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Topographic VAEs learn Equivariant Capsules (Machine Learning Research Paper Explained)
#tvae #topographic #equivariant
Variational Autoencoders model the latent space as a set of independent Gaussian random variables, which the decoder maps to a data distribution. However, this independence is not always desired, for example when dealing with video sequences, we know that successive frames are heavily correlated. Thus, any latent space dealing with such data should reflect this in its structure. Topographic VAEs are a framework for defining correlation structures among the latent variables and induce equivariance within the resulting model. This paper shows how such correlation structures can be built by correctly arranging higher-level variables, which are themselves independent Gaussians.
OUTLINE:
0:00 - Intro
1:40 - Architecture Overview
6:30 - Comparison to regular VAEs
8:35 - Generative Mechanism Formulation
11:45 - Non-Gaussian Latent Space
17:30 - Topographic Product of Student-t
21:15 - Introducing Temporal Coherence
24:50 - Topographic VAE
27:50 - Experimental Results
31:15 - Conclusion & Comments
Paper: https://arxiv.org/abs/2109.01394
Code: https://github.com/akandykeller/topographicvae
Abstract:
In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristics such as digit class, width, and style on MNIST. Furthermore, through topographic organization over time (i.e. temporal coherence), we demonstrate how predefined latent space transformation operators can be encouraged for observed transformed input sequences -- a primitive form of unsupervised learned equivariance. We demonstrate that this model successfully learns sets of approximately equivariant features (i.e. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. Finally, we demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.
Authors: T. Anderson Keller, Max Welling
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https://www.youtube.com/watch?v=pBau7umFhjQ
Variational Autoencoders model the latent space as a set of independent Gaussian random variables, which the decoder maps to a data distribution. However, this independence is not always desired, for example when dealing with video sequences, we know that successive frames are heavily correlated. Thus, any latent space dealing with such data should reflect this in its structure. Topographic VAEs are a framework for defining correlation structures among the latent variables and induce equivariance within the resulting model. This paper shows how such correlation structures can be built by correctly arranging higher-level variables, which are themselves independent Gaussians.
OUTLINE:
0:00 - Intro
1:40 - Architecture Overview
6:30 - Comparison to regular VAEs
8:35 - Generative Mechanism Formulation
11:45 - Non-Gaussian Latent Space
17:30 - Topographic Product of Student-t
21:15 - Introducing Temporal Coherence
24:50 - Topographic VAE
27:50 - Experimental Results
31:15 - Conclusion & Comments
Paper: https://arxiv.org/abs/2109.01394
Code: https://github.com/akandykeller/topographicvae
Abstract:
In this work we seek to bridge the concepts of topographic organization and equivariance in neural networks. To accomplish this, we introduce the Topographic VAE: a novel method for efficiently training deep generative models with topographically organized latent variables. We show that such a model indeed learns to organize its activations according to salient characteristics such as digit class, width, and style on MNIST. Furthermore, through topographic organization over time (i.e. temporal coherence), we demonstrate how predefined latent space transformation operators can be encouraged for observed transformed input sequences -- a primitive form of unsupervised learned equivariance. We demonstrate that this model successfully learns sets of approximately equivariant features (i.e. "capsules") directly from sequences and achieves higher likelihood on correspondingly transforming test sequences. Equivariance is verified quantitatively by measuring the approximate commutativity of the inference network and the sequence transformations. Finally, we demonstrate approximate equivariance to complex transformations, expanding upon the capabilities of existing group equivariant neural networks.
Authors: T. Anderson Keller, Max Welling
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batch-normalization-accelerating-deep
https://arxiv.org/abs/1502.03167
Abstract:
Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization. It also acts as a regularizer, in some cases eliminating the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.9% top-5 validation error (and 4.8% test error), exceeding the accuracy of human raters.
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Sergey Ioffe, Christian Szegedy
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ai-essay-competition-(lab42)
#shorts #ai #lab42
Write an essay that answers the following question:
Which fundamental principles of intelligence must be considered in the successful design of artificial intelligence?
Submit here: https://lab42.global/essay/
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axial-attention-metnet-a-neural-weather
MetNet is a predictive neural network model for weather prediction. It uses axial attention to capture long-range dependencies. Axial attention decomposes attention layers over images into row-attention and column-attention in order to save memory and computation.
https://ai.googleblog.com/2020/03/a-neural-weather-model-for-eight-hour.html
https://arxiv.org/abs/1912.12180
Abstract:
Weather forecasting is a long standing scientific challenge with direct social and economic impact. The task is suitable for deep neural networks due to vast amounts of continuously collected data and a rich spatial and temporal structure that presents long range dependencies. We introduce MetNet, a neural network that forecasts precipitation up to 8 hours into the future at the high spatial resolution of 1 km2 and at the temporal resolution of 2 minutes with a latency in the order of seconds. MetNet takes as input radar and satellite data and forecast lead time and produces a probabilistic precipitation map. The architecture uses axial self-attention to aggregate the global context from a large input patch corresponding to a million square kilometers. We evaluate the performance of MetNet at various precipitation thresholds and find that MetNet outperforms Numerical Weather Prediction at forecasts of up to 7 to 8 hours on the scale of the continental United States.
Authors: Casper Kaae Sønderby, Lasse Espeholt, Jonathan Heek, Mostafa Dehghani, Avital Oliver,Tim Salimans, Shreya Agrawal, Jason Hickey, Nal Kalchbrenner
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processing-megapixel-images-with-deep
Current CNNs have to downsample large images before processing them, which can lose a lot of detail information. This paper proposes attention sampling, which learns to selectively process parts of any large image in full resolution, while discarding uninteresting bits. This leads to enormous gains in speed and memory consumption.
https://arxiv.org/abs/1905.03711
Abstract:
Existing deep architectures cannot operate on very large signals such as megapixel images due to computational and memory constraints. To tackle this limitation, we propose a fully differentiable end-to-end trainable model that samples and processes only a fraction of the full resolution input image. The locations to process are sampled from an attention distribution computed from a low resolution view of the input. We refer to our method as attention sampling and it can process images of several megapixels with a standard single GPU setup. We show that sampling from the attention distribution results in an unbiased estimator of the full model with minimal variance, and we derive an unbiased estimator of the gradient that we use to train our model end-to-end with a normal SGD procedure. This new method is evaluated on three classification tasks, where we show that it allows to reduce computation and memory footprint by an order of magnitude for the same accuracy as classical architectures. We also show the consistency of the sampling that indeed focuses on informative parts of the input images.
Authors: Angelos Katharopoulos, François Fleuret
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Huggingface released its newest library called NLP, which gives you easy access to almost any NLP dataset and metric in one convenient interface. We will combine this with a BERT model from Huggingface's Transformers library to build a sentiment classifier for IMDB.
OUTLINE:
0:00 - Intro
1:30 - Boilerplate
3:20 - PyTorch Lightning Module
9:50 - Load Dataset
12:15 - Tokenization
20:50 - Torch Tensors
25:50 - Data Loader
28:00 - Create BERT Model
32:00 - Implement Validation and Train Step
47:00 - Run & Recap
50:20 - Epilogue
My Code: https://github.com/yk/huggingface-nlp-demo
NLP Library: https://github.com/huggingface/nlp
Tutorial Colab: https://colab.research.google.com/github/huggingface/nlp/blob/master/notebooks/Overview.ipynb
Transformers Library: https://github.com/huggingface/transformers
Pytorch Lightning: https://github.com/PyTorchLightning/pytorch-lightning
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cicero-an-ai-agent-that-negotiates,
#ai #cicero #diplomacy
A team from Meta AI has developed Cicero, an agent that can play the game Diplomacy, in which players have to communicate via chat messages to coordinate and plan into the future.
Paper Title: Human-level play in the game of Diplomacy by combining language models with strategic reasoning
Commented game by human expert: https://www.youtube.com/watch?v=u5192bvUS7k
OUTLINE:
0:00 - Introduction
9:50 - AI in cooperation games
13:50 - Cicero agent overview
25:00 - A controllable dialogue model
36:50 - Dialogue-conditional strategic planning
49:00 - Message filtering
53:45 - Cicero's play against humans
55:15 - More examples & discussion
Homepage: https://ai.facebook.com/research/cicero/
Code: https://github.com/facebookresearch/diplomacy_cicero
Blog: https://ai.facebook.com/blog/cicero-ai-negotiates-persuades-and-cooperates-with-people/
Paper: https://www.science.org/doi/10.1126/science.ade9097
Abstract:
Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.
Authors: Anton Bakhtin, Noam Brown, Emily Dinan, Gabriele Farina, Colin Flaherty, Daniel Fried, Andrew Goff, Jonathan Gray, Hengyuan Hu, Athul Paul Jacob, Mojtaba Komeili, Karthik Konath, Minae Kwon, Adam Lerer, Mike Lewis, Alexander H. Miller, Sasha Mitts, Adithya Renduchintala, Stephen Roller, Dirk Rowe, Weiyan Shi, Joe Spisak, Alexander Wei, David Wu, Hugh Zhang, Markus Zijlstra
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switch-transformers-scaling-to-trillion
#ai #technology #switchtransformer
Scale is the next frontier for AI. Google Brain uses sparsity and hard routing to massively increase a model's parameters, while keeping the FLOPs per forward pass constant. The Switch Transformer compares favorably to its dense counterparts in terms of speed and sample efficiency and breaks the next magic number: One Trillion Parameters.
OUTLINE:
0:00 - Intro & Overview
4:30 - Performance Gains from Scale
8:30 - Switch Transformer Architecture
17:00 - Model-, Data- and Expert-Parallelism
25:30 - Experimental Results
29:00 - Stabilizing Training
32:20 - Distillation into Dense Models
33:30 - Final Comments
Paper: https://arxiv.org/abs/2101.03961
Codebase T5: https://github.com/google-research/text-to-text-transfer-transformer
Abstract:
In deep learning, models typically reuse the same parameters for all inputs. Mixture of Experts (MoE) defies this and instead selects different parameters for each incoming example. The result is a sparsely-activated model -- with outrageous numbers of parameters -- but a constant computational cost. However, despite several notable successes of MoE, widespread adoption has been hindered by complexity, communication costs and training instability -- we address these with the Switch Transformer. We simplify the MoE routing algorithm and design intuitive improved models with reduced communication and computational costs. Our proposed training techniques help wrangle the instabilities and we show large sparse models may be trained, for the first time, with lower precision (bfloat16) formats. We design models based off T5-Base and T5-Large to obtain up to 7x increases in pre-training speed with the same computational resources. These improvements extend into multilingual settings where we measure gains over the mT5-Base version across all 101 languages. Finally, we advance the current scale of language models by pre-training up to trillion parameter models on the "Colossal Clean Crawled Corpus" and achieve a 4x speedup over the T5-XXL model.
Authors: William Fedus, Barret Zoph, Noam Shazeer
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this-ape-does-not-exist!-(ai-creates-new
#nft #gan #ai
Today we build our own AI that can create as many bored apes as we want! Fungibility for everyone!
Try the model here: https://huggingface.co/spaces/ykilcher/apes
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OUTLINE:
0:00 - Introduction
2:05 - Generative Adversarial Networks
3:40 - Scraping Opensea with BrightData
7:55 - Training the GAN
11:35 - Here are the results!
15:20 - Diving deeper into BrightData
References:
Stylegan 3 imagery: https://nvlabs.github.io/stylegan3/
Bored Ape Yacht Club NFT Collection: https://opensea.io/collection/boredapeyachtclub
Better GANFT model: https://medium.com/@nathancooperjones/these-bored-apes-do-not-exist-6bed2c73f02c
Abstract AI-created apes: https://opensea.io/collection/gan-apes-nft
https://mobile.twitter.com/gannft
Another good model: https://twitter.com/cyrilzakka/status/1463944040878071811
StyleGAN2 versions: https://thispersondoesnotexist.com/
https://thissneakerdoesnotexist.com/
https://thischairdoesnotexist.com/
GANs: https://en.wikipedia.org/wiki/Generative_adversarial_network
https://arxiv.org/pdf/1406.2661.pdf
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IS ST
is-stability-turning-into-openai
#stablediffusion #aiart #openai
Stability AI has stepped into some drama recently. They are accused of a hostile takeover of the community-led sub-reddits and Discord servers, of going after an alternative web UI, and of falsely dealing out IP takedown notices.
OUTLINE:
0:00 - Intro
2:40 - Stability takes over community Discord & Reddit
14:50 - AUTOMATIC1111 web UI, stolen or not ?
24:50 - Stable Diffusion 1.5 takedown request
31:20 - Scary: Stability CIO statement on safety & openness
References:
https://finance.yahoo.com/news/stability-ai-startup-behind-stable-170151950.html?guccounter=1
https://analyticsindiamag.com/when-stability-ai-went-rogue-on-reddit-rampage%ef%bf%bc/
https://www.reddit.com/r/StableDiffusion/comments/y12jo3/comment/irvsek2/?utm_source=share&utm_medium=web2x&context=3
https://imgur.com/a/JjpRpmP
https://imgur.com/a/JjpRpmP
https://www.reddit.com/r/StableDiffusion/comments/y19kdh/mod_here_my_side_of_the_story/
https://imgur.com/a/TpTMr0S
https://imgur.com/a/zTae3hz
https://imgur.com/a/QDNA6cG
https://www.reddit.com/r/StableDiffusion/comments/y17xn1/emad_in_discord_right_now/
https://www.reddit.com/r/StableDiffusion/comments/y156op/new_mods_hijacked_this_sub_2_weeks_ago/
https://www.reddit.com/r/StableDiffusion/comments/y1nc7t/rstablediffusion_should_be_independent_and_run_by/
https://github.com/AUTOMATIC1111/stable-diffusion-webui
https://github.com/AUTOMATIC1111/stable-diffusion-webui-feature-showcase
https://www.reddit.com/r/StableDiffusion/comments/y34h2a/comment/isiymmj/?context=3
https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/2509
https://www.reddit.com/r/StableDiffusion/comments/y1uuvj/automatic1111_did_nothing_wrong_some_people_are/is298ix/?context=3
https://www.reddit.com/r/OutOfTheLoop/comments/y22zg6/comment/is1h02a/
https://www.reddit.com/r/StableDiffusion/comments/y1uuvj/automatic1111_did_nothing_wrong_some_people_are/
https://imgur.com/a/Z2QsOEw
https://www.reddit.com/r/StableDiffusion/comments/y0uvps/automatic1111_removed_from_pinned_guide/
https://huggingface.co/runwayml/stable-diffusion-v1-5/discussions/1#6351a36ca9a9ae18220726c7
https://danieljeffries.substack.com/p/why-the-future-of-open-source-ai
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