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Deep Learning News
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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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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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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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
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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
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93
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
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160
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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Medium
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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
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278
Image Credit: Medium
Voice Cloning, Translation & Transcription: Revolutionizing Communication
- Voice cloning is a groundbreaking technology that replicates and recreates human voices with accuracy.
- It has the potential to transform interactions in various applications, from customer service to entertainment.
- Voice cloning combines speech synthesis, NLP, deep learning, and training models.
- OpenVoice and Whisper are cutting-edge platforms that offer voice cloning, transcription, and translation services.
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Medium
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Image Credit: Medium
Intro to Deep Learning
- Deep learning is a subfield of machine learning that involves the use of deep neural networks to model and solve complex problems.
- In a neural network, multiple single neurons are stacked together to form a larger neural network.
- Deep learning has achieved significant success in various fields and is expected to grow further with the availability of more data and powerful computing resources.
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Medium
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11
How Stylometric Analysis works part4(Machine Learning 2024)
- Function word adjacency networks (WANs) are used to study the authorship of plays from the Early Modern English period.
- WANs consist of nodes representing function words and directed edges representing the relative frequency of co-appearance.
- Author profile networks are generated by aggregating the WANs of analyzed plays.
- WANs prove to be accurate for authorship attribution, outperforming other frequency-based methods for Early English plays.
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Medium
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78
How Stylometric Analysis works part3(Machine Learning 2024)
- In this study, Japanese stylometric features of texts generated by GPT-3.5 and GPT-4 were compared to those written by humans.
- Multi-dimensional scaling (MDS) was performed to analyze the distributions of texts based on stylometric features.
- The distributions of GPT-3.5, GPT-4, and human-generated texts were found to be distinct.
- The classification performance using random forest (RF) for Japanese stylometric features showed high accuracy in distinguishing GPT-generated from human-written texts.
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Medium
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How Stylometric Analysis works part2(Machine Learning 2024)
- Large language models (LLMs) like GPT-4, PaLM, and Llama have increased the generation of AI-crafted text.
- Neural authorship attribution is a forensic effort to trace AI-generated text back to its originating LLM.
- The LLM landscape can be divided into proprietary and open-source categories.
- An empirical analysis of LLM writing signatures highlights the contrasts between proprietary and open-source models and scrutinizes variations within each group.
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Gbhackers
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ViperSoftX Malware Uses Deep Learning Model To Execute Commands
- ViperSoftX malware uses Tesseract, an open-source OCR engine, to target infected systems.
- The malware scans extracted text for passwords and cryptocurrency wallet phrases.
- ViperSoftX deploys additional malware strains like Quasar RAT and TesseractStealer.
- The malware exfiltrates image files containing sensitive information to the attacker's server.
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