Unsupervised learning methods for molecular simulation data
Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …
amounts of data produced by atomistic and molecular simulations, in material science, solid …
A review of speaker diarization: Recent advances with deep learning
Speaker diarization is a task to label audio or video recordings with classes that correspond
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
to speaker identity, or in short, a task to identify “who spoke when”. In the early years …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
SpeechBrain: A general-purpose speech toolkit
SpeechBrain is an open-source and all-in-one speech toolkit. It is designed to facilitate the
research and development of neural speech processing technologies by being simple …
research and development of neural speech processing technologies by being simple …
Graph learning: A survey
Graphs are widely used as a popular representation of the network structure of connected
data. Graph data can be found in a broad spectrum of application domains such as social …
data. Graph data can be found in a broad spectrum of application domains such as social …
Expressive text-to-image generation with rich text
Plain text has become a prevalent interface for text-to-image synthesis. However, its limited
customization options hinder users from accurately describing desired outputs. For example …
customization options hinder users from accurately describing desired outputs. For example …
Inference and analysis of cell-cell communication using CellChat
Understanding global communications among cells requires accurate representation of cell-
cell signaling links and effective systems-level analyses of those links. We construct a …
cell signaling links and effective systems-level analyses of those links. We construct a …
Contrastive and non-contrastive self-supervised learning recover global and local spectral embedding methods
R Balestriero, Y LeCun - Advances in Neural Information …, 2022 - proceedings.neurips.cc
Abstract Self-Supervised Learning (SSL) surmises that inputs and pairwise positive
relationships are enough to learn meaningful representations. Although SSL has recently …
relationships are enough to learn meaningful representations. Although SSL has recently …
Gcc: Graph contrastive coding for graph neural network pre-training
Graph representation learning has emerged as a powerful technique for addressing real-
world problems. Various downstream graph learning tasks have benefited from its recent …
world problems. Various downstream graph learning tasks have benefited from its recent …
Adaptive universal generalized pagerank graph neural network
In many important graph data processing applications the acquired information includes
both node features and observations of the graph topology. Graph neural networks (GNNs) …
both node features and observations of the graph topology. Graph neural networks (GNNs) …