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12/28/2022
Continuous Depth Recurrent Neural Differential Equations
Recurrent neural networks (RNNs) have brought a lot of advancements in s...
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10/12/2022
Towards Generalized and Explainable Long-Range Context Representation for Dialogue Systems
Context representation is crucial to both dialogue understanding and gen...
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10/01/2022
HyperHawkes: Hypernetwork based Neural Temporal Point Process
Temporal point process serves as an essential tool for modeling time-to-...
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09/16/2022
Continual Learning with Dependency Preserving Hypernetworks
Humans learn continually throughout their lifespan by accumulating diver...
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12/29/2021
Bayesian Neural Hawkes Process for Event Uncertainty Prediction
Many applications comprise of sequences of event data with the time of o...
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12/23/2021
Bi-Directional Recurrent Neural Ordinary Differential Equations for Social Media Text Classification
Classification of posts in social media such as Twitter is difficult due...
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12/23/2021
Latent Time Neural Ordinary Differential Equations
Neural ordinary differential equations (NODE) have been proposed as a co...
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12/23/2021
Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations
Neural ordinary differential equations (NODE) have been proposed as a co...
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08/08/2021
Monte Carlo DropBlock for Modelling Uncertainty in Object Detection
With the advancements made in deep learning, computer vision problems li...
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06/04/2021
Adiabatic Quantum Feature Selection for Sparse Linear Regression
Linear regression is a popular machine learning approach to learn and pr...
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12/12/2020
Delay Differential Neural Networks
Neural ordinary differential equations (NODEs) treat computation of inte...
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11/12/2019
Learning Multi-Sense Word Distributions using Approximate Kullback-Leibler Divergence
Learning word representations has garnered greater attention in the rece...
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06/05/2018
Deep Gaussian Processes with Convolutional Kernels
Deep Gaussian processes (DGPs) provide a Bayesian non-parametric alterna...
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12/25/2014
