Inductive biases for deep learning of higher-level cognition
A fascinating hypothesis is that human and animal intelligence could be explained by a few
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
principles (rather than an encyclopaedic list of heuristics). If that hypothesis was correct, we …
The relational bottleneck as an inductive bias for efficient abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired
from limited experience. This has often been framed in terms of a dichotomy between …
from limited experience. This has often been framed in terms of a dichotomy between …
Conditional object-centric learning from video
T Kipf, GF Elsayed, A Mahendran, A Stone… - ar**
In this paper, we tackle the problem of learning visual representations from unlabeled scene-
centric data. Existing works have demonstrated the potential of utilizing the underlying …
centric data. Existing works have demonstrated the potential of utilizing the underlying …
Improving coherence and consistency in neural sequence models with dual-system, neuro-symbolic reasoning
Human reasoning can be understood as an interplay between two systems: the intuitive and
associative (" System 1") and the deliberative and logical (" System 2"). Neural sequence …
associative (" System 1") and the deliberative and logical (" System 2"). Neural sequence …
Weakly supervised representation learning with sparse perturbations
The theory of representation learning aims to build methods that provably invert the data
generating process with minimal domain knowledge or any source of supervision. Most prior …
generating process with minimal domain knowledge or any source of supervision. Most prior …