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Found 8 papers, 4 papers with code
Trajectory Balance with Asynchrony: Decoupling Exploration and Learning for Fast, Scalable LLM Post-Training
A training node simultaneously samples data from this buffer based on reward or recency to update the policy using Trajectory Balance (TB), a diversity-seeking RL objective introduced for GFlowNets.
Solving Bayesian inverse problems with diffusion priors and off-policy RL
This paper presents a practical application of Relative Trajectory Balance (RTB), a recently introduced off-policy reinforcement learning (RL) objective that can asymptotically solve Bayesian inverse problems optimally.
Outsourced diffusion sampling: Efficient posterior inference in latent spaces of generative models
We propose to amortize the cost of sampling from such posterior distributions with diffusion models that sample a distribution in the noise space ($\mathbf{z}$).
Amortizing intractable inference in diffusion models for vision, language, and control
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem.
Reasoning with Latent Diffusion in Offline Reinforcement Learning
However, a key challenge in offline RL lies in effectively stitching portions of suboptimal trajectories from the static dataset while avoiding extrapolation errors arising due to a lack of support in the dataset.
MLNav: Learning to Safely Navigate on Martian Terrains
We present MLNav, a learning-enhanced path planning framework for safety-critical and resource-limited systems operating in complex environments, such as rovers navigating on Mars.
Machine Learning Based Path Planning for Improved Rover Navigation (Pre-Print Version)
Enhanced AutoNav (ENav), the baseline surface navigation software for NASA's Perseverance rover, sorts a list of candidate paths for the rover to traverse, then uses the Approximate Clearance Evaluation (ACE) algorithm to evaluate whether the most highly ranked paths are safe.
Deep Residual Neural Networks for Image in Speech Steganography
We propose a deep learning based technique to hide a source RGB image message inside finite length speech segments without perceptual loss.
Multimedia Sound Audio and Speech Processing
