Search Results for author:
Found 12 papers, 11 papers with code
SURE: SUrvey REcipes for building reliable and robust deep networks
In this paper, we revisit techniques for uncertainty estimation within deep neural networks and consolidate a suite of techniques to enhance their reliability.
Ranked #1 on
Learning with noisy labels
on ANIMAL
Endowing Protein Language Models with Structural Knowledge
Drawing from recent advances in graph transformers, our approach refines the self-attention mechanisms of pretrained language transformers by integrating structural information with structure extractor modules.
Fisher Information Embedding for Node and Graph Learning
Attention-based graph neural networks (GNNs), such as graph attention networks (GATs), have become popular neural architectures for processing graph-structured data and learning node embeddings.
Unsupervised Manifold Alignment with Joint Multidimensional Scaling
Our approach integrates Multidimensional Scaling (MDS) and Wasserstein Procrustes analysis into a joint optimization problem to simultaneously generate isometric embeddings of data and learn correspondences between instances from two different datasets, while only requiring intra-dataset pairwise dissimilarities as input.
Approximate Network Motif Mining Via Graph Learning
Frequent and structurally related subgraphs, also known as network motifs, are valuable features of many graph datasets.
Structure-Aware Transformer for Graph Representation Learning
Here, we show that the node representations generated by the Transformer with positional encoding do not necessarily capture structural similarity between them.
Ranked #4 on
Graph Property Prediction
on ogbg-code2
Emotion Recognition in Conversation
Graph Representation Learning
GraphiT: Encoding Graph Structure in Transformers
We show that viewing graphs as sets of node features and incorporating structural and positional information into a transformer architecture is able to outperform representations learned with classical graph neural networks (GNNs).
A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention
We address the problem of learning on sets of features, motivated by the need of performing pooling operations in long biological sequences of varying sizes, with long-range dependencies, and possibly few labeled data.
Convolutional Kernel Networks for Graph-Structured Data
On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks.
MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning
Since deep networks are capable of memorizing the entire dataset, the corrupted samples generated by vanilla MixUp with a badly chosen interpolation policy will degrade the performance of networks.
Recurrent Kernel Networks
Substring kernels are classical tools for representing biological sequences or text.
A Kernel Perspective for Regularizing Deep Neural Networks
We propose a new point of view for regularizing deep neural networks by using the norm of a reproducing kernel Hilbert space (RKHS).
