Search Results for author:
Found 36 papers, 21 papers with code
Fake It Until You Break It: On the Adversarial Robustness of AI-generated Image Detectors
While generative AI (GenAI) offers countless possibilities for creative and productive tasks, artificially generated media can be misused for fraud, manipulation, scams, misinformation campaigns, and more.
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation.
Gujarati-English Code-Switching Speech Recognition using ensemble prediction of spoken language
An important and difficult task in code-switched speech recognition is to recognize the language, as lots of words in two languages can sound similar, especially in some accents.
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies
We develop a nonparametric identifiability theory that formalizes this principle and shows that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
Towards Accurate Differential Diagnosis with Large Language Models
Comparing the two assisted study arms, the DDx quality score was higher for clinicians assisted by our LLM (top-10 accuracy 51. 7%) compared to clinicians without its assistance (36. 1%) (McNemar's Test: 45. 7, p < 0. 01) and clinicians with search (44. 4%) (4. 75, p = 0. 03).
The intersection of video capsule endoscopy and artificial intelligence: addressing unique challenges using machine learning
Discussion: Each of these five challenges is addressed, in part, by one of our AI-based models.
Provably Learning Object-Centric Representations
Under this generative process, we prove that the ground-truth object representations can be identified by an invertible and compositional inference model, even in the presence of dependencies between objects.
Weakly Supervised Deep Instance Nuclei Detection using Points Annotation in 3D Cardiovascular Immunofluorescent Images
In this study, we used a weakly supervised learning approach to train the HoVer-Net segmentation model using point annotations to detect nuclei in fluorescent images.
Pixel-level Correspondence for Self-Supervised Learning from Video
While self-supervised learning has enabled effective representation learning in the absence of labels, for vision, video remains a relatively untapped source of supervision.
MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
Nuclei vary substantially in structure and appearances across different cancer types, leading to a drop in performance of deep learning models when trained on one cancer type and tested on another.
Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network
Exercise testing has been available for more than a half-century and is a remarkably versatile tool for diagnostic and prognostic information of patients for a range of diseases, especially cardiovascular and pulmonary.
Unsupervised Learning of Compositional Energy Concepts
In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework.
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA
This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.
HistoTransfer: Understanding Transfer Learning for Histopathology
In this work, we compare the performance of features extracted from networks trained on ImageNet and histopathology data.
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
A common practice is to perform data augmentation via hand-crafted transformations intended to leave the semantics of the data invariant.
Ranked #1 on
Image Classification
on Causal3DIdent
Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification
We regularized the clustering mechanism by introducing a KL-divergence loss between the attention weights of patches in a cluster and the uniform distribution.
Contrastive Learning Inverts the Data Generating Process
Contrastive learning has recently seen tremendous success in self-supervised learning.
Ranked #1 on
Disentanglement
on KITTI-Masks
Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalise well and are robust to changes in the input distribution.
Improving Low Resource Code-switched ASR using Augmented Code-switched TTS
Building Automatic Speech Recognition (ASR) systems for code-switched speech has recently gained renewed attention due to the widespread use of speech technologies in multilingual communities worldwide.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Towards Nonlinear Disentanglement in Natural Data with Temporal Sparse Coding
We construct an unsupervised learning model that achieves nonlinear disentanglement of underlying factors of variation in naturalistic videos.
Ranked #1 on
Disentanglement
on Natural Sprites
S2RMs: Spatially Structured Recurrent Modules
Capturing the structure of a data-generating process by means of appropriate inductive biases can help in learning models that generalize well and are robust to changes in the input distribution.
Benchmarking Unsupervised Object Representations for Video Sequences
Perceiving the world in terms of objects and tracking them through time is a crucial prerequisite for reasoning and scene understanding.
Devising Malware Characterstics using Transformers
With the increasing number of cybersecurity threats, it becomes more difficult for researchers to skim through the security reports for malware analysis.
Self-Attentive Adversarial Stain Normalization
We propose a Self-Attentive Adversarial Stain Normalization (SAASN) approach for the normalization of multiple stain appearances to a common domain.
On the Effectiveness of Low Frequency Perturbations
Carefully crafted, often imperceptible, adversarial perturbations have been shown to cause state-of-the-art models to yield extremely inaccurate outputs, rendering them unsuitable for safety-critical application domains.
MMA Training: Direct Input Space Margin Maximization through Adversarial Training
We study adversarial robustness of neural networks from a margin maximization perspective, where margins are defined as the distances from inputs to a classifier's decision boundary.
CAAD 2018: Generating Transferable Adversarial Examples
Our team participated in the CAAD 2018 competition, and won 1st place in both attack subtracks, non-targeted and targeted adversarial attacks, and 3rd place in defense.
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
Our experiments on different datasets (MNIST, CIFAR-10, and ImageNet) show that GenAttack can successfully generate visually imperceptible adversarial examples against state-of-the-art image recognition models with orders of magnitude fewer queries than previous approaches.
Generating Natural Language Adversarial Examples
Deep neural networks (DNNs) are vulnerable to adversarial examples, perturbations to correctly classified examples which can cause the model to misclassify.
Bypassing Feature Squeezing by Increasing Adversary Strength
Feature Squeezing is a recently proposed defense method which reduces the search space available to an adversary by coalescing samples that correspond to many different feature vectors in the original space into a single sample.
Are Generative Classifiers More Robust to Adversarial Attacks?
There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed.
Attacking the Madry Defense Model with $L_1$-based Adversarial Examples
The Madry Lab recently hosted a competition designed to test the robustness of their adversarially trained MNIST model.
EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples
Recent studies have highlighted the vulnerability of deep neural networks (DNNs) to adversarial examples - a visually indistinguishable adversarial image can easily be crafted to cause a well-trained model to misclassify.
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
However, different from leveraging attack transferability from substitute models, we propose zeroth order optimization (ZOO) based attacks to directly estimate the gradients of the targeted DNN for generating adversarial examples.
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
An adversarial example library for constructing attacks, building defenses, and benchmarking both
