1

Latent Autoregressive Source Separation
Autoregressive models have achieved impressive results over a wide range of domains in terms of generation quality and downstream task …
Cite arXiv GitHub
Latent Autoregressive Source Separation
Metric Based Few-Shot Graph Classification
Few-shot graph classification is a novel yet promising emerging research field that still lacks the soundness of well-established …
Cite URL PDF GitHub
Metric Based Few-Shot Graph Classification
Play música alegre: A Large-Scale Empirical Analysis of Cross-Lingual Phenomena in Voice Assistant Interactions
Cross-lingual phenomena are quite common in informal contexts like social media, where users are likely to mix their native language …
Cite URL
Play música alegre: A Large-Scale Empirical Analysis of Cross-Lingual Phenomena in Voice Assistant Interactions
CLIP-Forge: Towards Zero-Shot Text-To-Shape Generation
Generating shapes using natural language can enable new ways of imagining and creating the things around us. While significant recent …
Cite
3D Human Pose Estimation Using Mobius Graph Convolutional Networks
Cite
Errare humanum est? a pilot study to evaluate the human-likeness of a AI othello playing agent
Olivaw is an AI Othello playing agent which autonomously learns how to improve its gameplay by playing against itself. Some top-notch …
Cite
GIM3D: A 3D Dataset for Garment Segmentation
The 3D cloth segmentation task is particularly challenging due to the extreme variation of shapes, even among the same category of …
Cite DOI URL GitHub
GIM3D: A 3D Dataset for Garment Segmentation
Neural Implicit Style-net: synthesizing shapes in a preferred style exploiting self supervision
We introduce a novel approach to disentangle style from content in the 3D domain and perform unsupervised neural style transfer. Our …
Cite
PC-GAU: PCA Basis of Scattered Gaussians for Shape Matching via Functional Maps
Shape matching is a central problem in geometry processing applications, ranging from texture transfer to statistical shape analysis. …
Cite DOI URL GitHub
Learning disentangled representations via product manifold projection
We propose a novel approach to disentangle the generative factors of variation underlying a given set of observations. Our method …
Cite URL