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Found 8 papers, 3 papers with code
nach0: Multimodal Natural and Chemical Languages Foundation Model
Large Language Models (LLMs) have substantially driven scientific progress in various domains, and many papers have demonstrated their ability to tackle complex problems with creative solutions.
Improving Small Molecule Generation using Mutual Information Machine
We achieve state-of-the-art results in several constrained single property optimization tasks as well as in the challenging task of multi-objective optimization, improving over previous success rate SOTA by more than 5\% .
SentenceMIM: A Latent Variable Language Model
MIM learning encourages high mutual information between observations and latent variables, and is robust against posterior collapse.
Ranked #1 on
Question Answering
on YahooCQA
(using extra training data)
MIM: Mutual Information Machine
Experiments show that MIM learns representations with high mutual information, consistent encoding and decoding distributions, effective latent clustering, and data log likelihood comparable to VAE, while avoiding posterior collapse.
High Mutual Information in Representation Learning with Symmetric Variational Inference
We introduce the Mutual Information Machine (MIM), a novel formulation of representation learning, using a joint distribution over the observations and latent state in an encoder/decoder framework.
TzK: Flow-Based Conditional Generative Model
We formulate a new class of conditional generative models based on probability flows.
Walking on Thin Air: Environment-Free Physics-based Markerless Motion Capture
To our knowledge, this is the first approach to take physics into account without explicit {\em a priori} knowledge of the environment or body dimensions.
TzK Flow - Conditional Generative Model
Unlike autoencoders, the bottleneck does not limit model expressiveness, similar to flow-based ML.
