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Convolutional neural nets going to spacetime 🚀 Our new ICML24 paper shows how to build Lorentz-equivariant CNNs/MPNNs for multivector fields on Minkowski spaces. This is useful for particle physics or Navier Stokes / electrodynamics simulations. 🧵1/N

Maurice Weiler

3,895 subscribers

266,639 次观看 • 2 年前 •via X (Twitter)

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🧵 On Open to Debate, No Labels Chief Strategist Ryan Clancy explains what motivates him to support No Labels: “This is not a party moment for me. This is a country moment.” READ our new Interested Parties Memo on how a Unity ticket can win in 2024: WATCH the full video on 12/1 here: John Donvan #OpenToDebate

🧵 On Open to Debate, No Labels Chief Strategist Ryan Clancy explains what motivates him to support No Labels: “This is not a party moment for me. This is a country moment.” READ our new Interested Parties Memo on how a Unity ticket can win in 2024: WATCH the full video on 12/1 here: John Donvan #OpenToDebate

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NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.

NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions paper page: present a novel type of neural fields that uses general radial bases for signal representation. State-of-the-art neural fields typically rely on grid-based representations for storing local neural features and N-dimensional linear kernels for interpolating features at continuous query points. The spatial positions of their neural features are fixed on grid nodes and cannot well adapt to target signals. Our method instead builds upon general radial bases with flexible kernel position and shape, which have higher spatial adaptivity and can more closely fit target signals. To further improve the channel-wise capacity of radial basis functions, we propose to compose them with multi-frequency sinusoid functions. This technique extends a radial basis to multiple Fourier radial bases of different frequency bands without requiring extra parameters, facilitating the representation of details. Moreover, by marrying adaptive radial bases with grid-based ones, our hybrid combination inherits both adaptivity and interpolation smoothness. We carefully designed weighting schemes to let radial bases adapt to different types of signals effectively. Our experiments on 2D image and 3D signed distance field representation demonstrate the higher accuracy and compactness of our method than prior arts. When applied to neural radiance field reconstruction, our method achieves state-of-the-art rendering quality, with small model size and comparable training speed.

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194,523 次观看 • 3 年前

Stumbled upon a fun use case for AI image editing ✨ Redfin now has a "redesign" button on listings that allows you to redecorate any room in a new style. This is going to be really useful for homes that need to be renovated or aren't staged - but it's fun to use anywhere 👇

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🚨 PHYSICISTS JUST SPLIT A SINGLE PHOTON AND IT TURNED INTO AN IMPROBABLE SWARM OF PARTICLES. In a striking experiment, researchers have shown that a photon can be split apart in such a way that it produces a large number of particles, creating what they describe as a “mixture from zero to infinity.” Instead of the usual clean splitting into two photons (as seen in spontaneous parametric down-conversion), this process generated a complex, broad swarm of particles. The result challenges conventional intuition about how photons behave when pushed into extreme nonlinear regimes. Why this matters: • It demonstrates a rare and complex form of photon splitting that was previously very difficult to observe cleanly • Such processes could help simulate high-energy particle physics in table-top experiments • It opens new possibilities for generating exotic quantum states of light • It provides deeper insight into nonlinear quantum electrodynamics (QED) in strong fields The deeper implication: Photons are usually thought of as indivisible quanta of light. But under the right extreme conditions, a single photon can effectively “break apart” into many particles. This isn’t just a curiosity it touches on fundamental questions about the nature of light and matter, and could eventually lead to new tools for quantum technologies and for studying physics that normally requires particle accelerators. We’re seeing light behave in ways that blur the line between a single quantum and a many-particle system. How do you think being able to controllably split photons into swarms of particles could impact quantum optics or fundamental physics research? Follow for more frontier quantum physics and breakthroughs in light-matter interaction.

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11,552 次观看 • 1 年前

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31,558 次观看 • 2 年前

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92,898 次观看 • 2 年前