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Found 10 papers, 6 papers with code
Effective Data Augmentation With Diffusion Models
Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning.
A Simple Approach for Visual Rearrangement: 3D Mapping and Semantic Search
Physically rearranging objects is an important capability for embodied agents.
AnyMorph: Learning Transferable Polices By Inferring Agent Morphology
Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent's morphology in advance.
Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization
To address this, we present Design-Bench, a benchmark for offline MBO with a unified evaluation protocol and reference implementations of recent methods.
Discovering Non-monotonic Autoregressive Orderings with Variational Inference
Permutations then serve as target generation orders for training an insertion-based Transformer language model.
Conservative Objective Models for Effective Offline Model-Based Optimization
Computational design problems arise in a number of settings, from synthetic biology to computer architectures.
Discovering Autoregressive Orderings with Variational Inference
One strategy to recover this information is to decode both the content and location of tokens.
Synthetic Datasets for Neural Program Synthesis
The goal of program synthesis is to automatically generate programs in a particular language from corresponding specifications, e. g. input-output behavior.
Inter-Level Cooperation in Hierarchical Reinforcement Learning
This allows us to draw on connections between communication and cooperation in multi-agent RL, and demonstrate the benefits of increased cooperation between sub-policies on the training performance of the overall policy.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Inferring the Optimal Policy using Markov Chain Monte Carlo
Existing methods for estimating the optimal stochastic control policy rely on high variance estimates of the policy descent.
