Learning Graphical Models
Leveraging the Exact Likelihood of Deep Latent Variable Models
Pierre-Alexandre Mattei, Jes Frellsen
The purpose of this work is to study the general properties of this quantity and to show how they can be leveraged in practice.
Analytic solution and stationary phase approximation for the Bayesian lasso and elastic net
Neural Information Processing Systems http://nips.cc/
Structured Prediction with Stronger Consistency Guarantees
In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.
Structured Prediction with Stronger Consistency Guarantees
In most applications, the output labels of learning problems have some structure that is crucial to consider. This includes natural language processing applications, where the output may be a sentence, a sequence of parts-of-speech tags, a parse tree, or a dependency graph.
Inverse Filtering for Hidden Markov Models
Robert Mattila, Cristian Rojas, Vikram Krishnamurthy, Bo Wahlberg
This paper considers a number of related inverse filtering problems for hidden Markov models (HMMs).
HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
Data serves as the fundamental basis for advancing deep learning.
Spectral Entry-wise Matrix Estimation for Low-Rank Reinforcement Learning
Both algorithms yield state-of-the-art performance guarantees.
Energy-Based Cross Attention for Bayesian Context Update in Text-to-Image Diffusion Models Geon Y eong Park 1 Jeongsol Kim 1 Beomsu Kim 2 Sang Wan Lee 1,2,3
Since diffusion models require the iterative sampling on high dimensional space, they are computationally expansive and time consuming.