google-research
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| RHO-Loss | google-research | |
|---|---|---|
| 1 | 96 | |
| 173 | 32,683 | |
| 4.6% | 1.2% | |
| 0.0 | 9.6 | |
| over 1 year ago | 8 days ago | |
| Python | Jupyter Notebook | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
RHO-Loss
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[D] Most important AI Paper´s this year so far in my opinion + Proto AGI speculation at the end
RHO-LOSS - Prioritized Training on Points that are Learnable, Worth Learning, and Not Yet Learnt - Trains Models 18x faster with higher accuracy Paper: https://arxiv.org/abs/2206.07137 Github: https://github.com/OATML/RHO-Loss
google-research
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Jpegli: A New JPEG Coding Library
The change was literally just made: https://github.com/google-research/google-research/commit/4a...
It appears this was in response to Hacker News comments.
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Vector Databases: A Technical Primer [pdf]
There are options such as Google's ScaNN that may let you go farther before needing to consider specialized databases.
https://github.com/google-research/google-research/blob/mast...
- Smerf: Streamable Memory Efficient Radiance Fields
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Shisa 7B: a new JA/EN bilingual model based on Mistral 7B
You could also try some dedicated translation models like https://huggingface.co/facebook/nllb-moe-54b (or https://github.com/google-research/google-research/tree/master/madlad_400 for something smaller) and see how they do.
- Mastering ROUGE Matrix: Your Guide to Large Language Model Evaluation for Summarization with Examples
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Announcing xAI July 12th 2023
Our team is led by Elon Musk, CEO of Tesla and SpaceX. We have previously worked at DeepMind, OpenAI, Google Research, Microsoft Research, Tesla, and the University of Toronto. Collectively we contributed some of the most widely used methods in the field, in particular the Adam optimizer, Batch Normalization, Layer Normalization, and the discovery of adversarial examples. We further introduced innovative techniques and analyses such as Transformer-XL, Autoformalization, the Memorizing Transformer, Batch Size Scaling, and μTransfer. We have worked on and led the development of some of the largest breakthroughs in the field including AlphaStar, AlphaCode, Inception, Minerva, GPT-3.5, and GPT-4.
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OpenAI: AI systems will exceed expert skill level in most domains within the next 10 years!
How about Google Research https://research.google/
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Why Nobody Is Talking About Google muNet ?
Github
Github: https://github.com/google-research/google-research/tree/master/muNet
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Top 10 Best Vector Databases & Libraries
ScaNN (Scalable Nearest Neighbors, Google Research) → A library for efficient vector similarity search, which finds the k nearest vectors to a query vector, as measured by a similarity metric. Vector similarity search is useful for applications such as image search, natural language processing, recommender systems, and anomaly detection.
What are some alternatives?
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
fast-soft-sort - Fast Differentiable Sorting and Ranking
faiss - A library for efficient similarity search and clustering of dense vectors.
ml-agents - The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.
Milvus - A cloud-native vector database, storage for next generation AI applications
struct2depth - Models and examples built with TensorFlow
bootcamp - Dealing with all unstructured data, such as reverse image search, audio search, molecular search, video analysis, question and answer systems, NLP, etc.
rmi - A learned index structure
ML-KWS-for-MCU - Keyword spotting on Arm Cortex-M Microcontrollers
CLIP - CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
torchsort - Fast, differentiable sorting and ranking in PyTorch
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