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Found 11 papers, 4 papers with code
Active World Model Learning in Agent-rich Environments with Progress Curiosity
World models are a family of predictive models that solve self-supervised problems on how the world evolves.
Explanatory models in neuroscience: Part 1 -- taking mechanistic abstraction seriously
These criteria require us, first, to identify a level of description that is both abstract but detailed enough to be "runnable", and then, to construct model-to-brain mappings using the same principles as those employed for brain-to-brain mapping across individuals.
Explanatory models in neuroscience: Part 2 -- constraint-based intelligibility
Computational modeling plays an increasingly important role in neuroscience, highlighting the philosophical question of how computational models explain.
Conditional Negative Sampling for Contrastive Learning of Visual Representations
To do this, we introduce a family of mutual information estimators that sample negatives conditionally -- in a "ring" around each positive.
Active World Model Learning with Progress Curiosity
Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents.
On Mutual Information in Contrastive Learning for Visual Representations
Reformulating previous learning objectives in terms of mutual information also simplifies and stabilizes them.
Flexible and Efficient Long-Range Planning Through Curious Exploration
In contrast, deep reinforcement learning (DRL) methods use flexible neural-network-based function approximators to discover policies that generalize naturally to unseen circumstances.
Unsupervised Learning from Video with Deep Neural Embeddings
Because of the rich dynamical structure of videos and their ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal for training visual representations in deep neural networks.
Local Label Propagation for Large-Scale Semi-Supervised Learning
It then propagates pseudolabels from known to unknown datapoints in a manner that depends on the local geometry of the embedding, taking into account both inter-point distance and local data density as a weighting on propagation likelihood.
Local Aggregation for Unsupervised Learning of Visual Embeddings
Unsupervised approaches to learning in neural networks are of substantial interest for furthering artificial intelligence, both because they would enable the training of networks without the need for large numbers of expensive annotations, and because they would be better models of the kind of general-purpose learning deployed by humans.
Ranked #12 on
Contrastive Learning
on imagenet-1k
Toward Goal-Driven Neural Network Models for the Rodent Whisker-Trigeminal System
In large part, rodents see the world through their whiskers, a powerful tactile sense enabled by a series of brain areas that form the whisker-trigeminal system.
