Quander

7:09

AGI is not coming!

more_vert

Yannic Kilcher check_circle

104K views 9 days ago

37:49

Context Rot: How Increasing Input Tokens Impacts LLM Performance (Paper Analysis)

more_vert

Yannic Kilcher check_circle

25K views 3 weeks ago

47:51

Energy-Based Transformers are Scalable Learners and Thinkers (Paper Review)

more_vert

Yannic Kilcher check_circle

24K views 4 weeks ago

56:26

On the Biology of a Large Language Model (Part 2)

more_vert

Yannic Kilcher check_circle

15K views 3 months ago

54:05

On the Biology of a Large Language Model (Part 1)

more_vert

Yannic Kilcher check_circle

50K views 4 months ago

1:09:00

[GRPO Explained] DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

more_vert

Yannic Kilcher check_circle

157K views 6 months ago

36:15

Byte Latent Transformer: Patches Scale Better Than Tokens (Paper Explained)

more_vert

Yannic Kilcher check_circle

45K views 7 months ago

40:40

Mamba: Linear-Time Sequence Modeling with Selective State Spaces (Paper Explained)

more_vert

Yannic Kilcher check_circle

161K views 1 year ago

33:26

ORPO: Monolithic Preference Optimization without Reference Model (Paper Explained)

more_vert

Yannic Kilcher check_circle

25K views 1 year ago

59:38

JEPA - A Path Towards Autonomous Machine Intelligence (Paper Explained)

more_vert

Yannic Kilcher check_circle

77K views 3 years ago

00:25:42

The Man behind Stable Diffusion

more_vert

Yannic Kilcher check_circle

1 views 3 years ago

00:33:56

NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis (ML Research Paper Explained)

more_vert

Yannic Kilcher check_circle

1 views 4 years ago

,#nerf #neuralrendering #deeplearning View Synthesis is a tricky problem, especially when only given a sparse set of images as an input. NeRF embeds an entire scene into the weights of a feedforward neural network, trained by backpropagation through a differential volume rendering procedure, and achieves state-of-the-art view synthesis. It includes directional dependence and is able to capture fine structural details, as well as reflection effects and transparency. OUTLINE: 0:00 - Intro & Overview 4:50 - View Synthesis Task Description 5:50 - The fundamental difference to classic Deep Learning 7:00 - NeRF Core Concept 15:30 - Training the NeRF from sparse views 20:50 - Radiance Field Volume Rendering 23:20 - Resulting View Dependence 24:00 - Positional Encoding 28:00 - Hierarchical Volume Sampling 30:15 - Experimental Results 33:30 - Comments & Conclusion Paper: https://arxiv.org/abs/2003.08934 Website & Code: https://www.matthewtancik.com/nerf My Video on SIREN: https://youtu.be/Q5g3p9Zwjrk Abstract: We present a method that achieves state-of-the-art results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views. Our algorithm represents a scene using a fully-connected (non-convolutional) deep network, whose input is a single continuous 5D coordinate (spatial location (x,y,z) and viewing direction (θ,ϕ)) and whose output is the volume density and view-dependent emitted radiance at that spatial location. We synthesize views by querying 5D coordinates along camera rays and use classic volume rendering techniques to project the output colors and densities into an image. Because volume rendering is naturally differentiable, the only input required to optimize our representation is a set of images with known camera poses. We describe how to effectively optimize neural radiance fields to render photorealistic novel views of scenes with complicated geometry and appearance, and demonstrate results that outperform prior work on neural rendering and view synthesis. View synthesis results are best viewed as videos, so we urge readers to view our supplementary video for convincing comparisons. Authors: Ben Mildenhall, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, Ren Ng Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ BiliBili: https://space.bilibili.com/1824646584 If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n