Audrūnas Gruslys

🚀 Want to employ the power of #PyTorch for your scientific models without a steep learning curve? 👀 Check out our latest blog, where Travis Oliphant discusses how PyTorch's integration with the Array API makes it easy to use with familiar libraries like NumPy and scikit-learn.🔥 https://lnkd.in/dDRkEY3v #Innovation

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This article by Pere Martra explores a structured pruning technique for state-of-the-art models, that uses a GLU architecture, enabling the creation of smaller and more efficient large language models. #Llama #LLM

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🔍 Curious about optimizer performance? Benjamin Marie's latest article delves into a comparative analysis of AdamW-32bit, its 8-bit variant, and paged AdamW optimizers. Discover their effects on memory consumption, learning curves, and training time. #MachineLearning #Programming

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🚀 Control Systems in AI 🚀 Control systems often face challenges with high-dimensional, noisy data. The ACDF algorithm, improves the learning of action-dependent latent states, and ensures more accurate representation and control in complex environments. Paper 🔗 - https://lnkd.in/gi82aUqn 💡 Keen on AI breakthroughs? DM us to explore opportunities!

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A Survey of RAG and RAU: Advancing Natural Language Processing with Retrieval-Augmented Language Models Quick read: https://lnkd.in/gnGqk44W Paper: https://lnkd.in/g8-VftbX #llms #largelanguagemodels #ai

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Combining the power of TensorFlow, NeuralForecast, and zero-shot LLMs, Jinhang Jiang shows how we can generate accurate multi-step quantile forecasts.

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Expand your explainable-AI toolkit by following along Conor O'Sullivan's latest tutorial, where you'll learn how to apply the H-stat with the artemis package and interpret the pairwise, overall, and unnormalized metrics.

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Expand your explainable-AI toolkit by following along Conor O'Sullivan's latest tutorial, where you'll learn how to apply the H-stat with the artemis package and interpret the pairwise, overall, and unnormalized metrics.

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Learn the basics of nonlinear dimensionality reduction, kernel PCA, and multidimensional scaling by following along Biman Chakraborty's clear, hands-on Python tutorial.

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Keys to leverage hidden knowledge relationships in graphs to improve the performance of RAG-based LLMs

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Brain Teaser #11: What mathematical arguments can explain how **Layer Normalization** enhances training stability in large language models (LLMs) and Transformer architectures, particularly in mitigating issues like vanishing and exploding gradients? Answer: Training destabilization in neural networks, particularly due to gradient explosion or vanishing, typically occurs because of the high condition numbers of weight matrices during backpropagation. These large condition numbers lead to unstable gradient scaling, either amplifying gradients excessively or diminishing them, hindering effective learning. How Layer Normalization overcomes and brings stability in training is briefly discussed here: https://lnkd.in/gmWm42tQ #MachineLearning #DeepLearning #Transformers #LargeLanguageModels #LayerNormalization #GradientStability #AIResearch #DataScience #NeuralNetworks #AIInsights

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New research from Meta FAIR: Large Concept Models (LCM) is a fundamentally different paradigm for language modeling that decouples reasoning from language representation inspired by how humans can plan high-level thoughts to communicate. LCM repo on GitHub ➡️ https://go.fb.me/tkhkeq More details on the Large Concept Models research in the full paper ➡️ https://go.fb.me/0vqbjd

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For his debut TDS article, Jeremi Nuer presents a thoughtful walkthrough of a recent paper on model interpretability: "We want to have finer-tuned control over LLMs. Being able to detect when certain features are active, and tune how active they are in the middle of generating output, is an amazing tool to have in our toolbox."

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Exploring In-Context Reinforcement Learning in LLMs with Sparse Autoencoders Read the full article: https://lnkd.in/ePyEX-YC Paper: https://lnkd.in/eeE_ErWt

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