Search Results for author:
Found 7 papers, 6 papers with code
Unsupervised Musical Object Discovery from Audio
Our novel MusicSlots method adapts SlotAttention to the audio domain, to achieve unsupervised music decomposition.
Exploring the Promise and Limits of Real-Time Recurrent Learning
To scale to such challenging tasks, we focus on certain well-known neural architectures with element-wise recurrence, allowing for tractable RTRL without approximation.
Mindstorms in Natural Language-Based Societies of Mind
What should be the social structure of an NLSOM?
Contrastive Training of Complex-Valued Autoencoders for Object Discovery
Current state-of-the-art object-centric models use slots and attention-based routing for binding.
Unsupervised Learning of Temporal Abstractions with Slot-based Transformers
The discovery of reusable sub-routines simplifies decision-making and planning in complex reinforcement learning problems.
Unsupervised Object Keypoint Learning using Local Spatial Predictability
We propose PermaKey, a novel approach to representation learning based on object keypoints.
A Neural Temporal Model for Human Motion Prediction
We propose novel neural temporal models for predicting and synthesizing human motion, achieving state-of-the-art in modeling long-term motion trajectories while being competitive with prior work in short-term prediction and requiring significantly less computation.
