Jinghong Chen

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Jinghong Chen

Jinghong Chen

(Vision-Language Researcher) Selected Papers @NeurIPS 2023

Here are some papers that we, who mainly work on vision-language models, think are interesting on Day 4 of NeurIPS 2023. Tree of Thoughts: Deliberate Problem Solving with Large Language Models. Tree of Thoughts (ToT) is a decoding scheme for auto-regressive Transformer. A thought is defined as a coherent piece

Efficient Learning Orals @NeurIPS 2023

We will walk through 3 oral presentations on Efficient Learning at NeurIPS 2023 in 2 minutes. Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture Presentor: Dan Fu@Stanford. Paper. Attention and MLP layers scale quadratically in sequence length and model dimension. This work proposes Monarch Mixer, an alternative architecture with sub-quadratic

Data Contribution Estimation (DCE) for Machine Learning: Tutorial@NeurIPS 2023 in 10-minutes

Prof. Ruoxi Jia, the tutorial's presentor has kindly proofread this summary. Data Contribution Estimation (DCE) estimates how important a training data point is to the model's performance. People use DCE to (1) find mislabeled data that has negative impact on performance; (2) conduct data valuation to

Highlight Talks @NeurIPS 2023

Highlight Talks @NeurIPS 2023

Here are the highlights of Talks from Day 1. Optimizing LLM Inference: the topic comes up in 4 talks. * Databricks presents how to use first principles to optimize Transformer inference. Linden Li explains the steps of inference, "prefill" and "decode", and shows simple formulas to determine

[Quick Pitch] Late-Interaction Knowledge Retriever (FLMR) for Visual Question Answering @NeurIPS 2023

Knowledge-based Visual Question Answering (KBVQA) aims to answer a question related to an image that requires some world knowledge. Here's an example. Our NeurIPS paper takes the retrieval-augmented approach to tackles KBVQA. We first retrieve relevant documents from an external database and then generate answers based on the

[Quick Pitch] Learning from MBR decoding using Direct Preference Optimization

Minimum Bayes Risk (MBR) decoding generally outperforms temperature sampling and beam search. But it is expensive computationally. We can train the model on the MBR decoding outputs so that cheaper decoding methods perform on par with MBR. Google calls it "MBR fine-tuning". Our recent work introduces a more