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Hi there, I am Jiaxin 👋!

🔭 I am a Staff Research Scientist at Intuit AI Research where my focus is Generative AI (large language models (LLMs), and diffusion models), and AI Robustness & Safety (uncertainty, reliability, and trustworthiness) with extensive applications to complex real-world tasks. Previously, I was a Research Staff in the Computer Science and Mathematics Division at Oak Ridge National Laboratory where my research aims at accelerating AI for Science on supercomputers, such as Summit and Frontier. I received my Ph.D. from the Johns Hopkins University with an emphasis on uncertainty quantification (UQ).

📫 You may find more information through my personal website and feel free to contact me via email at [email protected].

😄 Some recent publications in LLMs (full publication list in Google Scholar)

Jiaxin's GitHub stats

Jiaxin Zhang's Projects

cosine icon cosine

[NAACL 2021] This is the code for our paper `Fine-Tuning Pre-trained Language Model with Weak Supervision: A Contrastive-Regularized Self-Training Approach'.

course icon course

The Hugging Face course on Transformers

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Mutual Information Predicts Hallucinations in Abstractive Summarization

crossattentioncontrol icon crossattentioncontrol

Unofficial implementation of "Prompt-to-Prompt Image Editing with Cross Attention Control" with Stable Diffusion

cs-flow icon cs-flow

This is the official repository to the WACV 2022 paper "Fully Convolutional Cross-Scale-Flows for Image-based Defect Detection" by Marco Rudolph, Tom Wehrbein, Bodo Rosenhahn and Bastian Wandt.

cs228_pgm icon cs228_pgm

🌀 Stanford CS 228 - Probabilistic Graphical Models

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CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances (NeurIPS 2020)

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CURL: Contrastive Unsupervised Representation Learning for Sample-Efficient Reinforcement Learning

cycle-diffusion icon cycle-diffusion

Code for "Unifying Diffusion Models' Latent Space, with Applications to CycleDiffusion and Guidance"

d2c icon d2c

PyTorch implementation of D2C: Diffuison-Decoding Models for Few-shot Conditional Generation.

d2l-en icon d2l-en

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 200 universities.

dad icon dad

Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design

dag-nf icon dag-nf

Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tractable Jacobian.

data-augmentation-review icon data-augmentation-review

List of useful data augmentation resources. You will find here some not common techniques, libraries, links to github repos, papers and others.

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