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09/15/2023
Compositional Foundation Models for Hierarchical Planning
To make effective decisions in novel environments with long-horizon goal...
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07/12/2023
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Causal disentanglement aims to uncover a representation of data using la...
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06/27/2023
Learning from Invalid Data: On Constraint Satisfaction in Generative Models
Generative models have demonstrated impressive results in vision, langua...
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06/11/2023
A Probabilistic Framework for Modular Continual Learning
Modular approaches, which use a different composition of modules for eac...
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06/08/2023
Improving Negative-Prompt Inversion via Proximal Guidance
DDIM inversion has revealed the remarkable potential of real image editi...
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05/29/2023
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
Generative models have had a profound impact on vision and language, pav...
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05/24/2023
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Existing private synthetic data generation algorithms are agnostic to do...
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05/01/2023
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Functions of the ratio of the densities p/q are widely used in machine l...
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04/02/2023
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Transformations based on domain expertise (expert transformations), such...
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03/04/2023
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Deep ensembles (DE) have been successful in improving model performance ...
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02/06/2023
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
Deep generative models, such as Variational Autoencoders (VAEs), Generat...
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01/30/2023
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
Light goods vehicles (LGV) used extensively in the last mile of delivery...
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10/10/2022
On the Importance of Calibration in Semi-supervised Learning
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been...
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08/30/2022
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
In this paper, we introduce LINKS, a dataset of 100 million one degree o...
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08/14/2021
Security Assessment Rating Framework for Enterprises using MITRE ATT CK Matrix
Threats targeting cyberspace are becoming more prominent and intelligent...
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10/25/2020
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling
Current autoencoder-based disentangled representation learning methods a...
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09/09/2020
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget
BigGAN is the state-of-the-art in high-resolution image generation, succ...
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03/29/2020
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
In networks of independent entities that face similar predictive tasks, ...
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11/19/2019
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
This paper presents a simulator-assisted training method (SimVAE) for va...
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06/01/2019
BreGMN: scaled-Bregman Generative Modeling Networks
The family of f-divergences is ubiquitously applied to generative modeli...
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05/29/2019
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Extracellular recordings using modern, dense probes provide detailed foo...
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06/13/2018
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Uncertainty computation in deep learning is essential to design robust a...
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05/31/2018
Ratio Matching MMD Nets: Low dimensional projections for effective deep generative models
Deep generative models can learn to generate realistic-looking images on...
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04/21/2018
Variational Inference In Pachinko Allocation Machines
The Pachinko Allocation Machine (PAM) is a deep topic model that allows ...
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03/31/2018
Synthesis of Differentiable Functional Programs for Lifelong Learning
We present a neurosymbolic approach to the lifelong learning of algorith...
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05/22/2017
VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning
Deep generative models provide powerful tools for distributions over com...
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03/04/2017
Autoencoding Variational Inference For Topic Models
Topic models are one of the most popular methods for learning representa...
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06/19/2016