日本語
①
■書誌情報
Akihiro Nakano, Masahiro Suzuki, Yutaka Matsuo. “Interaction-Based Disentanglement of Entities for Object-Centric World Models”, International Conference on Learning Representations (ICLR 2023)
■概要
Perceiving the world compositionally in terms of space and time is essential to understanding object dynamics and solving downstream tasks. Object-centric learning using generative models has improved in its ability to learn distinct representations of individual objects and predict their interactions, and how to utilize the learned representations to solve untrained, downstream tasks is a focal question. However, as models struggle to predict object interactions and track the objects accurately, especially for unseen configurations, using object-centric representations in downstream tasks is still a challenge. This paper proposes STEDIE, a new model that disentangles object representations, based on interactions, into interaction-relevant relational features and interaction-irrelevant global features without supervision. Empirical evaluation shows that the proposed model factorizes global features, unaffected by interactions from relational features that are necessary to predict the outcome of interactions. We also show that STEDIE achieves better performance in planning tasks and understanding causal relationships. In both tasks, our model not only achieves better performance in terms of reconstruction ability but also utilizes the disentangled representations to solve the tasks in a structured manner.
②
◼︎書誌情報
Hiroki Furuta, Yusuke Iwasawa, Yutaka Matsuo, Shixiang Shane Gu. “A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation (ICLR 2023).
◼︎概要
The rise of generalist large-scale models in natural language and vision has made us expect that a massive data-driven approach could achieve broader generalization in other domains such as continuous control. In this work, we explore a method for learning a single policy that manipulates various forms of agents to solve various tasks by distilling a large amount of proficient behavioral data. In order to align input-output (IO) interface among multiple tasks and diverse agent morphologies while preserving essential 3D geometric relations, we introduce morphology-task graph, which treats observations, actions and goals/task in a unified graph representation. We also develop MxT-Bench for fast large-scale behavior generation, which supports procedural generation of diverse morphology-task combinations with a minimal blueprint and hardware-accelerated simulator. Through efficient representation and architecture selection on MxT-Bench, we find out that a morphology-task graph representation coupled with Transformer architecture improves the multi-task performances compared to other baselines including recent discrete tokenization, and provides better prior knowledge for zero-shot transfer or sample efficiency in downstream multi-task imitation learning. Our work suggests large diverse offline datasets, unified IO representation, and policy representation and architecture selection through supervised learning form a promising approach for studying and advancing morphology-task generalization.
③
■書誌情報
Machel Reid, Vincent Josua Hellendoorn, Graham Neubig”DiffusER: Diffusion via Edit-based Reconstruction”(ICLR 2023)
■概要
In text generation, models that generate text from scratch one token at a time are currently the dominant paradigm. Despite being performant, these models lack the ability to revise existing text, which limits their usability in many practical scenarios. We look to address this, with DIFFUSER (Diffusion via Edit-based Reconstruction), a new edit-based generative model for text based on denoising diffusion models – a class of models that use a Markov chain of denoising steps to incrementally generate data. DIFFUSER is not only a strong generative model in general, rivalling autoregressive models on several tasks spanning machine translation, summarization, and style transfer; it can also perform other varieties of generation that standard autoregressive models are not well-suited for. For instance, we demonstrate that DIFFUSER makes it possible for a user to condition generation on a prototype, or an incomplete sequence, and continue revising based on previous edit steps.
