Search Results for author:
Found 20 papers, 8 papers with code
Learning Attentive Meta-Transfer
Meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning in a setting with a stream of evolving tasks.
Dreamweaver: Learning Compositional World Representations from Pixels
Furthermore, we show how the modularized concept representations of our model enable compositional imagination, allowing the generation of novel videos by recombining attributes from different objects.
Slot State Space Models
Unlike conventional SSMs that maintain a monolithic state vector, SlotSSMs maintains the state as a collection of multiple vectors called slots.
Parallelized Spatiotemporal Binding
While modern best practices advocate for scalable architectures that support long-range interactions, object-centric models are yet to fully embrace these architectures.
Object-Centric Slot Diffusion
The recent success of transformer-based image generative models in object-centric learning highlights the importance of powerful image generators for handling complex scenes.
Neural Systematic Binder
While token-like structured knowledge representations are naturally provided in text, it is elusive how to obtain them for unstructured modalities such as scene images.
Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos
Unsupervised object-centric learning aims to represent the modular, compositional, and causal structure of a scene as a set of object representations and thereby promises to resolve many critical limitations of traditional single-vector representations such as poor systematic generalization.
Illiterate DALL-E Learns to Compose
In this paper, we propose a simple but novel slot-based autoencoding architecture, called SLATE, for combining the best of both worlds: learning object-centric representations that allows systematic generalization in zero-shot image generation without text.
Illiterate DALL$\cdot$E Learns to Compose
In experiments, we show that this simple architecture achieves zero-shot generation of novel images without text and better quality in generation than the models based on mixture decoders.
Structured World Belief for Reinforcement Learning in POMDP
In this paper, we propose Structured World Belief, a model for learning and inference of object-centric belief states.
Robustifying Sequential Neural Processes
When tasks change over time, meta-transfer learning seeks to improve the efficiency of learning a new task via both meta-learning and transfer-learning.
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Previous approaches for unsupervised object-oriented scene representation learning are either based on spatial-attention or scene-mixture approaches and limited in scalability which is a main obstacle towards modeling real-world scenes.
Attentive Sequential Neural Processes
In this paper, we propose the Attentive Sequential Neural Processes (ASNP) that resolve the underfitting in SNP by introducing a novel imaginary context as a latent variable and by applying attention over the imaginary context.
Sequential Neural Processes
In this paper, we propose Sequential Neural Processes (SNP) which incorporates a temporal state-transition model of stochastic processes and thus extends its modeling capabilities to dynamic stochastic processes.
Mining Procedures from Technical Support Documents
Existing research on question answering or intelligent chatbots does not look within procedures or deep-understand them.
Generating Clues for Gender based Occupation De-biasing in Text
Vast availability of text data has enabled widespread training and use of AI systems that not only learn and predict attributes from the text but also generate text automatically.
Cross-Lingual Predicate Mapping Between Linked Data Ontologies
In this paper, we propose and demonstrate an ad-hoc system to find possible owl:equivalentProperty links between predicates in ontologies of different natural languages.
Nonparametric Scene Parsing with Adaptive Feature Relevance and Semantic Context
This paper presents a nonparametric approach to semantic parsing using small patches and simple gradient, color and location features.
