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08/17/2023
Consciousness in Artificial Intelligence: Insights from the Science of Consciousness
Whether current or near-term AI systems could be conscious is a topic of...
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05/30/2023
How Does Information Bottleneck Help Deep Learning?
Numerous deep learning algorithms have been inspired by and understood v...
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02/13/2023
Sources of Richness and Ineffability for Phenomenally Conscious States
Conscious states (states that there is something it is like to be in) se...
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10/24/2022
GFlowOut: Dropout with Generative Flow Networks
Bayesian Inference offers principled tools to tackle many critical probl...
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10/02/2022
GFlowNets and variational inference
This paper builds bridges between two families of probabilistic algorith...
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02/02/2022
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Vector Quantization (VQ) is a method for discretizing latent representat...
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12/06/2021
Properties of Minimizing Entropy
Compact data representations are one approach for improving generalizati...
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06/15/2021
Predicting Unreliable Predictions by Shattering a Neural Network
Piecewise linear neural networks can be split into subfunctions, each wi...
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06/22/2020
Automatic Recall Machines: Internal Replay, Continual Learning and the Brain
Replay in neural networks involves training on sequential data with memo...
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04/06/2020
There and Back Again: Revisiting Backpropagation Saliency Methods
Saliency methods seek to explain the predictions of a model by producing...
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10/19/2019
NormGrad: Finding the Pixels that Matter for Training
The different families of saliency methods, either based on contrastive ...
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11/06/2018
On the role of neurogenesis in overcoming catastrophic forgetting
Lifelong learning capabilities are crucial for artificial autonomous age...
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07/17/2018
