Search Results for author:
Found 10 papers, 2 papers with code
Simulation-based Inference with the Generalized Kullback-Leibler Divergence
The objective recovers Neural Posterior Estimation when the model class is normalized and unifies it with Neural Ratio Estimation, combining both into a single objective.
Latent Representation and Simulation of Markov Processes via Time-Lagged Information Bottleneck
Markov processes are widely used mathematical models for describing dynamic systems in various fields.
On the Effectiveness of Hybrid Mutual Information Estimation
Estimating the mutual information from samples from a joint distribution is a challenging problem in both science and engineering.
Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics
Coarse-grained (CG) molecular dynamics enables the study of biological processes at temporal and spatial scales that would be intractable at an atomistic resolution.
Compositional Mixture Representations for Vision and Text
Learning a common representation space between vision and language allows deep networks to relate objects in the image to the corresponding semantic meaning.
Towards Lightweight Controllable Audio Synthesis with Conditional Implicit Neural Representations
The high temporal resolution of audio and our perceptual sensitivity to small irregularities in waveforms make synthesizing at high sampling rates a complex and computationally intensive task, prohibiting real-time, controllable synthesis within many approaches.
A Bayesian Approach to Invariant Deep Neural Networks
We propose a novel Bayesian neural network architecture that can learn invariances from data alone by inferring a posterior distribution over different weight-sharing schemes.
An Information-theoretic Approach to Distribution Shifts
Safely deploying machine learning models to the real world is often a challenging process.
Learning Robust Representations via Multi-View Information Bottleneck
This enables us to identify superfluous information as that not shared by both views.
Improved Bayesian Compression
Compression of Neural Networks (NN) has become a highly studied topic in recent years.
