Prior-Fitted Networks
[ Notebook (Soln.) ]Publication: Müller et al. Transformers can do Bayesian Inference. ICLR, 2022Task: Using Transformers to estimate Posterior Predictive Distributions Libraries: PyTorch Learning objectives:
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Data Generating Priors for Prior-Fitted Networks
[ Notebook (Soln.) ]Publication: Müller et al. Transformers can do Bayesian Inference. ICLR, 2022Hollmann et al. TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second. ICLR, 2023 Task: Generating data according to different priors for PFNs Libraries: PyTorch Learning objectives: Learn how to generate synthetic data based on specified priors and utilize it for training neural networks to approximate Bayesian inference. |
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LLMTime - Zero-shot prompting LLMs for time series forecasting
[ Notebook (DIY) ] [ Notebook (Soln.) ]Publication: Gruver et al. Large Language Models are Zero Shot Time Series Forecasters. NeurIPS, 2023Task: Weather forecasting using LLMs Libraries: openai, tiktoken, jax Learning objectives: Explore zero-shot prompting with Large Language Models (LLMs) for time series forecasting. In this tutorial, we aim to:
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Attention is all you need
[ Notebook (DIY) ] [ Notebook (Soln.) ]Publication: Vaswani, Ashish, et al. "Attention is all you need." NeurIPS 2017.Task: Neural Machine Translation (e.g, German-English) Dataset: Multi30k Libraries: PyTorch, NLTK, Spacy, torchtext Learning objectives:
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Critical Exploration of Transformer Models
[ Notebook (DIY) ] [ Notebook (Soln.) ]Learning objectives: Delve into the inner workings of transformer models beyond basic applicationsKey Areas:
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Molecule Attention Transformer
[ Notebook (Soln.) ]Publication: Maziarka, et al. "Molecule Attention Transformer"Task: Classification task to predict Blood-brain barrier permeability (BBBP) Dataset: BBBP Libraries: PyTorch, DeepChem, RDKit Learning objectives:
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Accessing Research Data for Social Science [Oxford Internet Institute, MT 2022]
[ Notebook (DIY) ]DeepNote: (Jupyter notebook hosting service) DIY Notebooks.Github: Repository to work on your local machine. Programming Language: Python Libraries: Pandas, feedparser, newscatcherapi, psaw, requests, twarc (Twitter API), requests-html Learning objectives:
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Prediction of COVID Infection using reported symptoms
[ Notebook (DIY) ] [ Notebook (Soln.) ]Based on: Zoabi et al. "Machine learning-based prediction of COVID-19 diagnosis based on symptoms." npj digital medicine 4.1 (2021): 1-5.Task: Predict COVID-19 infection from reported symptoms Dataset: English translation of COVID infections reported by Israeli Ministry of Health Learning objectives:
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