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07/04/2023
Learning Discrete Weights and Activations Using the Local Reparameterization Trick
In computer vision and machine learning, a crucial challenge is to lower...
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06/19/2023
GD-VDM: Generated Depth for better Diffusion-based Video Generation
The field of generative models has recently witnessed significant progre...
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06/05/2023
LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
Lip-to-speech involves generating a natural-sounding speech synchronized...
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05/30/2023
DisCLIP: Open-Vocabulary Referring Expression Generation
Referring Expressions Generation (REG) aims to produce textual descripti...
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02/19/2023
Guided Deep Kernel Learning
Combining Gaussian processes with the expressive power of deep neural ne...
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01/31/2023
Auxiliary Learning as an Asymmetric Bargaining Game
Auxiliary learning is an effective method for enhancing the generalizati...
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01/30/2023
Equivariant Architectures for Learning in Deep Weight Spaces
Designing machine learning architectures for processing neural networks ...
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09/04/2022
Communication Efficient Distributed Learning over Wireless Channels
Vertical distributed learning exploits the local features collected by m...
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06/22/2022
A Study on the Evaluation of Generative Models
Implicit generative models, which do not return likelihood values, such ...
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06/05/2022
Functional Ensemble Distillation
Bayesian models have many desirable properties, most notable is their ab...
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02/02/2022
Multi-Task Learning as a Bargaining Game
In Multi-task learning (MTL), a joint model is trained to simultaneously...
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10/11/2021
Can Stochastic Gradient Langevin Dynamics Provide Differential Privacy for Deep Learning?
Bayesian learning via Stochastic Gradient Langevin Dynamics (SGLD) has b...
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06/29/2021
Personalized Federated Learning with Gaussian Processes
Federated learning aims to learn a global model that performs well on cl...
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03/08/2021
Personalized Federated Learning using Hypernetworks
Personalized federated learning is tasked with training machine learning...
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02/15/2021
GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning
Gaussian processes (GPs) are non-parametric, flexible, models that work ...
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10/22/2020
Position-Agnostic Multi-Microphone Speech Dereverberation
Neural networks (NNs) have been widely applied in speech processing task...
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10/17/2020
On Size Generalization in Graph Neural Networks
Graph neural networks (GNNs) can process graphs of different sizes but t...
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10/08/2020
Learning the Pareto Front with Hypernetworks
Multi-objective optimization problems are prevalent in machine learning....
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03/04/2020
Restoration of Fragmentary Babylonian Texts Using Recurrent Neural Networks
The main source of information regarding ancient Mesopotamian history an...
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02/20/2020
On Learning Sets of Symmetric Elements
Learning from unordered sets is a fundamental learning setup, which is a...
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06/04/2019
Conditional Generative Models are not Robust
Class-conditional generative models are an increasingly popular approach...
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05/28/2019
Evaluating and Calibrating Uncertainty Prediction in Regression Tasks
Predicting not only the target but also an accurate measure of uncertain...
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01/27/2019
On the Universality of Invariant Networks
Constraining linear layers in neural networks to respect symmetry transf...
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10/16/2018
Incremental Few-Shot Learning with Attention Attractor Networks
Machine learning classifiers are often trained to recognize a set of pre...
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09/08/2018
Neural Guided Constraint Logic Programming for Program Synthesis
Synthesizing programs using example input/outputs is a classic problem i...
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03/21/2018
Inference in Probabilistic Graphical Models by Graph Neural Networks
A useful computation when acting in a complex environment is to infer th...
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03/16/2018
Reviving and Improving Recurrent Back-Propagation
In this paper, we revisit the recurrent back-propagation (RBP) algorithm...
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02/13/2018
Neural Relational Inference for Interacting Systems
Interacting systems are prevalent in nature, from dynamical systems in p...
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10/21/2017
Learning Discrete Weights Using the Local Reparameterization Trick
Recent breakthroughs in computer vision make use of large deep neural ne...
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03/27/2016
Human Pose Estimation using Deep Consensus Voting
In this paper we consider the problem of human pose estimation from a si...
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10/20/2015
Unsupervised Ensemble Learning with Dependent Classifiers
In unsupervised ensemble learning, one obtains predictions from multiple...
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02/04/2015
Learning Local Invariant Mahalanobis Distances
For many tasks and data types, there are natural transformations to whic...
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06/10/2014