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Found 7 papers, 2 papers with code
Dual-Space Optimization: Improved Molecule Sequence Design by Latent Prompt Transformer
Designing molecules with desirable properties, such as drug-likeliness and high binding affinities towards protein targets, is a challenging problem.
Latent Plan Transformer: Planning as Latent Variable Inference
We introduce the Latent Plan Transformer (LPT), a novel model that leverages a latent space to connect a Transformer-based trajectory generator and the final return.
Molecule Design by Latent Prompt Transformer
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 of a target chemical or biological property that can be computed by an existing software.
Molecule Design by Latent Space Energy-Based Modeling and Gradual Distribution Shifting
To search for molecules with desired properties, we propose a sampling with gradual distribution shifting (SGDS) algorithm, so that after learning the model initially on the training data of existing molecules and their properties, the proposed algorithm gradually shifts the model distribution towards the region supported by molecules with desired values of properties.
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior Inference
The capability to generate responses with diversity and faithfulness using factual knowledge is paramount for creating a human-like, trustworthy dialogue system.
Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling
We propose to learn an energy-based model (EBM) in the latent space of a top-down generative model such that the EBM in the low dimensional latent space is able to be learned efficiently and adapt to each task rapidly.
YouRefIt: Embodied Reference Understanding with Language and Gesture
To the best of our knowledge, this is the first embodied reference dataset that allows us to study referring expressions in daily physical scenes to understand referential behavior, human communication, and human-robot interaction.
