Pith. sign in

REVIEW 6 cited by

Hyperspherical Variational Auto-Encoders

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1804.00891 v3 pith:BLNZLKYT submitted 2018-04-03 stat.ML cs.LG

Tim R. Davidson , Luca Falorsi , Nicola De Cao , Thomas Kipf , Jakub M. Tomczak This is my paper
classification stat.MLcs.LG
keywords hypersphericaldatadistributionlatentgithubleadingmathcalnicola-decao
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
abstract

The Variational Auto-Encoder (VAE) is one of the most used unsupervised machine learning models. But although the default choice of a Gaussian distribution for both the prior and posterior represents a mathematically convenient distribution often leading to competitive results, we show that this parameterization fails to model data with a latent hyperspherical structure. To address this issue we propose using a von Mises-Fisher (vMF) distribution instead, leading to a hyperspherical latent space. Through a series of experiments we show how such a hyperspherical VAE, or $\mathcal{S}$-VAE, is more suitable for capturing data with a hyperspherical latent structure, while outperforming a normal, $\mathcal{N}$-VAE, in low dimensions on other data types. Code at http://github.com/nicola-decao/s-vae-tf and https://github.com/nicola-decao/s-vae-pytorch

Discussion (0). Sign in to comment.

Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 104 citations worldwide. Full citation record

  1. Exact Likelihood and Sampling for Riemannian Gaussian Distributions on Correlation Matrices

    stat.ME 2026-08 conditional novelty 8.0 of 10

    A quotient-affine Riemannian Gaussian on full-rank correlation matrices has finite moments, exact p=2/Fisher likelihood, and a center-dependent normalizer for p=3 that separates MLE from Fréchet estimation.

  2. Stable Autoregressive Speech Generation with Low-Frame-Rate High-Dimensional Continuous Tokens

    eess.AS 2026-07 conditional novelty 6.0 of 10

    Autoregressive TTS from 8-Hz, 768-dimensional continuous tokens works when the tokenizer shapes its latent space with a low-dimensional core and an energy hierarchy, and the generator separates guidance into local, se...

  3. Think Through a Bottleneck: Hourglass Reasoning for Rigorous Induction

    cs.AI 2026-07 conditional novelty 6.0 of 10

    Strict stage isolation that passes only a compressed symbolic schema and rule between LLM calls improves few-shot inductive reasoning more than self-refinement or explicit verbalization alone.

  4. Hyperspherical Variational Autoencoders Using Efficient Spherical Cauchy Distribution

    stat.ML 2025-06 accept novelty 6.0 of 10

    Spherical Cauchy latent variables give hyperspherical VAEs an exact Möbius reparameterization and stable, Bessel-free KL evaluation, matching vMF locally while running faster and remaining stable in high dimensions.

  5. Intent Recognition and Out-of-Scope Detection using LLMs in Multi-party Conversations

    cs.CL 2025-07 conditional novelty 5.0 of 10

    Using BERT probabilities to shrink the label list in LLM prompts improves intent recognition in multi-party dialogues and cuts latency, though gains vary by model and dataset.

  6. Heterogeneous Federated Learning with Prototype Alignment and Upscaling

    cs.LG 2025-07 conditional novelty 3.0 of 10

    ProtoNorm adds server-side prototype alignment and a per-dataset scaling factor to FedProto-style federated learning, improving accuracy but with the gain largely driven by the tuned scaling factor.

Pith tools