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Found 25 papers, 15 papers with code
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
The organization of the research roundtables at the conference involved 17 Senior Chairs and 19 Junior Chairs across 11 tables.
Demographic Bias of Expert-Level Vision-Language Foundation Models in Medical Imaging
Such demographic biases present over a wide range of pathologies and demographic attributes.
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
An obvious obstacle lies in the absence of a specific benchmark to evaluate the effectiveness of MLLMs on aesthetic perception.
The Limits of Fair Medical Imaging AI In The Wild
As artificial intelligence (AI) rapidly approaches human-level performance in medical imaging, it is crucial that it does not exacerbate or propagate healthcare disparities.
Training Robust Deep Physiological Measurement Models with Synthetic Video-based Data
However, there exists a significant gap between synthetic and real-world data, which hinders the generalization of neural models trained on these synthetic datasets.
u-LLaVA: Unifying Multi-Modal Tasks via Large Language Model
Recent advancements in multi-modal large language models (MLLMs) have led to substantial improvements in visual understanding, primarily driven by sophisticated modality alignment strategies.
Towards long-tailed, multi-label disease classification from chest X-ray: Overview of the CXR-LT challenge
Many real-world image recognition problems, such as diagnostic medical imaging exams, are "long-tailed" $\unicode{x2013}$ there are a few common findings followed by many more relatively rare conditions.
CLIP Brings Better Features to Visual Aesthetics Learners
Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss.
BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements
As an example, perception occurs at different scales both spatially and temporally, suggesting that the extraction of salient visual information may be made more effective by paying attention to specific features at varying scales.
Change is Hard: A Closer Look at Subpopulation Shift
Machine learning models often perform poorly on subgroups that are underrepresented in the training data.
SimPer: Simple Self-Supervised Learning of Periodic Targets
From human physiology to environmental evolution, important processes in nature often exhibit meaningful and strong periodic or quasi-periodic changes.
Mixed Sample Augmentation for Online Distillation
Mixed Sample Regularization (MSR), such as MixUp or CutMix, is a powerful data augmentation strategy to generalize convolutional neural networks.
Personalized Image Aesthetics Assessment with Rich Attributes
To solve the dilemma, we conduct so far, the most comprehensive subjective study of personalized image aesthetics and introduce a new Personalized image Aesthetics database with Rich Attributes (PARA), which consists of 31, 220 images with annotations by 438 subjects.
Self-Distillation from the Last Mini-Batch for Consistency Regularization
Afterwards, the former half mini-batch distills on-the-fly soft targets generated in the previous iteration.
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond
We first develop the domain-class transferability graph, and show that such transferability governs the success of learning in MDLT.
Domain Adaptation with Factorizable Joint Shift
In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels.
Targeted Supervised Contrastive Learning for Long-Tailed Recognition
This forces all classes, including minority classes, to maintain a uniform distribution in the feature space, improves class boundaries, and provides better generalization even in the presence of long-tail data.
Ranked #22 on
Long-tail Learning
on CIFAR-10-LT (ρ=100)
Delving into Deep Imbalanced Regression
We define Deep Imbalanced Regression (DIR) as learning from such imbalanced data with continuous targets, dealing with potential missing data for certain target values, and generalizing to the entire target range.
Rethinking the Value of Labels for Improving Class-Imbalanced Learning
Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models.
Ranked #24 on
Long-tail Learning
on CIFAR-100-LT (ρ=50)
Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation
As our key contribution, we develop a simple, iterative learning algorithm that finds $\epsilon$-optimal $Q$-function with sample complexity of $\widetilde{O}(\frac{1}{\epsilon^{\max(d_1, d_2)+2}})$ when the optimal $Q$-function has low rank $r$ and the discounting factor $\gamma$ is below a certain threshold.
When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
Recent advances in adversarial attacks uncover the intrinsic vulnerability of modern deep neural networks.
Harnessing Structures for Value-Based Planning and Reinforcement Learning
In this paper, we propose to exploit the underlying structures of the state-action value function, i. e., Q function, for both planning and deep RL.
ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation
We show that this process destroys the adversarial structure of the noise, while re-enforcing the global structure in the original image.
ImgSensingNet: UAV Vision Guided Aerial-Ground Air Quality Sensing System
Given the increasingly serious air pollution problem, the monitoring of air quality index (AQI) in urban areas has drawn considerable attention.
Rallying Adversarial Techniques against Deep Learning for Network Security
Recent advances in artificial intelligence and the increasing need for powerful defensive measures in the domain of network security, have led to the adoption of deep learning approaches for use in network intrusion detection systems.
