1 citations · 2 across the 7 of their papers we have counts for
7 papers
Inference-Time Rethinking with Latent Thought Vectors for Math Reasoning
Deqian Kong, Minglu Zhao, Aoyang Qin +10
Standard chain-of-thought reasoning generates a solution in a single forward pass, committing irrevocably to each token and lacking a mechanism to recover from early errors. We int…
Latent Thought Models with Variational Bayes Inference-Time Computation
Deqian Kong, Minglu Zhao, Dehong Xu +8
We propose a novel class of language models, Latent Thought Models (LTMs), which incorporate explicit latent thought vectors that follow an explicit prior model in latent space. Th…
Better Prompt Compression Without Multi-Layer Perceptrons
Edouardo Honig, Andrew Lizarraga, Zijun Frank Zhang +1
Prompt compression is a promising approach to speeding up language model inference without altering the generative model. Prior works compress prompts into smaller sequences of lea…
Long-range gene expression prediction with token alignment of large language model
Edouardo Honig, Huixin Zhan, Ying Nian Wu +1
Gene expression is a cellular process that plays a fundamental role in human phenotypical variations and diseases. Despite advances of deep learning models for gene expression pred…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +8
This work explores the challenging problem of molecule design by framing it as a conditional generative modeling task, where target biological properties or desired chemical constr…
Differentiable VQ-VAE's for Robust White Matter Streamline Encodings
Andrew Lizarraga, Brandon Taraku, Edouardo Honig +2
Given the complex geometry of white matter streamlines, Autoencoders have been proposed as a dimension-reduction tool to simplify the analysis streamlines in a low-dimensional late…
Molecule Design by Latent Prompt Transformer
Deqian Kong, Yuhao Huang, Jianwen Xie +1
This paper proposes a latent prompt Transformer model for solving challenging optimization problems such as molecule design, where the goal is to find molecules with optimal values…