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Found 5 papers, 4 papers with code
Evaluating Language Model Agency through Negotiations
We introduce an approach to evaluate language model (LM) agency using negotiation games.
Increasing Expressivity of a Hyperspherical VAE
Learning suitable latent representations for observed, high-dimensional data is an important research topic underlying many recent advances in machine learning.
Reparameterizing Distributions on Lie Groups
Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups.
Explorations in Homeomorphic Variational Auto-Encoding
Our experiments show that choosing manifold-valued latent variables that match the topology of the latent data manifold, is crucial to preserve the topological structure and learn a well-behaved latent space.
Hyperspherical Variational Auto-Encoders
But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure.
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