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Found 17 papers, 3 papers with code
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
In this work, we provide a finetuning approach to enhance the robustness of vision transformers inspired by the concept of nullspace from linear algebra.
Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Extensive empirical experiments suggest that our method not only outperforms standard adversarial training on both accuracy and robustness with less computation overhead but is also capable of generating robust distilled datasets that can withstand various adversarial attacks.
Toward a Team of AI-made Scientists for Scientific Discovery from Gene Expression Data
For instance, it has facilitated the identification of disease-predictive genes from gene expression data, significantly advancing healthcare.
Accelerating PDE Data Generation via Differential Operator Action in Solution Space
It applies differential operators on these solutions, a process we call 'operator action', to efficiently generate precise PDE data points.
Machine Learning Insides OptVerse AI Solver: Design Principles and Applications
To this end, we present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI Solver, which aims to mitigate the scarcity of real-world mathematical programming instances, and to surpass the capabilities of traditional optimization techniques.
Dataset Distillation via the Wasserstein Metric
Dataset Distillation (DD) emerges as a powerful strategy to encapsulate the expansive information of large datasets into significantly smaller, synthetic equivalents, thereby preserving model performance with reduced computational overhead.
Beyond Pixels: Exploring Human-Readable SVG Generation for Simple Images with Vision Language Models
Our method focuses on producing SVGs that are both accurate and simple, aligning with human readability and understanding.
Promoting Generalization for Exact Solvers via Adversarial Instance Augmentation
To tackle this problem, we propose a novel approach, which is called Adversarial Instance Augmentation and does not require to know the problem type for new instance generation, to promote data diversity for learning-based branching modules in the branch-and-bound (B&B) Solvers (AdaSolver).
QUIZ: An Arbitrary Volumetric Point Matching Method for Medical Image Registration
Rigid pre-registration involving local-global matching or other large deformation scenarios is crucial.
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
Machine learning has demonstrated remarkable performance over finite datasets, yet whether the scores over the fixed benchmarks can sufficiently indicate the model's performance in the real world is still in discussion.
Towards Trustworthy and Aligned Machine Learning: A Data-centric Survey with Causality Perspectives
Accordingly, this survey presents the background of trustworthy machine learning development using a unified set of concepts, connects this language to Pearl's causal hierarchy, and finally discusses methods explicitly inspired by causality literature.
Augmented Lagrangian Methods for Time-varying Constrained Online Convex Optimization
Furthermore, we extend MALM to deal with time-varying functional constrained OCO with delayed feedback, in which the feedback information of loss and constraint functions is revealed to decision maker with delays.
UIUC\_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions
We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications.
UIUC_BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions
We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications.
Exact high-dimensional asymptotics for Support Vector Machine
In this paper, we consider the soft-margin SVM used on data points with independent features, where the sample size $n$ and the feature dimension $p$ grows to $\infty$ in a fixed ratio $p/n\rightarrow \delta$.
An equivalence between critical points for rank constraints versus low-rank factorizations
Two common approaches in low-rank optimization problems are either working directly with a rank constraint on the matrix variable, or optimizing over a low-rank factorization so that the rank constraint is implicitly ensured.
Optimization and Control
Between hard and soft thresholding: optimal iterative thresholding algorithms
Instead, a general class of thresholding operators, lying between hard thresholding and soft thresholding, is shown to be optimal with the strongest possible convergence guarantee among all thresholding operators.
