196 citations · 333 across the 14 of their papers we have counts for
14 papers
Any-Property-Conditional Molecule Generation with Self-Criticism using Spanning Trees
Alexia Jolicoeur-Martineau, Aristide Baratin, Kisoo Kwon +2
Generating novel molecules is challenging, with most representations leading to generative models producing many invalid molecules. Spanning Tree-based Graph Generation (STGG) is a…
Graph Neural Networks for Learning Equivariant Representations of Neural Networks
Miltiadis Kofinas, Boris Knyazev, Yan Zhang +5
Neural networks that process the parameters of other neural networks find applications in domains as diverse as classifying implicit neural representations, generating neural netwo…
Unsupervised Concept Discovery Mitigates Spurious Correlations
Md Rifat Arefin, Yan Zhang, Aristide Baratin +4
Models prone to spurious correlations in training data often produce brittle predictions and introduce unintended biases. Addressing this challenge typically involves methods relyi…
Improved Generalization of Weight Space Networks via Augmentations
Aviv Shamsian, Aviv Navon, David W. Zhang +4
Learning in deep weight spaces (DWS), where neural networks process the weights of other neural networks, is an emerging research direction, with applications to 2D and 3D neural f…
Object-centric architectures enable efficient causal representation learning
Amin Mansouri, Jason Hartford, Yan Zhang +1
Causal representation learning has showed a variety of settings in which we can disentangle latent variables with identifiability guarantees (up to some reasonable equivalence clas…
PopulAtion Parameter Averaging (PAPA)
Alexia Jolicoeur-Martineau, Emy Gervais, Kilian Fatras +2
Ensemble methods combine the predictions of multiple models to improve performance, but they require significantly higher computation costs at inference time. To avoid these costs,…
Unlocking Slot Attention by Changing Optimal Transport Costs
Yan Zhang, David W. Zhang, Simon Lacoste-Julien +2
Slot attention is a powerful method for object-centric modeling in images and videos. However, its set-equivariance limits its ability to handle videos with a dynamic number of obj…
CrossSplit: Mitigating Label Noise Memorization through Data Splitting
Jihye Kim, Aristide Baratin, Yan Zhang +1
We approach the problem of improving robustness of deep learning algorithms in the presence of label noise. Building upon existing label correction and co-teaching methods, we prop…
Equivariance with Learned Canonicalization Functions
Sékou-Oumar Kaba, Arnab Kumar Mondal, Yan Zhang +2
Symmetry-based neural networks often constrain the architecture in order to achieve invariance or equivariance to a group of transformations. In this paper, we propose an alternati…
Multiset-Equivariant Set Prediction with Approximate Implicit Differentiation
Yan Zhang, David W. Zhang, Simon Lacoste-Julien +2
Most set prediction models in deep learning use set-equivariant operations, but they actually operate on multisets. We show that set-equivariant functions cannot represent certain…
Deep Set Prediction Networks
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Current approaches for predicting sets from feature vectors ignore the unordered nature of sets and suffer from discontinuity issues as a result. We propose a general model for pre…
FSPool: Learning Set Representations with Featurewise Sort Pooling
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Traditional set prediction models can struggle with simple datasets due to an issue we call the responsibility problem. We introduce a pooling method for sets of feature vectors ba…
Learning Representations of Sets through Optimized Permutations
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Representations of sets are challenging to learn because operations on sets should be permutation-invariant. To this end, we propose a Permutation-Optimisation module that learns h…
Learning to Count Objects in Natural Images for Visual Question Answering
Yan Zhang, Jonathon Hare, Adam Prügel-Bennett
Visual Question Answering (VQA) models have struggled with counting objects in natural images so far. We identify a fundamental problem due to soft attention in these models as a c…