#cnn #dl #deep_learning #mit #team #code_generation #ml #tensorflow #paper
https://towardsdatascience.com/paper-summary-optimal-dnn-primitive-selection-with-partitioned-boolean-quadratic-programming-84d8ca4cdbfc
https://towardsdatascience.com/paper-summary-optimal-dnn-primitive-selection-with-partitioned-boolean-quadratic-programming-84d8ca4cdbfc
Medium
Faster Deep Learning: Optimal DNN Primitives
This is a paper in a “Seminal Papers in ML” series by MIT Machine Intelligence Community (MIC). MIT MIC aims to educate the community…
#paper #lstm #cnn #rnn #ml #dl #openAI #machine_learning #deep_learning
https://www.youtube.com/watch?v=9b2dxc1QalM
https://www.youtube.com/watch?v=9b2dxc1QalM
YouTube
OpenAI’s Robot Hand Won't Stop Rotating The Rubik’s Cube 👋
❤️ Check out Weights & Biases here and sign up for a free demo: https://www.wandb.com/papers
The mentioned #OpenAI blog post on the gradients and its notebook are available here:
Post: https://www.wandb.com/articles/exploring-gradients
Notebook: https:/…
The mentioned #OpenAI blog post on the gradients and its notebook are available here:
Post: https://www.wandb.com/articles/exploring-gradients
Notebook: https:/…
#bptt #rnn #lstm #deep_learning #facebook_research #team #award #paper #tree #tree_structure #RvNN
https://www.youtube.com/watch?v=7REBftHDQOw
https://www.youtube.com/watch?v=7REBftHDQOw
YouTube
Yikang Shen: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks (ICLR2019)
Speaker: Yikang Shen
Paper: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Authors: Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville
In general, natural language is governed by a tree structure: smaller units (e.g.…
Paper: Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks
Authors: Yikang Shen, Shawn Tan, Alessandro Sordoni, Aaron Courville
In general, natural language is governed by a tree structure: smaller units (e.g.…
#paper #2k20 #contrastive #self_supervised_learning #dl #ml #framework #vision_problem #vision #data_argumentation #nonlinear_transformation #imageNet #SimCLR #resNet50 #alexNet #vs #benchmarks #generative_approch #vs #descriminative_approch #mlp #relu #semi_supervised_learning
https://arxiv.org/abs/2002.05709
https://arxiv.org/abs/2002.05709
#2k17 #paper #cnn #3dcnn #2dcnn #imagenet #vision_problem #vision #ml #dl #history #video #kinetics_dataset
https://arxiv.org/abs/1711.09577
https://arxiv.org/abs/1711.09577
arXiv.org
Can Spatiotemporal 3D CNNs Retrace the History of 2D CNNs and ImageNet?
The purpose of this study is to determine whether current video datasets have sufficient data for training very deep convolutional neural networks (CNNs) with spatio-temporal three-dimensional...
#deepmind #google #team #ai #dl #ml #list #paper #muZero #2k20 #benchmark #alphaZero
https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark
https://deepmind.com/blog/article/Agent57-Outperforming-the-human-Atari-benchmark
Google DeepMind
Agent57: Outperforming the human Atari benchmark
The Atari57 suite of games is a long-standing benchmark to gauge agent performance across a wide range of tasks. We’ve developed Agent57, the first deep reinforcement learning agent to obtain a...
Mastering_Atari,_Go,_Chess_and_Shogi_by_Planning_with.pdf
2.6 MB
Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
#deepmind #google #team #ai #dl #ml #list #paper #muZero #2k20 #benchmarks #alphaZero
https://arxiv.org/pdf/1911.08265.pdf
#deepmind #google #team #ai #dl #ml #list #paper #muZero #2k20 #benchmarks #alphaZero
https://arxiv.org/pdf/1911.08265.pdf
#rl #dl #paper
Communication in Multi-Agent Reinforcement Learning: Intention Sharing by kim et al
TL;DR: In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generates an imagined trajectory by modeling the environment dynamics and other agents' actions. The imagined trajectory is the simulated future trajectory of each agent based on the learned model of the environment dynamics and other agents and represents each agent's future action plan. Each agent compresses this imagined trajectory capturing its future action plan to generate its intention message for communication by applying an attention mechanism to learn the relative importance of the components in the imagined trajectory based on the received message from other agents. Numeral results show that the proposed IS scheme outperforms other communication schemes in multi-agent reinforcement learning.
Paper: https://openreview.net/pdf?id=qpsl2dR9twy
Communication in Multi-Agent Reinforcement Learning: Intention Sharing by kim et al
TL;DR: In this paper, we propose a new communication scheme named Intention Sharing (IS) for multi-agent reinforcement learning in order to enhance the coordination among agents. In the proposed IS scheme, each agent generates an imagined trajectory by modeling the environment dynamics and other agents' actions. The imagined trajectory is the simulated future trajectory of each agent based on the learned model of the environment dynamics and other agents and represents each agent's future action plan. Each agent compresses this imagined trajectory capturing its future action plan to generate its intention message for communication by applying an attention mechanism to learn the relative importance of the components in the imagined trajectory based on the received message from other agents. Numeral results show that the proposed IS scheme outperforms other communication schemes in multi-agent reinforcement learning.
Paper: https://openreview.net/pdf?id=qpsl2dR9twy
#reinforcement_learning #rl #drl #gamedev #rl_policy #paper
https://www.youtube.com/watch?v=Nz-X3cCeXVE&ab_channel=TwoMinutePapers
https://www.ea.com/seed/news/cog2021-curiosity-driven-rl-agents
https://www.youtube.com/watch?v=Nz-X3cCeXVE&ab_channel=TwoMinutePapers
https://www.ea.com/seed/news/cog2021-curiosity-driven-rl-agents
YouTube
This AI Helps Testing The Games Of The Future! 🤖
❤️ Check out Weights & Biases and sign up for a free demo here: https://wandb.com/papers
❤️ Their mentioned post is available here: https://colab.research.google.com/drive/1gKixa6hNUB8qrn1CfHirOfTEQm0qLCSS
📝 The paper "Improving Playtesting Coverage via…
❤️ Their mentioned post is available here: https://colab.research.google.com/drive/1gKixa6hNUB8qrn1CfHirOfTEQm0qLCSS
📝 The paper "Improving Playtesting Coverage via…
🦾 Test Suites for Validating ML Models & Data
#paper #validation #best_practice #github #example
Github: https://github.com/deepchecks/deepchecks
Example: https://docs.deepchecks.com/en/stable/examples/guides/quickstart_in_5_minutes.html
Docs: https://docs.deepchecks.com/
Paper: https://arxiv.org/abs/2203.08491v1
Blog: https://deepchecks.com/blog/
#paper #validation #best_practice #github #example
Github: https://github.com/deepchecks/deepchecks
Example: https://docs.deepchecks.com/en/stable/examples/guides/quickstart_in_5_minutes.html
Docs: https://docs.deepchecks.com/
Paper: https://arxiv.org/abs/2203.08491v1
Blog: https://deepchecks.com/blog/
GitHub
GitHub - deepchecks/deepchecks: Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open…
Deepchecks: Tests for Continuous Validation of ML Models & Data. Deepchecks is a holistic open-source solution for all of your AI & ML validation needs, enabling to thoroughly test ...
https://www.youtube.com/watch?v=2zW33LfffPc&ab_channel=YannicKilcher
#LLM #gpt #paper #yannic_kilcher #team #openai #team #LMM
https://openai.com/product/gpt-4
https://openai.com/research/gpt-4
https://cdn.openai.com/papers/gpt-4.pdf
#LLM #gpt #paper #yannic_kilcher #team #openai #team #LMM
https://openai.com/product/gpt-4
https://openai.com/research/gpt-4
https://cdn.openai.com/papers/gpt-4.pdf
YouTube
GPT-4 is here! What we know so far (Full Analysis)
#gpt4 #chatgpt #openai
References:
https://openai.com/product/gpt-4
https://openai.com/research/gpt-4
https://cdn.openai.com/papers/gpt-4.pdf
Links:
Homepage: https://ykilcher.com
Merch: https://ykilcher.com/merch
YouTube: https://www.youtube.com/c/yannickilcher…
References:
https://openai.com/product/gpt-4
https://openai.com/research/gpt-4
https://cdn.openai.com/papers/gpt-4.pdf
Links:
Homepage: https://ykilcher.com
Merch: https://ykilcher.com/merch
YouTube: https://www.youtube.com/c/yannickilcher…