Ameya Joshi | DeepAI

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Featured Co-authors

J. Zico Kolter
94 publications
Minsu Cho
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Soumik Sarkar
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Chinmay Hegde
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Siddharth Garg
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Baskar Ganapathysubramanian
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Anuj Sharma
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Aditya Balu
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Senem Velipasalar
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Brandon Reagen
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Nasir Memon
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∙ 08/07/2023

Distributionally Robust Classification on a Data Budget

Real world uses of deep learning require predictable model behavior unde...

0 Benjamin Feuer, et al. ∙

∙ 07/17/2023

Identity-Preserving Aging of Face Images via Latent Diffusion Models

The performance of automated face recognition systems is inevitably impa...

0 Sudipta Banerjee, et al. ∙

∙ 06/16/2023

Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos

Recognizing the activities, causing distraction, in real-world driving s...

0 Md. Zahid Hasan, et al. ∙

∙ 06/14/2023

ZeroForge: Feedforward Text-to-Shape Without 3D Supervision

Current state-of-the-art methods for text-to-shape generation either req...

10 Kelly O. Marshall, et al. ∙

∙ 10/13/2022

Caption supervision enables robust learners

Vision language models like CLIP are robust to natural distribution shif...

1 Benjamin Feuer, et al. ∙

∙ 06/17/2022

Revisiting Self-Distillation

Knowledge distillation is the procedure of transferring "knowledge" from...

4 Minh Pham, et al. ∙

∙ 06/15/2022

A Meta-Analysis of Distributionally-Robust Models

State-of-the-art image classifiers trained on massive datasets (such as ...

3 Benjamin Feuer, et al. ∙

∙ 05/12/2022

Smooth-Reduce: Leveraging Patches for Improved Certified Robustness

Randomized smoothing (RS) has been shown to be a fast, scalable techniqu...

14 Ameya Joshi, et al. ∙

∙ 02/04/2022

Selective Network Linearization for Efficient Private Inference

Private inference (PI) enables inference directly on cryptographically s...

1 Minsu Cho, et al. ∙

∙ 10/08/2021

Adversarial Token Attacks on Vision Transformers

Vision transformers rely on a patch token based self attention mechanism...

14 Ameya Joshi, et al. ∙

∙ 10/04/2021

NeuFENet: Neural Finite Element Solutions with Theoretical Bounds for Parametric PDEs

We consider a mesh-based approach for training a neural network to produ...

11 Biswajit Khara, et al. ∙

∙ 10/04/2021

Differentiable Spline Approximations

The paradigm of differentiable programming has significantly enhanced th...

24 Minsu Cho, et al. ∙

∙ 07/24/2020

Deep Generative Models that Solve PDEs: Distributed Computing for Training Large Data-Free Models

Recent progress in scientific machine learning (SciML) has opened up the...

14 Sergio Botelho, et al. ∙

∙ 06/28/2020

ESPN: Extremely Sparse Pruned Networks

Deep neural networks are often highly overparameterized, prohibiting the...

17 Minsu Cho, et al. ∙

∙ 06/04/2019

Encoding Invariances in Deep Generative Models

Reliable training of generative adversarial networks (GANs) typically re...

5 Ameya Joshi, et al. ∙

∙ 04/17/2019

Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers

Deep neural networks have been shown to exhibit an intriguing vulnerabil...

14 Ameya Joshi, et al. ∙

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