB (Transmitted Information Bottleneck for Disentangled representation learning) to address the challenges of performance drop and complicated optimization in representation compression.
- They employ Bayesian networks with transmitted information to formulate the interaction among input and representations during disentanglement.
- Experimental results demonstrate the appealing efficacy of DisTIB in various downstream tasks and validate its theoretical analyses.
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Medium
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58
Research on Information bottleneck for Machine Learning part14
- Researchers have proposed a novel Knowledge Distillation method called IBKD for distilling large language models into smaller representation models.
- IBKD is motivated by the Information Bottleneck principle and aims to maximize the mutual information between the teacher and student model's representations.
- The method reduces unnecessary information in the student model's representation while preserving important learned information.
- Empirical studies on two downstream tasks show the effectiveness of IBKD in text representation.
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Medium
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Research on Information bottleneck for Machine Learning part13
- Multimodal learning benefits cancer survival prediction, but suffers from intra-modal and inter-modal redundancy issues.
- A new framework, Prototypical Information Bottlenecking and Disentangling (PIBD), is proposed to address these issues.
- PIBD consists of the Prototypical Information Bottleneck (PIB) module for intra-modal redundancy and Prototypical Information Disentanglement (PID) module for inter-modal redundancy.
- Experiments on cancer benchmark datasets demonstrate the superiority of PIBD over other methods.
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Medium
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283
Research on Information bottleneck for Machine Learning part10
- End-to-end (E2E) training is a popular method in deep learning but faces challenges in memory consumption, parallel computing, and brain functionality.
- Alternative methods have been proposed, but none can match the performance of E2E training.
- A study analyzes the information plane dynamics of intermediate representations in E2E training using the Hilbert-Schmidt independence criterion (HSIC).
- The analysis reveals efficient information propagation and layer-role differentiation that follows the information bottleneck principle.
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Medium
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Research on Information bottleneck for Machine Learning part8
- The information bottleneck principle provides an information-theoretic framework for deep multi-view clustering (MVC).
- Existing IB-based deep MVC methods rely on variational approximation and distribution assumption, making it hard and impractical for high-dimensional multi-view spaces.
- A new differentiable information bottleneck (DIB) method is proposed, which provides a deterministic and analytical MVC solution.
- The DIB method directly fits the mutual information of high-dimensional spaces using a normalized kernel Gram matrix, without requiring auxiliary neural estimators.
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Medium
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131
Research on Information bottleneck for Machine Learning part7
- An important use case of next-generation wireless systems is device-edge co-inference, where a semantic task is partitioned between a device and an edge server.
- The device carries out data collection and partial processing of the data, while the remote server completes the given task based on information received from the device.
- A new system solution, termed neuromorphic wireless device-edge co-inference, is introduced.
- The proposed system aims to reduce communication overhead while retaining the most relevant information for the end-to-end semantic task.
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