495 citations · 810 across the 31 of their papers we have counts for
31 papers
LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund +2
In this paper, we introduce LInK, a novel framework that integrates contrastive learning of performance and design space with optimization techniques for solving complex inverse pr…
Improving Tuning-Free Real Image Editing with Proximal Guidance
Ligong Han, Song Wen, Qi Chen +13
DDIM inversion has revealed the remarkable potential of real image editing within diffusion-based methods. However, the accuracy of DDIM reconstruction degrades as larger classifie…
Identifiability Guarantees for Causal Disentanglement from Soft Interventions
Jiaqi Zhang, Chandler Squires, Kristjan Greenewald +3
Causal disentanglement aims to uncover a representation of data using latent variables that are interrelated through a causal model. Such a representation is identifiable if the la…
Constraining Generative Models for Engineering Design with Negative Data
Lyle Regenwetter, Giorgio Giannone, Akash Srivastava +2
Generative models have recently achieved remarkable success and widespread adoption in society, yet they often struggle to generate realistic and accurate outputs. This challenge e…
A Probabilistic Framework for Modular Continual Learning
Lazar Valkov, Akash Srivastava, Swarat Chaudhuri +1
Modular approaches that use a different composition of modules for each problem are a promising direction in continual learning (CL). However, searching through the large, discrete…
Aligning Optimization Trajectories with Diffusion Models for Constrained Design Generation
Giorgio Giannone, Akash Srivastava, Ole Winther +1
Generative models have had a profound impact on vision and language, paving the way for a new era of multimodal generative applications. While these successes have inspired researc…
Post-processing Private Synthetic Data for Improving Utility on Selected Measures
Hao Wang, Shivchander Sudalairaj, John Henning +2
Existing private synthetic data generation algorithms are agnostic to downstream tasks. However, end users may have specific requirements that the synthetic data must satisfy. Fail…
Estimating the Density Ratio between Distributions with High Discrepancy using Multinomial Logistic Regression
Akash Srivastava, Seungwook Han, Kai Xu +2
Functions of the ratio of the densities are widely used in machine learning to quantify the discrepancy between the two distributions and . For high-dimensional distri…
Constructive Assimilation: Boosting Contrastive Learning Performance through View Generation Strategies
Ligong Han, Seungwook Han, Shivchander Sudalairaj +8
Transformations based on domain expertise (expert transformations), such as random-resized-crop and color-jitter, have proven critical to the success of contrastive learning techni…
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Charlotte Loh, Seungwook Han, Shivchander Sudalairaj +5
Deep ensembles (DE) have been successful in improving model performance by learning diverse members via the stochasticity of random initialization. While recent works have attempte…
Beyond Statistical Similarity: Rethinking Metrics for Deep Generative Models in Engineering Design
Lyle Regenwetter, Akash Srivastava, Dan Gutfreund +1
Deep generative models such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Diffusion Models, and Transformers, have shown great promise in a variety of…
Modelling the performance of delivery vehicles across urban micro-regions to accelerate the transition to cargo-bike logistics
Max Schrader, Navish Kumar, Nicolas Collignon +5
Light goods vehicles (LGV) used extensively in the last mile of delivery are one of the leading polluters in cities. Cargo-bike logistics has been put forward as a high impact cand…
On the Importance of Calibration in Semi-supervised Learning
Charlotte Loh, Rumen Dangovski, Shivchander Sudalairaj +5
State-of-the-art (SOTA) semi-supervised learning (SSL) methods have been highly successful in leveraging a mix of labeled and unlabeled data by combining techniques of consistency…
LINKS: A dataset of a hundred million planar linkage mechanisms for data-driven kinematic design
Amin Heyrani Nobari, Akash Srivastava, Dan Gutfreund +1
In this paper, we introduce LINKS, a dataset of 100 million one degree of freedom planar linkage mechanisms and 1.1 billion coupler curves, which is more than 1000 times larger tha…
Equivariant Contrastive Learning
Rumen Dangovski, Li Jing, Charlotte Loh +5
In state-of-the-art self-supervised learning (SSL) pre-training produces semantically good representations by encouraging them to be invariant under meaningful transformations pres…
Targeted Neural Dynamical Modeling
Cole Hurwitz, Akash Srivastava, Kai Xu +4
Latent dynamics models have emerged as powerful tools for modeling and interpreting neural population activity. Recently, there has been a focus on incorporating simultaneously mea…
Security Assessment Rating Framework for Enterprises using MITRE ATT&CK Matrix
Hardik Manocha, Akash Srivastava, Chetan Verma +2
Threats targeting cyberspace are becoming more prominent and intelligent day by day. This inherently leads to a dire demand for continuous security validation and testing. Using th…
not-so-BigGAN: Generating High-Fidelity Images on Small Compute with Wavelet-based Super-Resolution
Seungwook Han, Akash Srivastava, Cole Hurwitz +2
State-of-the-art models for high-resolution image generation, such as BigGAN and VQVAE-2, require an incredible amount of compute resources and/or time (512 TPU-v3 cores) to train,…
Improving the Reconstruction of Disentangled Representation Learners via Multi-Stage Modeling
Akash Srivastava, Yamini Bansal, Yukun Ding +15
Current autoencoder-based disentangled representation learning methods achieve disentanglement by penalizing the (aggregate) posterior to encourage statistical independence of the…
Sequential Transfer Machine Learning in Networks: Measuring the Impact of Data and Neural Net Similarity on Transferability
Robin Hirt, Akash Srivastava, Carlos Berg +1
In networks of independent entities that face similar predictive tasks, transfer machine learning enables to re-use and improve neural nets using distributed data sets without the…
SimVAE: Simulator-Assisted Training forInterpretable Generative Models
Akash Srivastava, Jessie Rosenberg, Dan Gutfreund +1
This paper presents a simulator-assisted training method (SimVAE) for variational autoencoders (VAE) that leads to a disentangled and interpretable latent space. Training SimVAE is…
BreGMN: scaled-Bregman Generative Modeling Networks
Akash Srivastava, Kristjan Greenewald, Farzaneh Mirzazadeh
The family of f-divergences is ubiquitously applied to generative modeling in order to adapt the distribution of the model to that of the data. Well-definedness of f-divergences, h…
Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference
Cole L. Hurwitz, Kai Xu, Akash Srivastava +2
Determining the positions of neurons in an extracellular recording is useful for investigating functional properties of the underlying neural circuitry. In this work, we present a…
Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
Mohammad Emtiyaz Khan, Didrik Nielsen, Voot Tangkaratt +3
Uncertainty computation in deep learning is essential to design robust and reliable systems. Variational inference (VI) is a promising approach for such computation, but requires m…