NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean

AI Coffee Break with Letitia ยท Beginner ยท๐Ÿ“ฐ AI News & Updates ยท5y ago
Ms. Coffee Bean meets NVIDIA Jarvis (recently renamed to Nvidia Riva). This is how it went. โžก๏ธ AI Coffee Break Merch! ๐Ÿ›๏ธ https://aicoffeebreak.creator-spring.com/ โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€ ๐Ÿ”ฅ Optionally, pay us a coffee to boost our Coffee Bean production! โ˜• Patreon: https://www.patreon.com/AICoffeeBreak Ko-fi: https://ko-fi.com/aicoffeebreak โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€โ–€ ๐Ÿ”— Link to NVIDIA Jarvis: https://nvda.ws/3afJXJW NVIDIAโ€™s GTC is happening from April 12-16 and will share the latest in AI, HPC, healthcare, game development, networking and more. GTC registration is free. To learn more, โ€ฆ
Watch on YouTube โ†— (saves to browser)

Playlist

Uploads from AI Coffee Break with Letitia ยท AI Coffee Break with Letitia ยท 32 of 60

1 AI Coffee Break - Channel Trailer
AI Coffee Break - Channel Trailer
AI Coffee Break with Letitia
2 How to check if a neural network has learned a specific phenomenon?
How to check if a neural network has learned a specific phenomenon?
AI Coffee Break with Letitia
3 A brief history of the Transformer architecture in NLP
A brief history of the Transformer architecture in NLP
AI Coffee Break with Letitia
4 Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
AI Coffee Break with Letitia
5 The Transformer neural network architecture EXPLAINED. โ€œAttention is all you needโ€
The Transformer neural network architecture EXPLAINED. โ€œAttention is all you needโ€
AI Coffee Break with Letitia
6 Preparing for Virtual Conferences โ€“ 7 Tips for recording a good conference talk
Preparing for Virtual Conferences โ€“ 7 Tips for recording a good conference talk
AI Coffee Break with Letitia
7 Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
AI Coffee Break with Letitia
8 Pre-training of BERT-based Transformer architectures explained โ€“ language and vision!
Pre-training of BERT-based Transformer architectures explained โ€“ language and vision!
AI Coffee Break with Letitia
9 GPT-3 explained with examples. Possibilities, and implications.
GPT-3 explained with examples. Possibilities, and implications.
AI Coffee Break with Letitia
10 Adversarial Machine Learning explained! | With examples.
Adversarial Machine Learning explained! | With examples.
AI Coffee Break with Letitia
11 BERTology meets Biology | Solving biological problems with Transformers
BERTology meets Biology | Solving biological problems with Transformers
AI Coffee Break with Letitia
12 Can a neural network tell if an image is mirrored? โ€“ Visual Chirality
Can a neural network tell if an image is mirrored? โ€“ Visual Chirality
AI Coffee Break with Letitia
13 The ultimate intro to Graph Neural Networks. Maybe.
The ultimate intro to Graph Neural Networks. Maybe.
AI Coffee Break with Letitia
14 Can language models understand? Bender and Koller argument.
Can language models understand? Bender and Koller argument.
AI Coffee Break with Letitia
15 GANs explained | Generative Adversarial Networks video with showcase!
GANs explained | Generative Adversarial Networks video with showcase!
AI Coffee Break with Letitia
16 What nobody tells you about MULTIMODAL Machine Learning! ๐Ÿ™Š THE definition.
What nobody tells you about MULTIMODAL Machine Learning! ๐Ÿ™Š THE definition.
AI Coffee Break with Letitia
17 Multimodal Machine Learning models do not work. Here is why. Part 1/2 โ€“ The SYMPTOMS
Multimodal Machine Learning models do not work. Here is why. Part 1/2 โ€“ The SYMPTOMS
AI Coffee Break with Letitia
18 Why Multimodal Machine Learning models do not work. Part 2/2 โ€“ The CAUSES
Why Multimodal Machine Learning models do not work. Part 2/2 โ€“ The CAUSES
AI Coffee Break with Letitia
19 An image is worth 16x16 words: ViT | Vision Transformer explained
An image is worth 16x16 words: ViT | Vision Transformer explained
AI Coffee Break with Letitia
20 AI understanding language!? A roadmap to natural language understanding.
AI understanding language!? A roadmap to natural language understanding.
AI Coffee Break with Letitia
21 GPT2 wrote this 1000 subscribers special!
GPT2 wrote this 1000 subscribers special!
AI Coffee Break with Letitia
22 "What Can We Do to Improve Peer Review in NLP?" ๐Ÿ‘€
"What Can We Do to Improve Peer Review in NLP?" ๐Ÿ‘€
AI Coffee Break with Letitia
23 The curse of dimensionality. Or is it a blessing?
The curse of dimensionality. Or is it a blessing?
AI Coffee Break with Letitia
24 AI Coffee Break with Letitia Parcalabescu Live Stream
AI Coffee Break with Letitia Parcalabescu Live Stream
AI Coffee Break with Letitia
25 PCA explained with intuition, a little math and code
PCA explained with intuition, a little math and code
AI Coffee Break with Letitia
26 Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
AI Coffee Break with Letitia
27 OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
AI Coffee Break with Letitia
28 Leaking training data from GPT-2. How is this possible?
Leaking training data from GPT-2. How is this possible?
AI Coffee Break with Letitia
29 OpenAIโ€™s CLIP explained! | Examples, links to code and pretrained model
OpenAIโ€™s CLIP explained! | Examples, links to code and pretrained model
AI Coffee Break with Letitia
30 Transformers can do both images and text. Here is why.
Transformers can do both images and text. Here is why.
AI Coffee Break with Letitia
31 UMAP explained | The best dimensionality reduction?
UMAP explained | The best dimensionality reduction?
AI Coffee Break with Letitia
โ–ถ NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean
NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean
AI Coffee Break with Letitia
33 Transformer in Transformer: Paper explained and visualized | TNT
Transformer in Transformer: Paper explained and visualized | TNT
AI Coffee Break with Letitia
34 [RANT] Adversarial attack on OpenAIโ€™s CLIP? Are we the fools or the foolers?
[RANT] Adversarial attack on OpenAIโ€™s CLIP? Are we the fools or the foolers?
AI Coffee Break with Letitia
35 Pattern Exploiting Training explained! | PET, iPET, ADAPET
Pattern Exploiting Training explained! | PET, iPET, ADAPET
AI Coffee Break with Letitia
36 Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED
Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED
AI Coffee Break with Letitia
37 FNet: Mixing Tokens with Fourier Transforms โ€“ Paper Explained
FNet: Mixing Tokens with Fourier Transforms โ€“ Paper Explained
AI Coffee Break with Letitia
38 Are Pre-trained Convolutions Better than Pre-trained Transformers? โ€“ Paper Explained
Are Pre-trained Convolutions Better than Pre-trained Transformers? โ€“ Paper Explained
AI Coffee Break with Letitia
39 "Please Commit More Blatant Academic Fraud" โ€“ A fellow PhD student's response.
"Please Commit More Blatant Academic Fraud" โ€“ A fellow PhD student's response.
AI Coffee Break with Letitia
40 Scaling Vision Transformers? How much data can a transformer get? #Shorts
Scaling Vision Transformers? How much data can a transformer get? #Shorts
AI Coffee Break with Letitia
41 How cross-modal are vision and language models really? ๐Ÿ‘€ Seeing past words. [Own work]
How cross-modal are vision and language models really? ๐Ÿ‘€ Seeing past words. [Own work]
AI Coffee Break with Letitia
42 Charformer: Fast Character Transformers via Gradient-based Subword Tokenization +Tokenizer explained
Charformer: Fast Character Transformers via Gradient-based Subword Tokenization +Tokenizer explained
AI Coffee Break with Letitia
43 Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
AI Coffee Break with Letitia
44 Adding vs. concatenating positional embeddings & Learned positional encodings
Adding vs. concatenating positional embeddings & Learned positional encodings
AI Coffee Break with Letitia
45 Self-Attention with Relative Position Representations โ€“ Paper explained
Self-Attention with Relative Position Representations โ€“ Paper explained
AI Coffee Break with Letitia
46 Saddle points vs. local minima in high dimensional spaces | โ“ #AICoffeeBreakQuiz #Shorts
Saddle points vs. local minima in high dimensional spaces | โ“ #AICoffeeBreakQuiz #Shorts
AI Coffee Break with Letitia
47 What is the model identifiability problem? | Explained in 60 seconds! | โ“ #AICoffeeBreakQuiz #Shorts
What is the model identifiability problem? | Explained in 60 seconds! | โ“ #AICoffeeBreakQuiz #Shorts
AI Coffee Break with Letitia
48 Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
AI Coffee Break with Letitia
49 Is today's AI smarter than YOU? #Shorts
Is today's AI smarter than YOU? #Shorts
AI Coffee Break with Letitia
50 Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | โ“ #Shorts
Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | โ“ #Shorts
AI Coffee Break with Letitia
51 Why do we care about cross-correlations vs convolutions | โ“ #AICoffeeBreakQuiz #Shorts
Why do we care about cross-correlations vs convolutions | โ“ #AICoffeeBreakQuiz #Shorts
AI Coffee Break with Letitia
52 The convolution is not shift invariant. | Invariance vs Equivariance | โ“ #AICoffeeBreakQuiz #Shorts
The convolution is not shift invariant. | Invariance vs Equivariance | โ“ #AICoffeeBreakQuiz #Shorts
AI Coffee Break with Letitia
53 How to increase the receptive field in CNNs? | #AICoffeeBreakQuiz #Shorts
How to increase the receptive field in CNNs? | #AICoffeeBreakQuiz #Shorts
AI Coffee Break with Letitia
54 What is tokenization and how does it work? Tokenizers explained.
What is tokenization and how does it work? Tokenizers explained.
AI Coffee Break with Letitia
55 Foundation Models | On the opportunities and risks of calling pre-trained models โ€œFoundation Modelsโ€
Foundation Models | On the opportunities and risks of calling pre-trained models โ€œFoundation Modelsโ€
AI Coffee Break with Letitia
56 How modern search engines work โ€“ Vector databases explained! | Weaviate open-source
How modern search engines work โ€“ Vector databases explained! | Weaviate open-source
AI Coffee Break with Letitia
57 Eyes tell all: How to tell that an AI generated a face?
Eyes tell all: How to tell that an AI generated a face?
AI Coffee Break with Letitia
58 Swin Transformer paper animated and explained
Swin Transformer paper animated and explained
AI Coffee Break with Letitia
59 Data BAD | What Will it Take to Fix Benchmarking for NLU?
Data BAD | What Will it Take to Fix Benchmarking for NLU?
AI Coffee Break with Letitia
60 SimVLM explained | What the paper doesnโ€™t tell you
SimVLM explained | What the paper doesnโ€™t tell you
AI Coffee Break with Letitia
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