Search Results for author:
Found 33 papers, 2 papers with code
LIFBench: Evaluating the Instruction Following Performance and Stability of Large Language Models in Long-Context Scenarios
As Large Language Models (LLMs) continue to advance in natural language processing (NLP), their ability to stably follow instructions in long-context inputs has become crucial for real-world applications.
Denoising Diffusions in Latent Space for Medical Image Segmentation
In case of medical imaging, often times the images are large 3D scans, where segmenting one image using DPMs become extremely inefficient due to large memory consumption and time consuming iterative sampling process.
Towards unlocking the mystery of adversarial fragility of neural networks
We provide a matrix-theoretic explanation of the adversarial fragility of deep neural network for classification.
An Improved Empirical Fisher Approximation for Natural Gradient Descent
Approximate Natural Gradient Descent (NGD) methods are an important family of optimisers for deep learning models, which use approximate Fisher information matrices to pre-condition gradients during training.
Manipulating Predictions over Discrete Inputs in Machine Teaching
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher.
Surf-CDM: Score-Based Surface Cold-Diffusion Model For Medical Image Segmentation
Diffusion models have shown impressive performance for image generation, often times outperforming other generative models.
Diagnosis Of Takotsubo Syndrome By Robust Feature Selection From The Complex Latent Space Of DL-based Segmentation Network
However, directly using classification or segmentation models on medical to learn latent features opt out robust feature selection and may lead to overfitting.
gcDLSeg: Integrating Graph-cut into Deep Learning for Binary Semantic Segmentation
To combine the strengths of both approaches, we propose in this study to integrate the graph-cut approach into a deep learning network for end-to-end learning.
Trust, but Verify: Robust Image Segmentation using Deep Learning
We describe a method for verifying the output of a deep neural network for medical image segmentation that is robust to several classes of random as well as worst-case perturbations i. e. adversarial attacks.
Outlier Detection Using Generative Models with Theoretical Performance Guarantees
We propose an outlier detection approach for reconstructing the ground-truth signals modeled by generative models under sparse outliers.
Unveiling Security, Privacy, and Ethical Concerns of ChatGPT
This paper delves into the realm of ChatGPT, an AI-powered chatbot that utilizes topic modeling and reinforcement learning to generate natural responses.
Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e. g., multiple 2D slices of a CT scan for a patient).
A deep learning network with differentiable dynamic programming for retina OCT surface segmentation
Unfortunately, due to the scarcity of training data in medical imaging, it is challenging for DL networks to learn the global structure of the target surfaces, including surface smoothness.
Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices
(i) We benchmark a variety of loss functions with different algorithmic choices for deep AUROC optimization problem.
When AUC meets DRO: Optimizing Partial AUC for Deep Learning with Non-Convex Convergence Guarantee
In this paper, we propose systematic and efficient gradient-based methods for both one-way and two-way partial AUC (pAUC) maximization that are applicable to deep learning.
Joint Calibrationless Reconstruction and Segmentation of Parallel MRI
We introduce a novel image domain deep-learning framework for calibrationless parallel MRI reconstruction, coupled with a segmentation network to improve image quality and to reduce the vulnerability of current segmentation algorithms to image artifacts resulting from acceleration.
Learning Similarity between Movie Characters and Its Potential Implications on Understanding Human Experiences
While many different aspects of human experiences have been studied by the NLP community, none has captured its full richness.
Optimal Pooling Matrix Design for Group Testing with Dilution (Row Degree) Constraints
In this paper, we consider the problem of designing optimal pooling matrix for group testing (for example, for COVID-19 virus testing) with the constraint that no more than $r>0$ samples can be pooled together, which we call "dilution constraint".
Globally Optimal Segmentation of Mutually Interacting Surfaces using Deep Learning
In this work, we propose to parameterize the surface cost functions in the graph model and leverage DL to learn those parameters.
Globally Optimal Surface Segmentation using Deep Learning with Learnable Smoothness Priors
Automated surface segmentation is important and challenging in many medical image analysis applications.
Unsupervised anomaly localization using VAE and beta-VAE
An VAE trained on normal images is expected to only be able to reconstruct normal images, allowing the localization of anomalous pixels in an image via manipulating information within the VAE ELBO loss.
Do Deep Minds Think Alike? Selective Adversarial Attacks for Fine-Grained Manipulation of Multiple Deep Neural Networks
Recent works have demonstrated the existence of {\it adversarial examples} targeting a single machine learning system.
Author2Vec: A Framework for Generating User Embedding
Online forums and social media platforms provide noisy but valuable data every day.
No, you're not alone: A better way to find people with similar experiences on Reddit
This model is built upon the BERT Next Sentence Prediction model and reduces the time complexity for clustering all posts in a corpus from O(n{\^{}}2) to O(n) with respect to the number of posts.
Deep Neural Networks for Surface Segmentation Meet Conditional Random Fields
To the best of our knowledge, this is the first study to apply a 3-D neural network with a CRFs model for direct surface segmentation.
Trust but Verify: An Information-Theoretic Explanation for the Adversarial Fragility of Machine Learning Systems, and a General Defense against Adversarial Attacks
In this paper, we present a simple hypothesis about a feature compression property of artificial intelligence (AI) classifiers and present theoretical arguments to show that this hypothesis successfully accounts for the observed fragility of AI classifiers to small adversarial perturbations.
Deep segmentation networks predict survival of non-small cell lung cancer
Here we show that CNN trained to perform the tumor segmentation task, with no other information than physician contours, identify a rich set of survival-related image features with remarkable prognostic value.
Robust Image Segmentation Quality Assessment
Deep learning based image segmentation methods have achieved great success, even having human-level accuracy in some applications.
Outlier Detection using Generative Models with Theoretical Performance Guarantees
In this paper, we propose a generative model neural network approach for reconstructing the ground truth signals under sparse outliers.
Optimal Multi-Object Segmentation with Novel Gradient Vector Flow Based Shape Priors
Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness.
Simultaneous Multiple Surface Segmentation Using Deep Learning
The task of automatically segmenting 3-D surfaces representing boundaries of objects is important for quantitative analysis of volumetric images, and plays a vital role in biomedical image analysis.
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space
Optimal surface segmentation is a state-of-the-art method used for segmentation of multiple globally optimal surfaces in volumetric datasets.
Error-tolerant Scribbles Based Interactive Image Segmentation
The experimental results show that the proposed algorithm is robust to the errors in the user input and preserves the "anchoring" capability of the user input.
