Aaron Lefohn’s Post

Our neural radiance cache is finally available as a game dev SDK! https://lnkd.in/dR23wSXA If you work on a real-time path tracer, this neural cache can shorten your paths and thereby reduce noise & cost. We've seen efficiency gains of up to 50x in scenes that are super tough for non-neural caches, like the one in the video. It's also not "just" an implementation of the original research paper (https://lnkd.in/g5SZgfVu) but also comes with improvements, like Instant NGP's hash encoding (https://lnkd.in/dQC2f9Yp bottom of page) and turbo-charged neural networks.

Synthetic data is a form of artificially generated information that closely emulates the characteristics of real data. Essentially, it is fabricated data designed to match the statistical properties found in authentic datasets. The process of creating synthetic data involves training machine learning models on extensive sets of real data to identify patterns and relationships. Various models, including generative adversarial networks (GANs) and recurrent neural networks, can be employed for this purpose. Generative adversarial networks operate with two opposing models – a generator and a discriminator. The generator produces synthetic samples, and the discriminator assesses them for authenticity. Through an iterative process, the generator refines its output, generating increasingly realistic synthetic examples capable of deceiving the discriminator. More details about #syntheticdata and #digitaltwins here: https://lnkd.in/dPaPaa_a

Synthetic data is a form of artificially generated information that closely emulates the characteristics of real data. Essentially, it is fabricated data designed to match the statistical properties found in authentic datasets. The process of creating synthetic data involves training machine learning models on extensive sets of real data to identify patterns and relationships. Various models, including generative adversarial networks (GANs) and recurrent neural networks, can be employed for this purpose. Generative adversarial networks operate with two opposing models – a generator and a discriminator. The generator produces synthetic samples, and the discriminator assesses them for authenticity. Through an iterative process, the generator refines its output, generating increasingly realistic synthetic examples capable of deceiving the discriminator. More details about #syntheticdata and #digitaltwins here: https://lnkd.in/dyvhqj7z

Synthetic data is a form of artificially generated information that closely emulates the characteristics of real data. Essentially, it is fabricated data designed to match the statistical properties found in authentic datasets. The process of creating synthetic data involves training machine learning models on extensive sets of real data to identify patterns and relationships. Various models, including generative adversarial networks (GANs) and recurrent neural networks, can be employed for this purpose. Generative adversarial networks operate with two opposing models – a generator and a discriminator. The generator produces synthetic samples, and the discriminator assesses them for authenticity. Through an iterative process, the generator refines its output, generating increasingly realistic synthetic examples capable of deceiving the discriminator. More details about #syntheticdata and #digitaltwins here: https://lnkd.in/dPaPaa_a

Physical Neural Network Can Be Trained Like a Digital One: Here’s an unusual concept: a computer-guided mechanical neural network (video, embedded below.) Why would one want a mechanical neural network? It’s essentially a tool to explore what it would take …read more

What are GANs? Generative Adversarial Networks were first conceptualized by Ian J. Goodfellow in his paper in 2014. While most common neural networks perform tasks that involve predicting the probability of the test sample belonging to the probability distribution of a class, generative models perform quite the opposite. In layman's terms, the main goal of GANs is to recreate the probability distribution of the original data as best as they can. GANs comprise two separate models (Generator and Discriminator) which work in an adversarial environment. A discriminator is like any other neural network which performs binary classification, and its main goal is to predict whether the test sample is fake (generator models' output) or real. The main goal of the generator is to fool the discriminator by attaining a closer probability distribution function to the original data. At the end of this minimax optimization of a modified binary cross-entropy function (modified for multiple instances instead of a single instance), the GAN can sample new unseen data that fits into the generated probability distribution.

🔍 Unlocking the Magic of Transformer Models 🔮 Ever wondered how transformer models work their magic? Let's break it down: Encoder: The encoder is like the brain of the transformer model. It consists of a stack of self-attention layers. Here's how it operates: Takes a sequence of vectors as input. Utilizes self-attention to compute a score for each pair of words in the input sequence. Produces a new sequence of vectors, capturing important contextual information. Decoder: The decoder complements the encoder's function, responsible for generating output sequences: Comprises self-attention layers and a recurrent neural network (RNN). The self-attention layers, akin to those in the encoder, refine the input for the RNN. The RNN processes the output of self-attention layers, generating a sequence of output tokens, i.e., the words in the output sentence. Attention Mechanism: The heart of the transformer model lies in its attention mechanism: Enables learning of long-range dependencies between words in a sentence. Focuses on relevant words in the input sentence while decoding output tokens, enhancing contextual understanding and coherence. Curious to delve deeper into transformer models? 🤔 Dive into our comprehensive article: Read Now Unravel the mysteries of #transformermodels and empower your understanding of cutting-edge AI technology! 💡🚀 #encoder #decoder #AttentionMechanism #AI #NLP #DeepLearning #MachineLearning

𝐇𝐨𝐰 𝐚 𝐭𝐫𝐚𝐧𝐬𝐟𝐞𝐫 𝐦𝐨𝐝𝐞𝐥 𝐰𝐨𝐫𝐤𝐬 The #encoder consists of a stack of self-attention layers. Each self-attention layer takes a sequence of vectors as input and produces a new sequence of vectors. The self-attention layer works by first computing a score for each pair of words in the input sequence. The #decoder consists of a stack of self-attention layers and a recurrent neural network (RNN). The self-attention layers work the same way as in the encoder. The RNN takes the output of the self-attention layers as input and produces a sequence of output tokens. The output tokens are the words in the output sentence. The #AttentionMechanism is what allows the transformer model to learn long-range dependencies between words in a sentence. The attention mechanism works by focusing on the most relevant words in the input sentence when decoding the output tokens. Want to learn more? Read this detailed article on #transformermodels : https://hubs.la/Q02pFH4w0

#AI Lots of people talking about KAN as replacement for MLP. I see KAN is a great replacement for symbolic regression and for solving specific problems such as PDE problems. I definitely see it could help in better reinforcement learning as you can manipulate the edge without affecting global weights. It's not a drop in replacement, you need to build your network architecture to solve each problem individually. Where as today's neural networks you can just throw your problem at it - MotherNet, etc. I like learnable activation functions at the edges - really cool. It has great interpretability and less parameters needed. The theoretical and practical implications of KANs, especially in how they might contribute to the discovery of mathematical and physical laws alongside human collaborators, underline a potentially transformative tool in scientific research. I think it is a great replacement tools like Eureka - symbolic regression tool. https://lnkd.in/d4S9gr4d