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02/09/2023
Feature Likelihood Score: Evaluating Generalization of Generative Models Using Samples
Deep generative models have demonstrated the ability to generate complex...
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08/16/2022
Riemannian Diffusion Models
Diffusion models are recent state-of-the-art methods for image generatio...
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10/16/2021
Equivariant Discrete Normalizing Flows
At its core, generative modeling seeks to uncover the underlying factors...
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04/17/2021
Neural Path Hunter: Reducing Hallucination in Dialogue Systems via Path Grounding
Dialogue systems powered by large pre-trained language models (LM) exhib...
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03/02/2021
Online Adversarial Attacks
Adversarial attacks expose important vulnerabilities of deep learning mo...
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09/23/2020
Structure Aware Negative Sampling in Knowledge Graphs
Learning low-dimensional representations for entities and relations in k...
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07/01/2020
Adversarial Example Games
The existence of adversarial examples capable of fooling trained neural ...
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02/15/2020
Latent Variable Modelling with Hyperbolic Normalizing Flows
The choice of approximate posterior distributions plays a central role i...
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12/20/2019
Meta-Graph: Few shot Link Prediction via Meta Learning
Fast adaptation to new data is one key facet of human intelligence and i...
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06/06/2019
Improving Exploration in Soft-Actor-Critic with Normalizing Flows Policies
Deep Reinforcement Learning (DRL) algorithms for continuous action space...
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05/28/2019
A Cross-Domain Transferable Neural Coherence Model
Coherence is an important aspect of text quality and is crucial for ensu...
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05/26/2019
Generalizable Adversarial Attacks Using Generative Models
Adversarial attacks on deep neural networks traditionally rely on a cons...
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05/25/2019
Compositional Fairness Constraints for Graph Embeddings
Learning high-quality node embeddings is a key building block for machin...
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05/31/2018