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Found 13 papers, 4 papers with code
Can Large Language Models Understand Symbolic Graphics Programs?
While LLMs exhibit impressive skills in general program synthesis and analysis, symbolic graphics programs offer a new layer of evaluation: they allow us to test an LLM's ability to answer different-grained semantic-level questions of the images or 3D geometries without a vision encoder.
Improving Probabilistic Diffusion Models With Optimal Covariance Matching
The probabilistic diffusion model has become highly effective across various domains.
Verbalized Machine Learning: Revisiting Machine Learning with Language Models
Motivated by the progress made by large language models (LLMs), we introduce the framework of verbalized machine learning (VML).
Your Finetuned Large Language Model is Already a Powerful Out-of-distribution Detector
We revisit the likelihood ratio between a pretrained large language model (LLM) and its finetuned variant as a criterion for out-of-distribution (OOD) detection.
A Compact Representation for Bayesian Neural Networks By Removing Permutation Symmetry
Bayesian neural networks (BNNs) are a principled approach to modeling predictive uncertainties in deep learning, which are important in safety-critical applications.
The SVHN Dataset Is Deceptive for Probabilistic Generative Models Due to a Distribution Mismatch
However, with this work, we aim to warn the community about an issue of the SVHN dataset as a benchmark for generative modeling tasks: we discover that the official split into training set and test set of the SVHN dataset are not drawn from the same distribution.
Upgrading VAE Training With Unlimited Data Plans Provided by Diffusion Models
Variational autoencoders (VAEs) are popular models for representation learning but their encoders are susceptible to overfitting (Cremer et al., 2018) because they are trained on a finite training set instead of the true (continuous) data distribution $p_{\mathrm{data}}(\mathbf{x})$.
Trading Information between Latents in Hierarchical Variational Autoencoders
Variational Autoencoders (VAEs) were originally motivated (Kingma & Welling, 2014) as probabilistic generative models in which one performs approximate Bayesian inference.
Iterative Teaching by Data Hallucination
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i. e., a pool of finite samples), which greatly limits the teacher's capability.
Out-of-Distribution Detection with Class Ratio Estimation
In this work, we propose to unify density ratio based methods under a novel framework that builds energy-based models and employs differing base distributions.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
Improving VAE-based Representation Learning
Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images.
Solipsistic Reinforcement Learning
We introduce a new model-based reinforcement learning framework that aims to tackle environments with high dimensional state spaces.
Model-based Reinforcement Learning
reinforcement-learning
+2
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models.
