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14,757,828 results
34.1K subscribers
Flow matching is a more general method than diffusion and serves as the basis for models like Stable Diffusion 3. Paper: ...
95,899 views
1 year ago
Support the channel ❤️ https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join Paper: ...
17,823 views
4 years ago
Publish Fast *Guaranteed*: Apply to work 1:1 with Prof Stuckler: https://www.stucklerconsulting.com/consultation/?el=yt1 Get ...
633,546 views
In this video, we break down Meta AI's DINOv3, the latest advancement in computer vision foundation models. Much like large ...
15,026 views
4 months ago
System Design for SDE-2 and above: https://arpitbhayani.me/masterclass System Design for Beginners: ...
83,122 views
In this video, Prof. Carr (faculty member at the University of Minnesota, Department of Chemistry) is explaining the Algorithm of ...
2,510,687 views
9 years ago
Visual tutorial of how the DINOv2 model functions. I've also streamlined the DinoV2 code repository and integrated a wrapper for ...
16,119 views
2 years ago
Thank you, Bonnie Bees, for making this video possible: https://www.patreon.com/cgpgrey ## Related Videos Powers of Ten: ...
9,271,219 views
Flow matching is a new generative modeling method that combines the advantages of Continuous Normalising Flows (CNFs) and ...
54,811 views
These days, we have LLMs which can do powerful tasks. These tasks can greatly vary from writing poetry, doing mathematics, ...
8,648 views
3 months ago
Inside my school and program, I teach you my system to become an AI engineer or freelancer. Life-time access, personal help by ...
12,361 views
5 months ago
This is a hard paper! Energy-functions are typically a mere afterthought in current machine learning. A core function of the Energy ...
38,213 views
5 years ago
CLIP was introduced in the work "Learning transferable visual models from natural language supervision" by A. Radford et al. at ...
12,247 views
3 years ago
This paper outlines how, under certain circumstances, text embeddings can be used to reconstruct the original embedded text.
41,914 views
In this video, Encord's Machine Learning Lead, Frederik Hvilshøj breaks down DINOv3 — Meta AI's third-generation ...
6,270 views
In this video, we break down the paper Less is More: Recursive Reasoning with Tiny Networks, which introduces the Tiny ...
8,478 views
Paper Explained: Emerging Properties in Self-Supervised Vision Transformers The DINO paper(s) were a breakthrough in ...
13,504 views
ai #openai #technology Paper Title: Learning Transferable Visual Models From Natural Language Supervision CLIP trains on 400 ...
169,621 views
mamba #s4 #ssm OUTLINE: 0:00 - Introduction 0:45 - Transformers vs RNNs vs S4 6:10 - What are state space models? 12:30 ...
168,953 views
jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised ...
85,875 views
ai #research #attention Transformers have huge memory and compute requirements because they construct an Attention matrix, ...
58,584 views
Support the channel ❤️ https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join In this video we walk through the ...
12,109 views
Paper: https://arxiv.org/abs/2508.21038 Abstract: Vector embeddings have been tasked with an ever-increasing set of retrieval ...
34,230 views
In this video we go back to the extremely important Google paper which introduced the Mixture-of-Experts (MoE) layer with ...
11,638 views
mixtral #mistral #chatgpt OUTLINE: 0:00 - Introduction 3:00 - Mixture of Experts 6:00 - Classic Transformer Blocks 11:15 - Expert ...
65,292 views
The viral video explained. How can you see the object behind the paper. How does the mirror know it's there? Part 2 - with more ...
351,673 views
This is a paper breakdown video of the paper: Kolmogorov Arnold Networks, which brilliantly provides an alternative to standard ...
68,184 views
Have you ever wondered why A4 is the way it is? Well Matt Parker has sorted it all out for you. Two A4's side by side make an A3.
525,999 views
12 years ago
![[Paper Analysis] On the Theoretical Limitations of Embedding-Based Retrieval (Warning: Rant)](/api/proxy/image?url=https%3A%2F%2Fi.ytimg.com%2Fvi%2FzKohTkN0Fyk%2Fsddefault.jpg)
