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Found 22 papers, 16 papers with code
Embracing Unknown Step by Step: Towards Reliable Sparse Training in Real World
Sparse training has emerged as a promising method for resource-efficient deep neural networks (DNNs) in real-world applications.
Gradient-based Discrete Sampling with Automatic Cyclical Scheduling
Discrete distributions, particularly in high-dimensional deep models, are often highly multimodal due to inherent discontinuities.
Training Bayesian Neural Networks with Sparse Subspace Variational Inference
Bayesian neural networks (BNNs) offer uncertainty quantification but come with the downside of substantially increased training and inference costs.
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Enhancing Low-Precision Sampling via Stochastic Gradient Hamiltonian Monte Carlo
This paper investigates low-precision sampling via Stochastic Gradient Hamiltonian Monte Carlo (SGHMC) with low-precision and full-precision gradient accumulators for both strongly log-concave and non-log-concave distributions.
Entropy-MCMC: Sampling from Flat Basins with Ease
Bayesian deep learning counts on the quality of posterior distribution estimation.
Rethinking Data Distillation: Do Not Overlook Calibration
Neural networks trained on distilled data often produce over-confident output and require correction by calibration methods.
Analysis of Climate Campaigns on Social Media using Bayesian Model Averaging
Climate change is the defining issue of our time, and we are at a defining moment.
DP-Fast MH: Private, Fast, and Accurate Metropolis-Hastings for Large-Scale Bayesian Inference
In this paper, we study Metropolis-Hastings (MH), one of the most fundamental MCMC methods, for large-scale Bayesian inference under differential privacy.
Long-tailed Classification from a Bayesian-decision-theory Perspective
Long-tailed classification poses a challenge due to its heavy imbalance in class probabilities and tail-sensitivity risks with asymmetric misprediction costs.
Efficient Informed Proposals for Discrete Distributions via Newton's Series Approximation
Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions.
Calibrating the Rigged Lottery: Making All Tickets Reliable
Previous research has shown that deep neural networks tend to be over-confident, and we find that sparse training exacerbates this problem.
Balance is Essence: Accelerating Sparse Training via Adaptive Gradient Correction
To accelerate and stabilize the convergence of sparse training, we analyze the gradient changes and develop an adaptive gradient correction method.
Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent
Sampling methods, as important inference and learning techniques, are typically designed for unconstrained domains.
Low-Precision Stochastic Gradient Langevin Dynamics
While low-precision optimization has been widely used to accelerate deep learning, low-precision sampling remains largely unexplored.
A Langevin-like Sampler for Discrete Distributions
We propose discrete Langevin proposal (DLP), a simple and scalable gradient-based proposal for sampling complex high-dimensional discrete distributions.
Meta-Learning Divergences of Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and broad applicability.
Asymptotically Optimal Exact Minibatch Metropolis-Hastings
Metropolis-Hastings (MH) is a commonly-used MCMC algorithm, but it can be intractable on large datasets due to requiring computations over the whole dataset.
AMAGOLD: Amortized Metropolis Adjustment for Efficient Stochastic Gradient MCMC
This improves performance, but introduces bias that can cause SGHMC to converge to the wrong distribution.
Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees
Gibbs sampling is a Markov chain Monte Carlo method that is often used for learning and inference on graphical models.
Meta-Learning for Variational Inference
Variational inference (VI) plays an essential role in approximate Bayesian inference due to its computational efficiency and general applicability.
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
The posteriors over neural network weights are high dimensional and multimodal.
