Akash Srivastava | DeepAI

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∙ 09/15/2023

Compositional Foundation Models for Hierarchical Planning

To make effective decisions in novel environments with long-horizon goal...

0 Anurag Ajay, et al. ∙

∙ 07/12/2023

Identifiability Guarantees for Causal Disentanglement from Soft Interventions

Causal disentanglement aims to uncover a representation of data using la...

0 Jiaqi Zhang, et al. ∙

∙ 06/27/2023

Learning from Invalid Data: On Constraint Satisfaction in Generative Models

Generative models have demonstrated impressive results in vision, langua...

0 Giorgio Giannone, et al. ∙

∙ 06/11/2023

A Probabilistic Framework for Modular Continual Learning

Modular approaches, which use a different composition of modules for eac...

0 Lazar Valkov, et al. ∙

∙ 06/08/2023

Improving Negative-Prompt Inversion via Proximal Guidance

DDIM inversion has revealed the remarkable potential of real image editi...

0 Ligong Han, et al. ∙

∙ 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...

0 Giorgio Giannone, et al. ∙

∙ 05/24/2023

Post-processing Private Synthetic Data for Improving Utility on Selected Measures

Existing private synthetic data generation algorithms are agnostic to do...

0 Hao Wang, et al. ∙

∙ 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...

1 Akash Srivastava, et al. ∙

∙ 04/02/2023

Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies

Transformations based on domain expertise (expert transformations), such...

8 Ligong Han, et al. ∙

∙ 03/04/2023

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries

Deep ensembles (DE) have been successful in improving model performance ...

0 Charlotte Loh, et al. ∙

∙ 02/06/2023

Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design

Deep generative models, such as Variational Autoencoders (VAEs), Generat...

0 Lyle Regenwetter, et al. ∙

∙ 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...

0 Max Schrader, et al. ∙

∙ 10/10/2022

On the Importance of Calibration in Semi-supervised Learning

State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been...

0 Charlotte Loh, et al. ∙

∙ 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...

2 Amin Heyrani Nobari, et al. ∙

∙ 08/14/2021

Security Assessment Rating Framework for Enterprises using MITRE ATT CK Matrix

Threats targeting cyberspace are becoming more prominent and intelligent...

0 Hardik Manocha, et al. ∙

∙ 10/25/2020

Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modelling

Current autoencoder-based disentangled representation learning methods a...

8 Akash Srivastava, et al. ∙

∙ 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...

14 Seungwook Han, et al. ∙

∙ 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, ...

4 Robin Hirt, et al. ∙

∙ 11/19/2019

SimVAE: Simulator-Assisted Training forInterpretable Generative Models

This paper presents a simulator-assisted training method (SimVAE) for va...

11 Akash Srivastava, et al. ∙

∙ 06/01/2019

BreGMN: scaled-Bregman Generative Modeling Networks

The family of f-divergences is ubiquitously applied to generative modeli...

0 Akash Srivastava, et al. ∙

∙ 05/29/2019

Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference

Extracellular recordings using modern, dense probes provide detailed foo...

0 Cole L. Hurwitz, et al. ∙

∙ 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...

0 Mohammad Emtiyaz Khan, et al. ∙

∙ 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...

0 Akash Srivastava, et al. ∙

∙ 04/21/2018

Variational Inference In Pachinko Allocation Machines

The Pachinko Allocation Machine (PAM) is a deep topic model that allows ...

0 Akash Srivastava, et al. ∙

∙ 03/31/2018

Synthesis of Differentiable Functional Programs for Lifelong Learning

We present a neurosymbolic approach to the lifelong learning of algorith...

0 Lazar Valkov, et al. ∙

∙ 05/22/2017

VEEGAN: Reducing Mode Collapse in GANs using Implicit Variational Learning

Deep generative models provide powerful tools for distributions over com...

0 Akash Srivastava, et al. ∙

∙ 03/04/2017

Autoencoding Variational Inference For Topic Models

Topic models are one of the most popular methods for learning representa...

0 Akash Srivastava, et al. ∙

∙ 06/19/2016

Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation

A good clustering can help a data analyst to explore and understand a da...

0 Akash Srivastava, et al. ∙

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