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#SIGGRAPHAsia2024 🔥 Replace your Marching Cubes code with our Occupancy-Based Dual Contouring to reveal the "real" shape from either a signed distance function or an occupancy function. No neural networks involved. Web:

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Minhyuk Sung @ ICML 2026

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14,638 просмотров • 1 год назад •via X (Twitter)

ОбразованиеИгрыНаука и технологии#SIGGRAPHAsia2024
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I've been digging into what Roblox is working on with Terrain. There's an ongoing effort to switch from an occupancy model to a SDF model (signed distance field) In practice voxels aren't stored as a [0 to 1] value anymore, it's a [-1 to 1] value.
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I've been digging into what Roblox is working on with Terrain. There's an ongoing effort to switch from an occupancy model to a SDF model (signed distance field) In practice voxels aren't stored as a [0 to 1] value anymore, it's a [-1 to 1] value.

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🔥 TypeScript Tip 🔥 Here, we build a function that creates zod schemas from your own interfaces, with full autocomplete. The lesson? It's often really hard to get autocomplete when your function _accepts the results of another function_.
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🔥 TypeScript Tip 🔥 Here, we build a function that creates zod schemas from your own interfaces, with full autocomplete. The lesson? It's often really hard to get autocomplete when your function _accepts the results of another function_.

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This video, created by my dear coauthor Mahdi E Kahou for our teaching and papers, shows how overparameterized neural networks produce smooth function approximations even in the context of the Runge phenomenon. Some background. Imagine you want to approximate the Runge function using polynomial interpolation at equally spaced points. It is well known that, despite targeting an infinitely differentiable function, such a polynomial approximation produces oscillatory behavior that worsens with the degree of the polynomial. In other words, higher-degree polynomial approximations might not improve accuracy. Instead, approximate the Runge function with a neural network (here, two layers are just to make the example concrete; nothing fundamental depends on it). As you increase the number of parameters well above the 11 training points (in our example, a two-layer neural network with 128 nodes each), you nicely converge to the target, without wild oscillations. Yes, this has much to do with double descent and benign overparameterization, but the main punchline of this post is that neural networks are really very different types of animals than polynomial approximations. And yes, Chebyshev nodes and splines exist, and in this case, they will prevent the oscillations. But that's not the point. Chebyshev nodes and splines still confront Faber’s theorem, which states that for any system of polynomial interpolation nodes, there exists a continuous function whose sequence of interpolating polynomials diverges as the number of nodes grows to infinity. Faber’s theorem does not apply to neural networks because they are not polynomials. The notebook, if you want to check the details, is here: Stay tuned for more on this 👀
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This video, created by my dear coauthor Mahdi E Kahou for our teaching and papers, shows how overparameterized neural networks produce smooth function approximations even in the context of the Runge phenomenon. Some background. Imagine you want to approximate the Runge function using polynomial interpolation at equally spaced points. It is well known that, despite targeting an infinitely differentiable function, such a polynomial approximation produces oscillatory behavior that worsens with the degree of the polynomial. In other words, higher-degree polynomial approximations might not improve accuracy. Instead, approximate the Runge function with a neural network (here, two layers are just to make the example concrete; nothing fundamental depends on it). As you increase the number of parameters well above the 11 training points (in our example, a two-layer neural network with 128 nodes each), you nicely converge to the target, without wild oscillations. Yes, this has much to do with double descent and benign overparameterization, but the main punchline of this post is that neural networks are really very different types of animals than polynomial approximations. And yes, Chebyshev nodes and splines exist, and in this case, they will prevent the oscillations. But that's not the point. Chebyshev nodes and splines still confront Faber’s theorem, which states that for any system of polynomial interpolation nodes, there exists a continuous function whose sequence of interpolating polynomials diverges as the number of nodes grows to infinity. Faber’s theorem does not apply to neural networks because they are not polynomials. The notebook, if you want to check the details, is here: Stay tuned for more on this 👀

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As we all know, Valve is working on an AI based Anticheat called VACNet however there are already AI based Aimbots ruining the game experience. According to the dev, these aimbots doesn't modify the game files or interfere with the game at all. Here is a working AI based Aimbot in CS2. It works by real-time object detection with neural networks and recognizes the patterns similar to human movements. I am sure with the amount of data Valve has, they will be able to ban these aimbots however it's gonna be a tough battle to fight. What do you think about this? 🤔

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We’re excited to announce that our integration with Patreon is live! Now you can add Patreon as a destination in either Streamlabs Desktop or the Plugin for OBS, and take advantage of your favorite Streamlabs features like multistream, dual output, and more!

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