Karyna Naminas’ Post

🚀Here is another interesting project ⏭️ Next Word Prediction with LSTM and Early Stopping 📖 I developed a sophisticated web application leveraging an LSTM (Long Short-Term Memory) Recurrent Neural Network to predict the next word in a sequence. Built with Streamlit, the app provides a user-friendly interface where users can input text and receive predictive suggestions. This project showcases my expertise in NLP and deep learning, emphasizing practical implementation and user interaction. GitHub : https://lnkd.in/gzCfRs75 Live Demo : https://lnkd.in/gjj3yPAU #DeepLearning #NLP #MachineLearning #LSTM #AI #Streamlit #DataScience

Exciting AI research alert! 🚀 Researchers from Amazon and Michigan State University have introduced a groundbreaking model aiming to enhance long-term coherence in language models. This innovative approach reduces computational load while maintaining context over extensive text segments. By segmenting text into smaller units, the model ensures accuracy in responses, marking a significant leap in the NLP field. Moreover, the model's error-aware reasoning mechanism allows for adjustments based on detected inaccuracies in reasoning steps. By breaking down inputs into smaller segments, the model maintains coherence over lengthy passages, offering scalable modular adjustments for various language tasks like question-answering and conversational AI. In experiments, the model showcased impressive performance improvements across benchmarks. For example, in the “Tracking Shuffled Objects” dataset, accuracy surged from 56.53% to 61.20%, and in the “Penguins in a Table” dataset, performance increased from 81.34% to 82.19%. This collaborative research effort by Amazon and Michigan State University represents a significant advancement in NLP, addressing key challenges in coherence and computational efficiency. Kudos to the team for pushing the boundaries of AI research! 🌟 Source https://lnkd.in/d4xCgFWw #AI #NLP #Research #Innovation

🌟 **Proud to Announce: My New Fill-Mask Model on Hugging Face!** 🌟 I am thrilled to share that I have developed fill-mask model on Hugging Face! This model is designed to enhance text prediction and elevate NLP tasks to new heights. *Key Features of My Model:* *High Accuracy:* Delivers top-notch performance in predicting masked words, ensuring your text predictions are both reliable and contextually accurate. *Broad Applications:* Perfect for text completion, data augmentation, and creative content generation. *Seamless Integration:*Easily integrates with existing NLP pipelines, making it user-friendly and efficient to deploy. *Diverse Training:* Trained on an extensive and varied dataset, ensuring versatility across different domains and languages. AIMER Society - Artificial Intelligence Medical and Engineering Researchers Society #AI #NLP #MachineLearning #HuggingFace #FillMaskModel #Innovation #TechDevelopment

✨ Final Project: Quantum Machine Learning for NLP Lecture ✨ 🗒 English speakers, I have provided some subtitles for you which you can activate!! I recently completed the module 2 of my Quantum Computing Diploma at the Universidad Anáhuac Mayab university, and I had the awesome opportunity to deliver a lecture on Quantum Machine Learning for Natural Language Processing (NLP) as a final project. 🎓💡 In this lecture, I explored how Quantum Long Short-Term Memory (QLSTM) and Variational Quantum Circuits (VQCs) can enhance traditional NLP methods, comparing them with their classical LSTMs counterparts. 🔑 Key Takeaways: - Quantum-enhanced models showed promise in handling mixed-language texts more efficiently. - QLSTMs demonstrated overall better performance with the same or fewer data compared to classical LSTMs. - This research opens new doors for applying Quantum Machine Learning in NLP, leveraging the best of both classical and quantum computation! #QuantumComputing #MachineLearning #NLP #QLSTM #QuantumMachineLearning #Innovation #AI #FinalProject

Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networks The paper focuses on enhancing sentence representation in NLP by improving semantic separability and interpretability. The authors propose using a flow-based invertible neural network (INN) integrated with a transformer-based language autoencoder to create better-separated latent spaces, resulting in improved sentence disentanglement and controlled generation. The findings of the paper are as follows: 🔹 Improved Disentanglement: The supervised INN model significantly enhances the semantic separability of sentence representations compared to unsupervised INNs and the Optimus model. This results in better cluster separation of different predicate-argument structures and content. 🔹Better Semantic Control: The supervised training strategy leads to improved localized semantic control during sentence generation. The INN model can fix predicates during interpolation, indicating superior semantic disentanglement and generation control. 🔹Enhanced Latent Space Geometry: The supervised INN model provides smoother interpolations and better geometric properties of the latent space, facilitating more efficient traversal and interpolation between sentences. 🔹Effective Data Augmentation: Geometric data augmentation strategies assist in the disentanglement process, resulting in more semantically consistent sentence representations and improved generative control. Overall, the integration of flow-based INNs with transformer-based language autoencoders offers significant improvements in semantic disentanglement, interpretability, and control over sentence generation. Reference : https://lnkd.in/gzhGmc9Q #LLM #GenAI #NLP #datascience #machinelearning #NeuralNetwork

We are thrilled to announce the publication of our latest paper, "Recent advancements and challenges of NLP-based sentiment analysis: A state-of-the-art review," in the prestigious Natural Language Processing Journal, Volume 6, March 2024. This work represents a collaborative effort among a dedicated team of researchers: Jamin Rahman Jim, Md Apon Riaz, Partha Malakar, Mohsin Kabir, Prof. Dr. Kamruddin Nur, and Dr. M. F. Mridha. In our extensive review, we dive deep into the world of sentiment analysis, a crucial aspect of natural language processing that deciphers the emotional tone within textual data. Our exploration covers the broad spectrum of application domains, innovative pre-processing techniques, critical datasets, and evaluation metrics shaping the future of sentiment analysis. Moreover, we dissect the latest advancements in Machine Learning, Deep Learning, Large Language Models, and Pre-trained models, evaluating their strengths and limitations. Our paper also presents an in-depth analysis of experimental results and the challenges currently faced in the field, setting the stage for future research directions. This publication is a testament to our hard work and a valuable resource for anyone looking to understand the intricate landscape of sentiment analysis today. We are excited to share our findings and contribute to the ongoing evolution of NLP technologies. Read our full paper here: https://lnkd.in/dw4kJqNj Your thoughts, feedback, and discussions are highly welcomed and appreciated as we navigate through these fascinating advancements together. #NLP #SentimentAnalysis #NaturalLanguageProcessing #MachineLearning #DeepLearning #AIResearch

🌟 Day 19 of 30 Days of NLP 🌟 Today, let's delve into RMSProp (Root Mean Square Propagation), a powerful optimizer used in training neural networks! 🚀 📢 What is RMSProp? RMSProp is an optimization algorithm designed for neural networks to handle non-stationary objectives and sparse gradients. It adjusts the learning rate adaptively for each parameter. 🔹 Key Characteristics of RMSProp 1. Adaptive Learning Rate: Scales the learning rate by an exponentially decaying average of squared gradients. 2. Variance Reduction: Normalizes the gradient updates by the magnitude of recent gradients, improving stability. 3. Efficiency: Well-suited for deep learning tasks with varying gradients and large datasets. 🔹 Why Use RMSProp? 1. Convergence: Accelerates convergence compared to basic stochastic gradient descent (SGD). 2. Stability: Reduces oscillations and ensures smoother updates during training. 3. Robustness: Handles different learning rates for each parameter, beneficial for non-convex optimization tasks. 🔹 Considerations 1. Learning Rate: Tuning the learning rate (here set to 0.001) can significantly impact model performance. 2. Adaptability: Adjusts learning rates per parameter based on recent gradient magnitudes, beneficial for complex optimization landscapes. Let's continue to unravel the complexities of Natural Language Processing! Stay tuned for more insights and questions as we progress through our 30 Days of NLP journey. 🌍💬 #30DaysOfNLP #NaturalLanguageProcessing #MachineLearning #AI #DeepLearning #NeuralNetworks #RMSProp #Optimization

A Powerful NLP Algorithm . . . . Word2Vec is an algorithm that leverages neural networks to find word associations from a large corpus of text. By training on vast amounts of data, it can identify synonymous words and recommend words for partial sentences. Essentially, Word2Vec transforms words into vectors, allowing for sophisticated analysis of word relationships. How Word2Vec Works: Vector Representation: Each word is represented as a vector based on its relation to features such as gender, age, and royalty. Example: Words like "boy," "girl," and "king" are compared with features to calculate vectors. Cosine Similarity: This metric calculates the distance between word vectors to find similar words. If the cosine similarity (𝑐𝑜𝑠𝜃) is 90°, the distance is large (1), indicating dissimilar words. If the cosine similarity (𝑐𝑜𝑠𝜃) is 0°, the distance is small (0), indicating similar words. Simple Example: Feature: Gender Words: Boy (vector: [0.1, 0.8]), Girl (vector: [0.1, 0.7]) Cosine similarity: High similarity, small distance. #Word2Vec #NLP #MachineLearning #DataScience #ArtificialIntelligence #CosineSimilarity

I am thrilled to share that I have successfully completed an training program in Artificial Intelligence, covering Data Analysis, Machine Learning, Deep Learning, and NLP. This program, powered by SHAI For AI | شاي للذكاء الاصطناعي company, spanned from June 1, 2023, to February 1, 2024. I'm grateful to the entire Shai team for their guidance and support throughout this journey. This experience has significantly enhanced my skills and knowledge in the field of AI. #AI #MachineLearning #DeepLearning #DataAnalysis #NLP #ArtificialIntelligence #SHAI

🚀 Can Large Language Models (LLMs) Generate Novel Research Ideas? 🧠🔬 Exciting new research has just been published that sheds light on a fascinating question: Can LLMs really come up with groundbreaking research ideas? 🤖💡 In a groundbreaking study led by Chenglei Si, Diyi Yang, and Tatsunori Hashimoto, over 100 NLP experts were recruited to test this very hypothesis. The researchers compared ideas generated by LLMs with those from human experts, exploring the potential of AI in the early stages of scientific discovery. 🌟 📊 What Did They Find? Novelty: LLM-generated ideas were deemed more novel than those from human experts, with statistical significance (p < 0.05). This suggests AI can offer fresh perspectives that might not be immediately apparent to human researchers. Feasibility: While LLM ideas were judged as more novel, they were slightly weaker in terms of feasibility. This highlights the current limitations of LLMs in producing actionable, practical research proposals. 🔍 Key Insights: Challenges in Evaluation: The study uncovered issues with LLM self-evaluation and a lack of diversity in the generated ideas. These are crucial areas for improvement in developing effective research agents. Future Directions: The researchers propose an innovative study design to take these AI-generated ideas from paper to practice. By executing these ideas into full projects, they aim to see if their novelty and feasibility translate into meaningful research outcomes. 🌐 Why It Matters: This research opens the door to a new era of AI-driven innovation. As LLMs continue to evolve, their ability to inspire and generate novel research ideas could transform how we approach scientific discovery. Imagine a future where human expertise and AI creativity work hand in hand to tackle the world’s biggest challenges! 🌍 Dive into the full study to explore these exciting findings and see how AI could shape the future of research. 📚🔗 #AI #Research #Innovation #LLMs #MachineLearning #NLP #ScientificDiscovery #FutureOfScience #TechTrends