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Found 13 papers, 4 papers with code
$α$-TCVAE: On the relationship between Disentanglement and Diversity
The proposed TC bound is grounded in information theory constructs, generalizes the $\beta$-VAE lower bound, and can be reduced to a convex combination of the known variational information bottleneck (VIB) and conditional entropy bottleneck (CEB) terms.
Object-Centric Temporal Consistency via Conditional Autoregressive Inductive Biases
Unsupervised object-centric learning from videos is a promising approach towards learning compositional representations that can be applied to various downstream tasks, such as prediction and reasoning.
Masked Generative Priors Improve World Models Sequence Modelling Capabilities
Building on the Efficient Stochastic Transformer-based World Models (STORM) architecture, we replace the traditional MLP prior with a Masked Generative Prior (e. g., MaskGIT Prior) and introduce GIT-STORM.
Bayesian-LoRA: LoRA based Parameter Efficient Fine-Tuning using Optimal Quantization levels and Rank Values trough Differentiable Bayesian Gates
In this work, we propose Bayesian-LoRA which approaches low-rank adaptation and quantization from a Bayesian perspective by employing a prior distribution on both quantization levels and rank values.
Precipitation Nowcasting Using Physics Informed Discriminator Generative Models
The proposed model adopts a GAN structure, featuring a Vector Quantization Generative Adversarial Network (VQ-GAN) and a Transformer as the generator, with a temporal discriminator serving as the discriminator.
Extreme Precipitation Nowcasting using Transformer-based Generative Models
This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization.
Discrete Messages Improve Communication Efficiency among Isolated Intelligent Agents
Individuals, despite having varied life experiences and learning processes, can communicate effectively through languages.
TC-VAE: Uncovering Out-of-Distribution Data Generative Factors
We show that the proposed model is able to uncover OOD generative factors on different datasets and outperforms on average the related baselines in terms of downstream disentanglement metrics.
Stateful active facilitator: Coordination and Environmental Heterogeneity in Cooperative Multi-Agent Reinforcement Learning
We formalize the notions of coordination level and heterogeneity level of an environment and present HECOGrid, a suite of multi-agent RL environments that facilitates empirical evaluation of different MARL approaches across different levels of coordination and environmental heterogeneity by providing a quantitative control over coordination and heterogeneity levels of the environment.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Coordinating Policies Among Multiple Agents via an Intelligent Communication Channel
In Multi-Agent Reinforcement Learning (MARL), specialized channels are often introduced that allow agents to communicate directly with one another.
Intelligent Communication
Multi-agent Reinforcement Learning
+2
Adaptation through prediction: multisensory active inference torque control
Adaptation to external and internal changes is major for robotic systems in uncertain environments.
Active Inference in Robotics and Artificial Agents: Survey and Challenges
Active inference is a mathematical framework which originated in computational neuroscience as a theory of how the brain implements action, perception and learning.
Multimodal VAE Active Inference Controller
Active inference, a theoretical construct inspired by brain processing, is a promising alternative to control artificial agents.
