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README.md

10-701 Project: Molecular Machine Learning


Project Topic


Datasets



Potential Project Topics

  • Compound Figure Separation
    (deep learning) (convolutional neural networks)

  • Transfer Learning

    • Overcoming Data Scarcity with Transfer Learning (abstract)
  • Causality
    (experimental design) (counterfactual prediction) (deep learning)

    • DeepIV: Counterfactual Prediction using Instrument Variables (abstract) (github)
    • A Minimax Surrogate Loss Approach to Causal Inference (paper)
  • Information Retrieval
    (probabilistic graphical models)

  • Infinitely differentiable loss function (Monte Carlo Estimator)
    (optimization) (deep learning) (reinforcement learning)

    • DiCE: The Infinitely Differentiable Monte Carlo Estimator (abstract)
  • Automatic generation of surrogate loss functions given non-differentiable loss functions (ACKTR, A2C, PPO)
    (optimization)

    • Surrogate Loss Functions (blog post)
    • ML for non-differentiable Loss Functions (blog post)
    • Comparing Loss Functions by Risk (paper)
    • Surrogate Loss Functions and ƒ-divergences (paper)

About

Replicating results from the MoleculeNet benchmark.

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