Why Multimodal Machine Learning models do not work. Part 2/2 – The CAUSES
Do you want to know the REASONS for problems in integrating images and text with deep learning? This is the second part of a two-videos series.
The first part of the series: 📺 https://youtu.be/P23EWdiPWDw, where Ms. Coffee Bean talks about the SYMPTOMS.
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📺 Ms. Coffee Bean explains a Multimodal Transformer: https…
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AI Coffee Break - Channel Trailer
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A brief history of the Transformer architecture in NLP
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Our paper at CVPR 2020 - MUL Workshop and ACL 2020 - ALVR Workshop
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The Transformer neural network architecture EXPLAINED. “Attention is all you need”
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Preparing for Virtual Conferences – 7 Tips for recording a good conference talk
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Transformer combining Vision and Language? ViLBERT - NLP meets Computer Vision
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Pre-training of BERT-based Transformer architectures explained – language and vision!
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GPT-3 explained with examples. Possibilities, and implications.
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Adversarial Machine Learning explained! | With examples.
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BERTology meets Biology | Solving biological problems with Transformers
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The ultimate intro to Graph Neural Networks. Maybe.
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Can language models understand? Bender and Koller argument.
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GANs explained | Generative Adversarial Networks video with showcase!
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What nobody tells you about MULTIMODAL Machine Learning! 🙊 THE definition.
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Multimodal Machine Learning models do not work. Here is why. Part 1/2 – The SYMPTOMS
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Why Multimodal Machine Learning models do not work. Part 2/2 – The CAUSES
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An image is worth 16x16 words: ViT | Vision Transformer explained
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AI understanding language!? A roadmap to natural language understanding.
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GPT2 wrote this 1000 subscribers special!
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"What Can We Do to Improve Peer Review in NLP?" 👀
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The curse of dimensionality. Or is it a blessing?
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AI Coffee Break with Letitia Parcalabescu Live Stream
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PCA explained with intuition, a little math and code
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Data-efficient Image Transformers EXPLAINED! Facebook AI's DeiT paper
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OpenAI's DALL-E explained. How GPT-3 creates images from descriptions.
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Leaking training data from GPT-2. How is this possible?
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OpenAI’s CLIP explained! | Examples, links to code and pretrained model
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Transformers can do both images and text. Here is why.
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UMAP explained | The best dimensionality reduction?
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NVIDIA Jarvis (now NVIDIA Riva) meets Ms. Coffee Bean
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Transformer in Transformer: Paper explained and visualized | TNT
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[RANT] Adversarial attack on OpenAI’s CLIP? Are we the fools or the foolers?
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Pattern Exploiting Training explained! | PET, iPET, ADAPET
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Deep Learning for Symbolic Mathematics!? | Paper EXPLAINED
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FNet: Mixing Tokens with Fourier Transforms – Paper Explained
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Are Pre-trained Convolutions Better than Pre-trained Transformers? – Paper Explained
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"Please Commit More Blatant Academic Fraud" – A fellow PhD student's response.
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Scaling Vision Transformers? How much data can a transformer get? #Shorts
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How cross-modal are vision and language models really? 👀 Seeing past words. [Own work]
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Charformer: Fast Character Transformers via Gradient-based Subword Tokenization +Tokenizer explained
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Positional embeddings in transformers EXPLAINED | Demystifying positional encodings.
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Adding vs. concatenating positional embeddings & Learned positional encodings
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Self-Attention with Relative Position Representations – Paper explained
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Saddle points vs. local minima in high dimensional spaces | ❓ #AICoffeeBreakQuiz #Shorts
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What is the model identifiability problem? | Explained in 60 seconds! | ❓ #AICoffeeBreakQuiz #Shorts
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Data leakage during data preparation? | Using AntiPatterns to avoid MLOps Mistakes
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Is today's AI smarter than YOU? #Shorts
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Convolution vs Cross-Correlation. How most CNNs do not compute convolutions. | ❓ #Shorts
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Why do we care about cross-correlations vs convolutions | ❓ #AICoffeeBreakQuiz #Shorts
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The convolution is not shift invariant. | Invariance vs Equivariance | ❓ #AICoffeeBreakQuiz #Shorts
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How to increase the receptive field in CNNs? | #AICoffeeBreakQuiz #Shorts
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What is tokenization and how does it work? Tokenizers explained.
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Foundation Models | On the opportunities and risks of calling pre-trained models “Foundation Models”
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How modern search engines work – Vector databases explained! | Weaviate open-source
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Eyes tell all: How to tell that an AI generated a face?
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Swin Transformer paper animated and explained
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Data BAD | What Will it Take to Fix Benchmarking for NLU?
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SimVLM explained | What the paper doesn’t tell you
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DeepCamp AI