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Medium
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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
1d
3
Research on Information bottleneck for Machine Learning part15
- Adversarial examples pose a serious threat to deep learning models.
- Existing works lack analysis of adversarial information and its interpretation.
- This paper studies adversarial information as unstructured noise and proposes a new module to regularize it.
- The proposed method improves robust accuracy and is efficient with few additional parameters.
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Medium
1d
197
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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177
Research on Information bottleneck for Machine Learning part12
- Information Bottleneck (IB) is a framework for extracting information from a source variable to a target variable.
- Flexible Variational Information Bottleneck (FVIB) is introduced, enabling efficient training for all values of β.
- FVIB maximizes the objective function for Variational Information Bottleneck (VIB) effectively.
- FVIB outperforms other IB and calibration methods in terms of calibration performance.
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Medium
1d
282
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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309
Research on Information bottleneck for Machine Learning part9
- This paper introduces a new metric learning model VIB-DML (Variational Information Bottleneck Distance Metric Learning) for rating prediction.
- It combines the Variationl Information Bottleneck with metric learning to improve recommendation quality.
- The model limits the mutual information of the latent space feature vector and satisfiy the assumption of Euclidean distance.
- Experimental results show that VIB-DML has excellent generalization ability and reduces prediction error compared to other metric learning models.
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Medium
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23
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
1d
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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Medium
1d
92
Research on Information bottleneck for Machine Learning part6
- Markov state models (MSMs) are used to study dynamics of protein conformational changes in molecular dynamics (MD) simulations.
- This work introduces a continuous embedding approach using state predictive information bottleneck (SPIB) for molecular conformations.
- SPIB combines dimensionality reduction and state space partitioning via a continuous, machine learned basis set.
- SPIB demonstrates state-of-the-art performance in identifying slow dynamical processes and constructing predictive multi-resolution Markovian models.
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Medium
1d
108
Image Credit: Medium
Bayesian Learning in a minute
- Probability of having a disease is 1%, not having disease 99% and Probability of report being accurate is 99% and 1% for not.
- In Bayesian learning, plausibility can be understood as the likelihood or probability of a certain hypothesis given the data.
- Bayesian learning is widely used in various fields, including machine learning, medicine, and finance, for making informed and probabilistic decisions.
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Medium
1d
158
Image Credit: Medium
Foundational Model AI Startups Are Not For The Faint Hearted
- Developing foundational AI models involves high costs and technical challenges.
- Training small genomic models can be extremely expensive, costing $30,000 per TPU week.
- Predictive models are built to forecast training errors and optimize experimental runs, based on mathematical analysis.
- Foundational AI models require a different approach than traditional machine learning projects and can be difficult to explain to investors.
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Marktechpost
1d
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This AI Research from Google DeepMind Explores the Performance Gap between Online and Offline Methods for AI Alignment
- Researchers from Google DeepMind conducted a study to explore the performance gap between online and offline methods for AI alignment.
- The study found that online methods outperform offline methods, suggesting that on-policy sampling is crucial for AI alignment.
- Factors such as offline data coverage and quality contribute to the performance gap.
- The researchers propose further exploration of hybrid approaches combining online and offline methods for improved AI alignment.
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Medium
1d
275
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Observations from Google I/O 2024
- Google showcased the future of applications and how generative AI can be applied to transform work and daily lives.
- Examples of applying generative AI included summarizing emails into action items, automatically processing invoices, and improving productivity.
- Google launched Gemini 1.5 and variations of Gemini, as well as announced Project Astra to compete in the AI model race.
- The future of data science lies in problem framing and solution evaluation, while AI startups should focus on finding niches and solving meaningful problems.
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Medium
2d
330
Image Credit: Medium
How to Make Machine Learning Predictions Step by Step
- Machine learning prediction is a process of using algorithms and statistical models to make predictions or forecasts based on data.
- To make machine learning predictions, you need to gather and preprocess data properly.
- After preprocessing, you need to choose the algorithm that best fits your data and the type of prediction you are trying to make.
- Once your algorithm is selected, you can train your machine learning model by feeding your prepared data into the algorithm to learn the underlying patterns and relationships.
- After the training process, it is important to evaluate the performance of your model on a separate, unseen dataset before deployment.
- If your model's performance is not up to par, you may need to refine data preprocessing steps, algorithm selection, or adjust hyperparameters.
- After training and evaluating your machine learning model, the final step is to deploy it and monitor its performance in the real world.
- Regular monitoring and maintenance will help ensure that your machine learning model remains accurate and up-to-date, providing valuable insights and enabling better decision-making for your organization.
- To demonstrate the steps, the article presents a simple example of making machine learning predictions using Python and scikit-learn library.
- Making accurate machine learning predictions is a crucial skill, and it requires understanding the problem domain, the data, and the end user's needs.
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Medium
2d
249
Image Credit: Medium
Unveiling the Magic of Large Language Models: Your Complete Guide to Understanding LLM
- Large Language Models (LLMs) are AI systems trained on vast amounts of text data to process and generate human language.
- LLMs use algorithms, neural networks, and tokens to understand and produce human-like text.
- Data collection and preprocessing, model architecture, and training algorithms are the three main components of building an LLM.
- LLMs can be used in various fields such as healthcare, customer support, education, and creative writing.
- To learn about LLMs, one must have skills in programming, machine learning, data handling, and NLP.
- Beginners can start by experimenting with simple projects and pre-trained models, while more advanced learners can contribute to open-source projects or participate in hackathons.
- It is crucial to stay updated on the latest research and news in the field of LLMs and NLP to remain competitive.
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