[ML News] New ImageNet SOTA | Uber’s H3 hexagonal coordinate system | New text-image-pair dataset
Yannic provides the latest news in machine learning in this video. Time Stamps: 0:00 – Intro 0:20 – TruthfulQA benchmark shines new light on GPT-3 2:00 – LAION-400M image-text-pair dataset 4:10 – GoogleAI’s EfficientNetV2 and CoAtNet 6:15 – Uber’s H3: A hexagonal coordinate system 7:40 – AWS NeurIPS 2021 DeepRacer Challenge 8:15 – Helpful Libraries […]
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PonderNet: Learning to Ponder
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Frank
- August 24, 2021
- AI
- Andrea Banino
- artificial intelligence
- Arxiv
- broader impact
- Charles Blundell
- Deep Learning
- deep learning classic algorithms
- deep learning recurrent computation
- deep network learning to stop
- DeepMind
- deepmind pondernet
- dynamic computation
- dynamic recurrent network
- explained
- halting probability
- Jan Balaguer
- Machine Learning
- Neural Networks
- Paper
- pondernet
- pondernet explained
- pondernet learning to ponder
Humans don’t spend the same amount of mental effort on all problems equally. Instead, we respond quickly to easy tasks, and we take our time to deliberate hard tasks. DeepMind’s PonderNet attempts to achieve the same by dynamically deciding how many computation steps to allocate to any single input sample. This is done via a […]
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What Your Phone Sends Every 5 Minutes to Apple or Google
Apple’s push into privacy may be mostly talk as a new privacy study analyzed which data smartphones transmit to Apple and Google, even with telemetry options disabled. It turns out, iPhones and Androids not only send a number of identifiers on average every 5 minutes, some network connections even include location and nearby devices. Professor […]
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Why AI is Harder Than We Think
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Frank
- May 3, 2021
- AI
- ai spring
- AI Winter
- alan turing
- are neural networks artificial intelligence
- artificial intelligence
- Arxiv
- can machines be conscious
- can machines think
- Deep Learning
- deep learning artificial intelligence
- deep learning tutorial
- elon musk artificial intelligence
- expert systems
- explained
- introduction to deep learning
- Machine Learning
- marvin minsky
- Neural Networks
- Paper
- Self Driving Cars
- what is deep learning
- why is ai hard
- why is deep learning important
Yannic Kilcher explains how the AI community has gone through regular cycles of AI Springs, where rapid progress gave rise to massive overconfidence, high funding, and overpromise, followed by these promises being unfulfilled, subsequently diving into periods of disenfranchisement and underfunding, called AI Winters. This video he explores a paper which examines the reasons for […]
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Deep Networks Are Kernel Machines
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Frank
- February 18, 2021
- AI
- artificial intelligence
- Arxiv
- data representations
- Deep Learning
- deep neural networks
- explained
- kernel machines
- kernel trick
- learning theory
- Linear Regression
- Machine Learning
- machine learning theory
- math proof
- nearest neighbor
- Neural Networks
- neural networks gradient descent
- Paper
- pedro domingos
- proof
- representation learning
- representations
- representer theorem
- SGD
- stochastic gradient descent
- Support Vector Machine
- SVM
- what is deep learning
Yannic Kilcher explains the paper “Every Model Learned by Gradient Descent Is Approximately a Kernel Machine.” Deep Neural Networks are often said to discover useful representations of the data. However, this paper challenges this prevailing view and suggest that rather than representing the data, deep neural networks store superpositions of the training data in their […]
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NFNets: High-Performance Large-Scale Image Recognition Without Normalization
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Frank
- February 15, 2021
- AI
- artificial intelligence
- Arxiv
- batch norm
- batch normalization
- batchnorm
- best imagenet model
- best neural network
- Deep Learning
- deep learning code
- deep learning tutorial
- Deep Mind
- DeepMind
- distributed training
- explained
- gradient clipping
- imagenet
- JAX
- layer normalization
- Machine Learning
- machine learning explained
- machine learning tutorial
- mean shift
- ml code
- Neural Networks
- nfnet
- nfnets
- nfnets code
- nfresnet
- normalizer-free
- Paper
- weight standardization
Yannic Kilcher explains NFNets in this video.
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SingularityNET – A Decentralized, Open Market and Network for AIs
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Frank
- February 11, 2021
- AGI
- agi token
- AI
- API
- Artificial General Intelligence
- artificial intelligence
- Arxiv
- Ben Goertzel
- benefit
- Blockchain
- cardano
- Deep Learning
- deoldify
- erc20
- Ethereum
- explained
- goertzel
- Governance
- Graph
- hanson
- hanson robotics
- ICO
- inflation
- intelligence
- liquid rank
- liquidrank
- Machine Learning
- matchmaking
- network
- Neural Networks
- offer coin
- offer networks
- offercoin
- offernetworks
- open cog
- opencog
- Paper
- reputation
- reputation system
- Singularity
- singularitynet
- sophia
- tontoni phi
Yannic Kilcher explains this white paper on SingularityNET. Big Tech is currently dominating the pursuit of ever more capable AI. This happens behind closed doors and results in a monopoly of power. SingularityNET is an open, decentralized network where anyone can offer and consume AI services, and where AI agents can interlink with each other […]
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Transformers for Image Recognition at Scale
-
Frank
- October 6, 2020
- AI
- andrej karpathy
- anonymous
- artificial intelligence
- Arxiv
- attention is all you need
- attention mechanism
- beyer
- big transfer
- bit
- CNN
- Convolutional Neural Network
- Data Science
- Deep Learning
- explained
- Google Brain
- google research
- iclr
- iclr 2021
- karpathy
- Machine Learning
- Neural Networks
- Paper
- peer review
- review
- TPU
- tpu v3
- transformer
- transformer computer vision
- transformer images
- under submission
- vaswani
- vision transformer
- visual transformer
- vit
Yannic Kilcher explains why transformers are ruining convolutions. This paper, under review at ICLR, shows that given enough data, a standard Transformer can outperform Convolutional Neural Networks in image recognition tasks, which are classically tasks where CNNs excel. In this Video, I explain the architecture of the Vision Transformer (ViT), the reason why it works […]
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Explaining the Paper: Hopfield Networks is All You Need
-
Frank
- August 10, 2020
- AI
- artificial intelligence
- Arxiv
- attention
- attention is all you need
- Bert
- binary
- continuous
- Deep Learning
- energy function
- error
- explained
- exponental
- gru
- hochreiter
- hopfield
- hopfield network
- key
- lse
- LSTM
- Machine Learning
- metastable
- Neural Networks
- Paper
- pattern
- query
- retrieval
- RNN
- routing
- Schmidhuber
- separation
- store
- transformer
- update rule
- value
Yannic Kilcher explains the paper “Hopfield Networks is All You Need.” Hopfield Networks are one of the classic models of biological memory networks. This paper generalizes modern Hopfield Networks to continuous states and shows that the corresponding update rule is equal to the attention mechanism used in modern Transformers. It further analyzes a pre-trained BERT […]
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Object-Centric Learning with Slot Attention
-
Frank
- July 1, 2020
- AI
- artificial intelligence
- Arxiv
- attention
- attention mechanism
- capsules
- clevr
- CNN
- Convolutional Neural Network
- Deep Learning
- detr
- disentanglement
- embeddings
- encoder
- ethz
- explained
- gru
- LSTM
- Machine Learning
- Neural Networks
- objects
- Paper
- permutation invariant
- render
- routing
- set
- slots
- tetris
- transformer
- vision
- weight sharing
Visual scenes are often comprised of sets of independent objects. Yet, current vision models make no assumptions about the nature of the pictures they look at. Yannic Kilcher explore a paper on object-centric learning. By imposing an objectness prior, this paper a module that is able to recognize permutation-invariant sets of objects from pixels in […]
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