📃 Large language model to multimodal large language model: A journey to shape the biological macromolecules to biological sciences and medicine
📓 Journal: Molecular Therapy Nucleic Acids (I.F.=6.5)
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@Machine_learn
📓 Journal: Molecular Therapy Nucleic Acids (I.F.=6.5)
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@Machine_learn
📑 Advances of the recent data-driven paradigm shift in medicine and healthcare: From machine learning to deep learning
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@Machine_learn
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@Machine_learn
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Machine learning books and papers
با عرض سلام مقاله زیر در مرحله major revision میباشد. نفر ۴ ام از این مقاله قابل اضافه کردن. Abstract Breast cancer stands as a prevalent cause of fatality among females on a global scale, with prompt detection playing a pivotal role in diminishing mortality…
با عرض سلام تمامي كار هاي مشترك تموم شدن و فقط اين كار باقي مونده....!
@Raminmousa
@Raminmousa
Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main
📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf
🌟 Dataset: https://paperswithcode.com/dataset/bdd100k
@Machine_learn
🌟 Dataset: https://paperswithcode.com/dataset/bdd100k
@Machine_learn
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# Clone repo
git clone https://github.com/Johanan528/DepthLab.git
cd DepthLab
# Create conda env
conda env create -f environment.yaml
conda activate DepthLab
# Run inference
cd scripts
bash infer.sh@Machine_learn
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Deep_Learning_Hyperparameter_tuning_Regularization_and_Optimization.pdf
2.4 MB
Improving Deep Neural Networks: Hyperparameter tuning, Regularization and
Optimization
#Dl
@Machine_learn
Optimization
#Dl
@Machine_learn
# Install from PyPI
pip install outetts
# Interface Usage
import outetts
# Configure the model
model_config = outetts.HFModelConfig_v1(
model_path="OuteAI/OuteTTS-0.2-500M",
language="en", # Supported languages in v0.2: en, zh, ja, ko
)
# Initialize the interface
interface = outetts.InterfaceHF(model_version="0.2", cfg=model_config)
# Optional: Create a speaker profile (use a 10-15 second audio clip)
speaker = interface.create_speaker(
audio_path="path/to/audio/file",
transcript="Transcription of the audio file."
)
# Optional: Load speaker from default presets
interface.print_default_speakers()
speaker = interface.load_default_speaker(name="male_1")
output = interface.generate(
text="%Prompt Text%%.",
temperature=0.1,
repetition_penalty=1.1,
max_length=4096,
# Optional: Use a speaker profile
speaker=speaker,
)
# Save the synthesized speech to a file
output.save("output.wav")@Machine_learn
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Are They the Same? Exploring Visual Correspondence Shortcomings of Multimodal LLMs
🖥 Github: https://github.com/zhouyiks/CoLVA/tree/main
📕 Paper: https://arxiv.org/pdf/2501.04670v1.pdf
⭐️ Dataset: https://paperswithcode.com/dataset/bdd100k
@Machine_learn
@Machine_learn
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📃 Bioinformatics perspectives on transcriptomics: A comprehensive review of bulk and single-cell RNA sequencing analyses
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@Machine_learn
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@Machine_learn
📄 Application of Artificial Intelligence In Drug-target Interactions Prediction: A Review
📗 Journal: npj Biomedical Innovations
🗓Publish year: 2025
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@Machine_learn
📗 Journal: npj Biomedical Innovations
🗓Publish year: 2025
📎 Study the paper
@Machine_learn