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09/11/2023
On the Fine-Grained Hardness of Inverting Generative Models
The objective of generative model inversion is to identify a size-n late...
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09/04/2023
Towards Foundational AI Models for Additive Manufacturing: Language Models for G-Code Debugging, Manipulation, and Comprehension
3D printing or additive manufacturing is a revolutionary technology that...
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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...
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08/03/2023
Circumventing Concept Erasure Methods For Text-to-Image Generative Models
Text-to-image generative models can produce photo-realistic images for a...
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07/17/2023
Identity-Preserving Aging of Face Images via Latent Diffusion Models
The performance of automated face recognition systems is inevitably impa...
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06/16/2023
Vision-Language Models can Identify Distracted Driver Behavior from Naturalistic Videos
Recognizing the activities, causing distraction, in real-world driving s...
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06/14/2023
ZeroForge: Feedforward Text-to-Shape Without 3D Supervision
Current state-of-the-art methods for text-to-shape generation either req...
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02/12/2023
LiT Tuned Models for Efficient Species Detection
Recent advances in training vision-language models have demonstrated unp...
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01/29/2023
Implicit Regularization for Group Sparsity
We study the implicit regularization of gradient descent towards structu...
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01/17/2023
Pathfinding Neural Cellular Automata
Pathfinding makes up an important sub-component of a broad range of comp...
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11/07/2022
Neural PDE Solvers for Irregular Domains
Neural network-based approaches for solving partial differential equatio...
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10/24/2022
Active Learning for Single Neuron Models with Lipschitz Non-Linearities
We consider the problem of active learning for single neuron models, als...
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10/13/2022
Caption supervision enables robust learners
Vision language models like CLIP are robust to natural distribution shif...
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10/12/2022
Gotcha: A Challenge-Response System for Real-Time Deepfake Detection
The integrity of online video interactions is threatened by the widespre...
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09/21/2022
Distributed Online Non-convex Optimization with Composite Regret
Regret has been widely adopted as the metric of choice for evaluating th...
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09/11/2022
On The Computational Complexity of Self-Attention
Transformer architectures have led to remarkable progress in many state-...
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06/17/2022
Revisiting Self-Distillation
Knowledge distillation is the procedure of transferring "knowledge" from...
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06/15/2022
A Meta-Analysis of Distributionally-Robust Models
State-of-the-art image classifiers trained on massive datasets (such as ...
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05/12/2022
Smooth-Reduce: Leveraging Patches for Improved Certified Robustness
Randomized smoothing (RS) has been shown to be a fast, scalable techniqu...
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02/04/2022
Selective Network Linearization for Efficient Private Inference
Private inference (PI) enables inference directly on cryptographically s...
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12/06/2021
MDPGT: Momentum-based Decentralized Policy Gradient Tracking
We propose a novel policy gradient method for multi-agent reinforcement ...
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10/08/2021
Adversarial Token Attacks on Vision Transformers
Vision transformers rely on a patch token based self attention mechanism...
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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...
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10/04/2021
Differentiable Spline Approximations
The paradigm of differentiable programming has significantly enhanced th...
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08/12/2021
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
In this paper, we study the implicit bias of gradient descent for sparse...
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06/17/2021
Sphynx: ReLU-Efficient Network Design for Private Inference
The emergence of deep learning has been accompanied by privacy concerns ...
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05/13/2021
Provably Convergent Algorithms for Solving Inverse Problems Using Generative Models
The traditional approach of hand-crafting priors (such as sparsity) for ...
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04/29/2021
Distributed Multigrid Neural Solvers on Megavoxel Domains
We consider the distributed training of large-scale neural networks that...
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03/02/2021
Cross-Gradient Aggregation for Decentralized Learning from Non-IID data
Decentralized learning enables a group of collaborative agents to learn ...
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02/25/2021
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Deep generative models have emerged as a powerful class of priors for si...
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08/27/2020
Adversarially Robust Learning via Entropic Regularization
In this paper we propose a new family of algorithms for training adversa...
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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...
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07/07/2020
Hyperparameter Optimization in Neural Networks via Structured Sparse Recovery
In this paper, we study two important problems in the automated design o...
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06/28/2020
ESPN: Extremely Sparse Pruned Networks
Deep neural networks are often highly overparameterized, prohibiting the...
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11/27/2019
Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis
A remarkable recent discovery in machine learning has been that deep neu...
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10/15/2019
On Higher-order Moments in Adam
In this paper, we investigate the popular deep learning optimization rou...
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09/05/2019
Spatiotemporally Constrained Action Space Attacks on Deep Reinforcement Learning Agents
Robustness of Deep Reinforcement Learning (DRL) algorithms towards adver...
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06/20/2019
Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors
Deep neural networks as image priors have been recently introduced for p...
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06/07/2019
One-Shot Neural Architecture Search via Compressive Sensing
Neural architecture search (NAS), or automated design of neural network ...
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06/04/2019
Encoding Invariances in Deep Generative Models
Reliable training of generative adversarial networks (GANs) typically re...
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04/24/2019
Reducing The Search Space For Hyperparameter Optimization Using Group Sparsity
We propose a new algorithm for hyperparameter selection in machine learn...
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04/17/2019
Semantic Adversarial Attacks: Parametric Transformations That Fool Deep Classifiers
Deep neural networks have been shown to exhibit an intriguing vulnerabil...
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04/11/2019
A Kaczmarz Algorithm for Solving Tree Based Distributed Systems of Equations
The Kaczmarz algorithm is an iterative method for solving systems of lin...
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03/07/2019
Alternating Phase Projected Gradient Descent with Generative Priors for Solving Compressive Phase Retrieval
The classical problem of phase retrieval arises in various signal acquis...
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12/03/2018
Signal Reconstruction from Modulo Observations
We consider the problem of reconstructing a signal from under-determined...
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11/21/2018
Physics-aware Deep Generative Models for Creating Synthetic Microstructures
A key problem in computational material science deals with understanding...
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10/08/2018
Algorithmic Aspects of Inverse Problems Using Generative Models
The traditional approach of hand-crafting priors (such as sparsity) for ...
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07/17/2018
Phase Retrieval for Signals in Union of Subspaces
We consider the phase retrieval problem for signals that belong to a uni...
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06/20/2018
Learning ReLU Networks via Alternating Minimization
We propose and analyze a new family of algorithms for training neural ne...
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06/02/2018
