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14,768,380 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: ...
95,916 views
1 year ago
Support the channel ❤️ https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join Paper: ...
17,825 views
4 years ago
Publish Fast *Guaranteed*: Apply to work 1:1 with Prof Stuckler: https://www.stucklerconsulting.com/consultation/?el=yt1 Get ...
633,770 views
In this video, we break down Meta AI's DINOv3, the latest advancement in computer vision foundation models. Much like large ...
15,037 views
4 months ago
System Design for SDE-2 and above: https://arpitbhayani.me/masterclass System Design for Beginners: ...
83,147 views
Thank you, Bonnie Bees, for making this video possible: https://www.patreon.com/cgpgrey ## Related Videos Powers of Ten: ...
9,273,450 views
Flow matching is a new generative modeling method that combines the advantages of Continuous Normalising Flows (CNFs) and ...
54,854 views
Mamba is an exciting LLM architecture that, when used with Transformers, might introduce new capabilities we haven't seen ...
22,144 views
10 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 ...
12,369 views
5 months ago
Visual tutorial of how the DINOv2 model functions. I've also streamlined the DinoV2 code repository and integrated a wrapper for ...
16,121 views
2 years ago
Swin Transformer paper explained, visualized, and animated by Ms. Coffee Bean. Find out what the Swin Transformer proposes ...
91,557 views
CLIP was introduced in the work "Learning transferable visual models from natural language supervision" by A. Radford et al. at ...
12,250 views
3 years ago
In this Big Technology Podcast clip, Meta Chief AI Scientist Yann LeCun explains why bigger models and more data alone can't ...
444,271 views
8 months ago
This paper outlines how, under certain circumstances, text embeddings can be used to reconstruct the original embedded text.
41,915 views
Sappi is a global company focused on providing chemical cellulose, paper-pulp and paper based solutions to its direct and ...
4,164,172 views
13 years ago
In this video, we break down the paper Less is More: Recursive Reasoning with Tiny Networks, which introduces the Tiny ...
8,488 views
3 months ago
Paper Explained: Emerging Properties in Self-Supervised Vision Transformers The DINO paper(s) were a breakthrough in ...
13,512 views
ai #openai #technology Paper Title: Learning Transferable Visual Models From Natural Language Supervision CLIP trains on 400 ...
169,632 views
5 years ago
jepa #ai #machinelearning Yann LeCun's position paper on a path towards machine intelligence combines Self-Supervised ...
85,899 views
mamba #s4 #ssm OUTLINE: 0:00 - Introduction 0:45 - Transformers vs RNNs vs S4 6:10 - What are state space models? 12:30 ...
168,973 views
Support the channel ❤️ https://www.youtube.com/channel/UCkzW5JSFwvKRjXABI-UTAkQ/join In this video we walk through the ...
12,109 views
ai #research #attention Transformers have huge memory and compute requirements because they construct an Attention matrix, ...
58,585 views
Paper: https://arxiv.org/abs/2508.21038 Abstract: Vector embeddings have been tasked with an ever-increasing set of retrieval ...
34,237 views
This is a paper breakdown video of the paper: Kolmogorov Arnold Networks, which brilliantly provides an alternative to standard ...
68,196 views
Get better at MATH with Brilliant at https://brilliant.org/TreforBazett to get started for free and to get 20% off an annual premium ...
53,348 views
7 months ago
In this video, Prof. Carr (faculty member at the University of Minnesota, Department of Chemistry) is explaining the Algorithm of ...
2,510,945 views
9 years ago
mixtral #mistral #chatgpt OUTLINE: 0:00 - Introduction 3:00 - Mixture of Experts 6:00 - Classic Transformer Blocks 11:15 - Expert ...
65,296 views
Long videos are a nightmare for language models—too many tokens to handle, plus many tokens are redundant, slow inference, ...
6,151 views
![[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)
