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C2M3: Cycle-Consistent Multi-Model Merging
In this paper, we present a novel data-free method for merging neural networks in weight space. Differently from most existing works, …
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C2M3: Cycle-Consistent Multi-Model Merging
Latent Functional Maps
Neural models learn data representations that lie on low-dimensional manifolds, yet modeling the relation between these …
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Latent Functional Maps
ReMatching: Low-Resolution Representations for Scalable Shape Correspondence
We introduce ReMatching, a novel shape correspondence solution based on the functional maps framework. Our method, by exploiting a new …
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ReMatching: Low-Resolution Representations for Scalable Shape Correspondence
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
SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis
Sound design involves creatively selecting, recording, and editing sound effects for various media like cinema, video games, and …
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SyncFusion: Multimodal Onset-synchronized Video-to-Audio Foley Synthesis
Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models
Multi-Source Diffusion Models (MSDM) allow for compositional musical generation tasks: generating a set of coherent sources, creating …
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Generalized Multi-Source Inference for Text Conditioned Music Diffusion Models
Camoscio: an Italian Instruction-tuned LLaMA
In recent years Large Language Models (LLMs) have increased the state of the art on several natural language processing tasks. However, …
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Camoscio: an Italian Instruction-tuned LLaMA
Latent Space Translation via Semantic Alignment
Different neural models often exhibit similar latent spaces when exposed to semantically similar data; however, this inherent …
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Latent Space Translation via Semantic Alignment
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings. Mostly building on …
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Leveraging sparse and shared feature activations for disentangled representation learning
AVEN-GR: Attribute Value Extraction and Normalization using product GRaphs
Getting a good understanding of the user intent is vital for e-commerce applications to surface the right product to a given customer …
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AVEN-GR: Attribute Value Extraction and Normalization using product GRaphs