CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
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Performance analysis of predictive (alpha) stock factors
A library for debugging/inspecting machine learning classifiers and explaining their predictions
An unsupervised learning framework for depth and ego-motion estimation from monocular videos
- deep-learning
- depth-prediction
- visual-odometry
- self-supervised-learning
Natural Gradient Boosting for Probabilistic Prediction
- machine-learning
- gradient-boosting
- natural-gradients
- uncertainty-estimation
A Deep Learning Toolkit for DTI, Drug Property, PPI, DDI, Protein Function Prediction (Bioinformatics)
- drug-repurposing
- deep-learning
- drug-target-interactions
- toolkit
Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow - Guillaume Chevalier
- seq2seq
- tensorflow
- tensorflow-tutorials
- python
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
- aifinance
- fintech
- llama
- machine-learning
🍇 GRAPE is a Rust/Python Graph Representation Learning library for Predictions and Evaluations
- graph
- high-performance
- machine-learning
- knowledge-graph
ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission (CHIL 2020 Workshop)
- clinicalbert
- hospital-readmission
- prediction
TranslatedTensorFlow in practice: using LSTM to predict lottery results
Genomic Pretrained Network - GPN, GPN-MSA, PhyloGPN, GPN-Star
- dna
- language-model
- variant-effect-prediction
- genomics
MolTrans: Molecular Interaction Transformer for Drug Target Interaction Prediction (Bioinformatics)
This is the code for "Bitcoin Prediction" by Siraj Raval on Youtube
Code for "Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution", NeurIPS 2022.
- disentanglement
- drug-discovery
- genomics
- perturbation
A collection of tasks to probe the effectiveness of protein sequence representations in modeling aspects of protein design
- protein
- protein-sequences
- protein-design
- representation-learning
PRnet is a flexible and scalable perturbation-conditioned generative model predicting transcriptional responses to unseen complex perturbations at bulk and single-cell levels.
Interpretable drug response prediction with graph neural networks over drugs, cell lines, and genes.
Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.