WTA bottlenecks enforce highly symbolic, disentangled categorical representations of latent factors under defined conditions in multi-task DNNs, shown via theorem and experiments on two datasets.
Autoencoders.Machine learning for data science handbook: data mining and knowledge discovery handbook, pages 353–374, 2023
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Cola DLM proposes a hierarchical latent diffusion model that learns a text-to-latent mapping, fits a global semantic prior in continuous space with a block-causal DiT, and performs conditional decoding, establishing latent prior modeling as an alternative to token-level autoregressive language model
A pedestal-trained convolutional autoencoder identifies particle structures in CYGNO optical TPC images, retaining 93% of signal intensity while discarding 98% of the area at 25 ms per frame on a consumer GPU.
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Winner-Take-All bottlenecks enforce disentangled symbolic representations in multi-task learning
WTA bottlenecks enforce highly symbolic, disentangled categorical representations of latent factors under defined conditions in multi-task DNNs, shown via theorem and experiments on two datasets.
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Continuous Latent Diffusion Language Model
Cola DLM proposes a hierarchical latent diffusion model that learns a text-to-latent mapping, fits a global semantic prior in continuous space with a block-causal DiT, and performs conditional decoding, establishing latent prior modeling as an alternative to token-level autoregressive language model
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Fast reconstruction-based ROI triggering via anomaly detection in the CYGNO optical TPC
A pedestal-trained convolutional autoencoder identifies particle structures in CYGNO optical TPC images, retaining 93% of signal intensity while discarding 98% of the area at 25 ms per frame on a consumer GPU.