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OmniGen: Unified Image Generation
Paper: https://arxiv.org/pdf/2409.11340v1.pdf
Code: https://github.com/vectorspacelab/omnigen
Dataset: DreamBooth | MagicBrush
โ
@Machine_learn
Paper: https://arxiv.org/pdf/2409.11340v1.pdf
Code: https://github.com/vectorspacelab/omnigen
Dataset: DreamBooth | MagicBrush
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๐ Advancing biomedical discovery and innovation in the era of big data and artificial intelligence
๐ฅ Perspective Article
๐ Study the paper
โ
@Machine_learn
๐ฅ Perspective Article
๐ Study the paper
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๐ Natural Language Processing Methods for the Study of Protein-Ligand Interactions
๐Publish year: 2024
๐ Study the paper
โ @Machine_learn
๐Publish year: 2024
๐ Study the paper
โ @Machine_learn
Here are some Hyperparameter (HP) tuning & optimization packages you can use in your projects:
- Scikit-Optimize: https://lnkd.in/gbJqdFq9
- Optuna: https://optuna.org/
- Hyperopt: https://lnkd.in/gPSRhW_6
- Ray.tune: https://lnkd.in/gzrDAbHg
- Keras tuner: https://lnkd.in/g_HDHiug
- BayesianOptimization: https://lnkd.in/g8UKEvjc
- Metric Optimization Engine (MOE): https://lnkd.in/g89JGFB2
- Spearmint: https://lnkd.in/gJwG3AwE
- GPyOpt: https://lnkd.in/g4cWEBPz
- SigOpt: https://sigopt.com/
โ
@Machine_learn
- Scikit-Optimize: https://lnkd.in/gbJqdFq9
- Optuna: https://optuna.org/
- Hyperopt: https://lnkd.in/gPSRhW_6
- Ray.tune: https://lnkd.in/gzrDAbHg
- Keras tuner: https://lnkd.in/g_HDHiug
- BayesianOptimization: https://lnkd.in/g8UKEvjc
- Metric Optimization Engine (MOE): https://lnkd.in/g89JGFB2
- Spearmint: https://lnkd.in/gJwG3AwE
- GPyOpt: https://lnkd.in/g4cWEBPz
- SigOpt: https://sigopt.com/
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lnkd.in
LinkedIn
This link will take you to a page thatโs not on LinkedIn
๐ฅช TripoSR (MIT license) is now available on , free for individual use!
๐งฌcode: https://github.com/VAST-AI-Research/TripoSR
๐paper: https://arxiv.org/abs/2403.02151
๐runpod: https://github.com/camenduru/triposr-tost
๐jupyter: https://github.com/camenduru/TripoSR-jupyter
@Machine_learn
๐งฌcode: https://github.com/VAST-AI-Research/TripoSR
๐paper: https://arxiv.org/abs/2403.02151
๐runpod: https://github.com/camenduru/triposr-tost
๐jupyter: https://github.com/camenduru/TripoSR-jupyter
@Machine_learn
GitHub
GitHub - VAST-AI-Research/TripoSR
Contribute to VAST-AI-Research/TripoSR development by creating an account on GitHub.
How to Train Long-Context Language Models (Effectively)
๐ฅ Github: https://github.com/hijkzzz/pymarl2
๐ Paper: https://arxiv.org/abs/2410.02511v1
โ
Dataset: https://paperswithcode.com/dataset/smac
@Machine_learn
@Machine_learn
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WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild
Paper: https://arxiv.org/pdf/2409.12259v1.pdf
Code: https://github.com/rolpotamias/WiLoR
Datasets: FreiHAND - HO-3D v2 - COCO-WholeBody
โ
@Machine_learn
Paper: https://arxiv.org/pdf/2409.12259v1.pdf
Code: https://github.com/rolpotamias/WiLoR
Datasets: FreiHAND - HO-3D v2 - COCO-WholeBody
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Large Brain Model for Learning Generic Representations with Tremendous EEG Data in BCI
๐ฅ Github: https://github.com/935963004/labram
๐ Paper: https://arxiv.org/abs/2405.18765v1
@Machine_learn
@Machine_learn
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Forwarded from Papers
ุณูุงู
ุฏูุณุชุงูู ูู ู
ูุงูู ุจุฑุงู ุงุฑุณุงู ุจู ฺูุฑูุงู ุฏุงุฑู ู
ู ุชููู ุจูุฏู ุฑู ุจู ุนููุงู ุฏุงูุฑ ุฏุฑ ุณู ฺูุฑูุงู ุฒูุฑ ู
ุนุฑูู ูููุฏ
1-Knowledge-Based system(https://www.sciencedirect.com/journal/knowledge-based-systems)
2-Machine learning with application(https://www.sciencedirect.com/journal/machine-learning-with-applications)
3-Ai(https://www.sciencedirect.com/journal/artificial-intelligence)
Name:Ramin Mousa
Email: [email protected]
ูู ฺููู ุฏูุณุชุงูู ูู ู ูุงูู ุจุฑุงู ุงุฑุณุงู ุฏุงุฑู ู ู ุชููู ูุจู ุงุฑุณุงู ุฌูุช ุจุฑุฑุณู ุจู ุจูุฏู ุงุฑุณุงู ููู ุชุง ูู ูพูุด ุฏุงูุฑู ุงูุฌุงู ุจุฏู .
@Raminmousa
@Paper4money
@Machine_learn
1-Knowledge-Based system(https://www.sciencedirect.com/journal/knowledge-based-systems)
2-Machine learning with application(https://www.sciencedirect.com/journal/machine-learning-with-applications)
3-Ai(https://www.sciencedirect.com/journal/artificial-intelligence)
Name:Ramin Mousa
Email: [email protected]
ูู ฺููู ุฏูุณุชุงูู ูู ู ูุงูู ุจุฑุงู ุงุฑุณุงู ุฏุงุฑู ู ู ุชููู ูุจู ุงุฑุณุงู ุฌูุช ุจุฑุฑุณู ุจู ุจูุฏู ุงุฑุณุงู ููู ุชุง ูู ูพูุด ุฏุงูุฑู ุงูุฌุงู ุจุฏู .
@Raminmousa
@Paper4money
@Machine_learn
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TensorIR: An Abstraction for Automatic Tensorized Program Optimization
Paper: https://arxiv.org/pdf/2207.04296v2.pdf
Codes: https://github.com/mlc-ai/web-llm - https://github.com/apache/tvm
โ
@Machine_learn
Paper: https://arxiv.org/pdf/2207.04296v2.pdf
Codes: https://github.com/mlc-ai/web-llm - https://github.com/apache/tvm
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# setting up a venv:
conda create -n depth-pro -y python=3.9
conda activate depth-pro
pip install -e .
# Download pretrained checkpoints:
source get_pretrained_models.sh
# Run the inference from CLI on a single image:
depth-pro-run -i ./data/example.jpg
# Running from python
from PIL import Image
import depth_pro
model, transform = depth_pro.create_model_and_transforms()
model.eval()
image, _, f_px = depth_pro.load_rgb(image_path)
image = transform(image)
prediction = model.infer(image, f_px=f_px)
depth = prediction["depth"] # Depth in [m].
focallength_px = prediction["focallength_px"] # Focal length in pixels.@Machine_learn
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