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Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
In this work, we define a diffusion-based generative model capable of both music synthesis and source separation by learning the score …
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Multi-Source Diffusion Models for Simultaneous Music Generation and Separation
ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
CLIP proved that aligning visual and language spaces is key to solving many vision tasks without explicit training, but required to …
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ASIF: Coupled Data Turns Unimodal Models to Multimodal Without Training
Accelerating Transformer Inference for Translation via Parallel Decoding
Autoregressive decoding limits the efficiency of transformers for Machine Translation (MT). The community proposed specific network …
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Accelerating Transformer Inference for Translation via Parallel Decoding
Relative representations enable zero-shot latent space communication
Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. …
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Relative representations enable zero-shot latent space communication
Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Predicting interactions between proteins and other biomolecules solely based on structure remains a challenge in biology. A high-level …
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
LIMP: Learning Latent Shape Representations with Metric Preservation Priors
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D …
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LIMP: Learning Latent Shape Representations with Metric Preservation Priors
Isospectralization, or how to hear shape, style, and correspondence
The question whether one can recover the shape of a geometric object from its Laplacian spectrum (‘hear the shape of the …
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Isospectralization, or how to hear shape, style, and correspondence
Deep Functional Maps: Structured Prediction for Dense Shape Correspondence
We introduce a new framework for learning dense correspondence between deformable 3D shapes. Existing learning based approaches model …
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Deep Functional Maps: Structured Prediction for Dense Shape Correspondence