Sergei Burkov’s Post

⚡️ Just had an incredible deep dive into Kolmogorov-Arnold Networks (KANs) – a transformative approach to function approximation in machine learning inspired by the Kolmogorov-Arnold theorem! KANs leverage the Kolmogorov-Arnold representation theorem, which states that any continuous function can be represented as a sum of one-dimensional functions. This opens up new avenues for simplifying complex, high-dimensional functions into compositions of simpler ones. The result? Neural networks that are often more efficient, interpretable, and generalizable than conventional architectures. 🔹 Why Kolmogorov-Arnold Networks? Dimensionality Reduction: They break down high-dimensional problems into a series of one-dimensional mappings, making complex functions easier to manage. Model Interpretability: Since the functions are decomposed, the network structure lends itself to easier interpretation – we see more of the “why” behind the model. Computational Efficiency: By focusing on one-dimensional mappings, KANs can often run faster and be more memory-efficient. The possibilities for KANs are especially exciting in fields like signal processing, anomaly detection, and computational physics, where function complexity is high and interpretability matters. This approach isn’t just about better performance – it’s about reshaping the way we think about neural networks altogether. Here is the paper, if anyone is interested: https://lnkd.in/eZJ8s-qQ #MachineLearning #KolmogorovArnoldNetworks #AI #NeuralNetworks

https://lnkd.in/gxTSTNeu " The fundamental category where supervised learning takes place is the composite of the two constructions in the previous sections. As noted in the previous section, for a Cartesian category C, Lens(C) is monoidal, and so we can form the the category Para(Lens(C)), which we shall call the category of parametric lenses of C. The definition of this category follows automatically from definitions of Para and Lens(C):" Professor  Jarek Duda, Symbolica AI, What is the fundamental category where HCRNN takes place? HCRNN transformers? HCRNN STATE SPACE MODELS? Their hybrids? What's about the biological neuron? Petar Veličković

I applied Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) layers to forecast Tesla’s closing stock prices, achieving strong results with a Mean Squared Error (MSE) of 0.000518 and an R-squared score of 0.991. The model successfully captures both short-term trends and long-term dependencies in the stock data, thanks to LSTM’s ability to retain relevant information over time. https://lnkd.in/d4iY8kau

**Exploring the Power of Neural Networks: My Journey with Green Code** I recently watched a fascinating video on the Green Code channel that left me incredibly inspired. The video detailed the process of creating a custom neural network, and the data presented was filled with valuable insights. It was an incredible demonstration of the potential neural networks have to transform numerous industries. https://lnkd.in/drWvBytn #ArtificialIntelligence #NeuralNetworks #Innovation #Technology #MachineLearning

📖 Day 35 of #100DaysOfCode challenge: Perceptron ⭐ What is a Perceptron ? ⭐ It is the building block of a neural network. The simplest type of artificial neural network. Used for binary classification [classify inputs into one of two classes] 🔵 Components of a Perceptron: 🔸 Inputs: The perceptron receives one or more input values. 🔸 Weights: Each input has a corresponding weight 🔸 Bias: a bias term added when calculating the weighted sum ❗ The weighted sum is the multiplication of each input value with a corresponding weight, and then adding the bias term. 🔸 Activation Function: Weighted sum is passed through an activation function which determines the output. ❗ This output is then compared to a ▪️ threshold value ▪️ . If the output is greater or equal to the threshold, the perceptron outputs 1 (or "true") otherwise it outputs a 0 (or "false")

What are xLSTMs(Extended Long Short-term Memory Networks)? The Extended Long Short-term Memory Network is an enhanced variant of the Long Short-term Memory Network. It comes with improved gating mechanisms, memory utilization, and computational efficiency. Sepp Hochreiter and his team at NXAI have developed it. "xLSTM represents more than just a technological breakthrough; it is a step towards a future where the efficiency, accuracy, and understanding of language processing can meet and exceed human capabilities." — Sepp Hochreiter Recurrent neural networks(RNNs) struggled with the problem of vanishing gradients. In RNNs, the gradient gets to zero if we stack too many blocks together. To tackle this problem, LSTMs introduced cell states and gating mechanisms. However, LSTMs struggled with scalability and efficiency when dealing with huge datasets or models. LSTMs were introduced in 1997. After 20 years in 2017, the transformer architecture with parallelizable self-attention at its core marked the dawn of a new era. Key improvements in xLSTMs: - Exponential Gating Mechanism: uses exponential gating instead of sigmoid for dynamic information flow, improving memory control for complex sequences. - Advanced Memory Structures: combines sLSTM's and mLSTM's advanced memory, enabling improved parallel processing and efficient data management for large-scale tasks. Here, sLSTM comes with a scalar memory, a scalar update, and new memory mixing. And mLSTM has a fully parallelizable matrix memory and a covariance update rule. - Normalization and Stabilization: uses advanced normalization and stabilization for model stability, despite exponential gates' dynamic range. - Residual Connections: enhances gradient flow in deeper networks by integrating residual blocks with LSTM units. This is a short post about xLSTMs. If you want to learn more, check out these links: https://lnkd.in/gk48tt4V https://lnkd.in/guyrsRi7 https://lnkd.in/g7VA5cp2 https://lnkd.in/gJuiA2ub #artificialintelligence #deeplearning #machinelearning #lstms #xlstms #machineintelligence #transformers #rnns #genai #generativeai #llms #innovation #research #airesearch #researchanddevelopment

Excited to share my latest article on Recurrent Neural Networks (RNNs)! 🧠🔁 In this piece, I dive deep into the fascinating world of RNNs, exploring: • The unique architecture that allows RNNs to process sequential data • The forward and backward pass mechanisms and mathematics behind them • Challenges like vanishing and exploding gradients Whether you're a machine learning enthusiast or a seasoned data scientist, this article offers valuable insights into one of the most powerful tools in deep learning. I'd love to hear your thoughts! Have you worked with RNNs? What has been your experience?#MachineLearning #DeepLearning #RNN

🚀 Aaren - Merging the Best of RNNs and Transformers! 🤖✨ A groundbreaking approach to sequence modeling has been introduced by integrating the best of both Recurrent Neural Networks (RNNs) and Transformer models. Meet Aaren, an attention-based module that trains in parallel like Transformers and updates efficiently with new tokens, similar to traditional RNNs. 1️⃣ Aaren Module: Combines the parallel training benefits of Transformers with the efficient update capabilities of traditional RNNs. 2️⃣ Efficiency: Requires only constant memory for inferences, making it highly suitable for low-resource settings like mobile and embedded devices. 3️⃣ Performance:/ Aaren performs comparably to Transformers across 38 datasets spanning reinforcement learning, event forecasting, time series classification, and time series forecasting tasks. 4️⃣ Attention as RNN: Attention is formulated as a special RNN capable of efficiently computing its many-to-one RNN output, addressing the quadratic computational complexity of Transformers. 5️⃣ Parallel Prefix Scan Algorithm: An efficient method for computing attention’s many-to-many RNN output using the parallel prefix scan algorithm, reducing computational overhead. 💡 Impact: - Computational Efficiency: Addresses the high computational expense of Transformers, making high-performance sequence modeling accessible for a wider range of applications. - Broad Applicability: From reinforcement learning to time series forecasting, #Aaren shows promise in enhancing the efficiency and performance of AI models in various domains. Stay tuned for more updates on this innovative AI development! 🚀 📊Paper: https://lnkd.in/ejxBs9ZH #AI #Aaren #MachineLearning #TechInnovation #RNN #Transformers #DataScience #DeepLearning