Catarina Belém

@PastelBelem8 · User

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PhD Student @ UCI and previously a research data scientist at Feedzai

UCIIrvine, California81 followers42 repositories

Repositories

PastelBelem8/LightGBM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

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PastelBelem8/LM-Steer

Official Code Repository for LM-Steer Paper: "Word Embeddings Are Steers for Language Models" (ACL 2024 Outstanding Paper Award)

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PastelBelem8/axbench

Stanford NLP Python library for benchmarking the utility of LLM interpretability methods

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PastelBelem8/semantic_uncertainty

Codebase for reproducing the experiments of the semantic uncertainty paper (short-phrase and sentence-length experiments).

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PastelBelem8/clinical-data-ml-metadata

Clinical ML metadata specification for columns found available in MIMIC and eICU datasets after being preprocessed with the YAIB-Cohort package.

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PastelBelem8/t-few

Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

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PastelBelem8/ml4nlp-cogsci-summer22

Course materials for the Machine Learning for NLP course taught by Sameer Singh for the Cognitive Science summer school 2022.

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PastelBelem8/unscrambler

NLP-based system whose goal is to recover corrupted files. To this end, we are given both a training and a test set, where the test set is assumed to be generated from the same distribution as the training set.

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PastelBelem8/handwritten-recognition-and-detection

Repository concerning the creation of a system capable of detecting handwritting and recognizing its content. This system will be applied to images involving visual elements other than just handwritting.

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PastelBelem8/ADOPT.jl

This is the result of my master thesis on Multi-Objective Optimization. This repository is more focused towards Pareto-based optimization rather than SIngle-Objective optimization with preference articulation. We focus on time-consuming optimization routines and, as a result we focus on model-based methods to allow for faster convergence times. This is relevant for Architectural Design Optimization, which depends on time-intensive simulations (e.g. minutes, hours or even days to complete a single simulation).

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