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- The Split Bregman Method for L1-Regularized Problems4,507
This paper proposes a “split Bregman” method, which can solve a very broad class of L1-regularized problems, and applies this technique to the Rudin-Osher-Fatemi functional for image denoising and to a compressed sensing problem that arises in magnetic resonance imaging.
- A Watermark for Large Language Models1,108
A statistical test for detecting the watermark with interpretable p-values is proposed, and an information-theoretic framework for analyzing the sensitivity of the watermarks is derived.
- 926
- Fast Alternating Direction Optimization Methods837
This paper considers accelerated variants of two common alternating direction methods: the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA), of the form first proposed by Nesterov for gradient descent methods.
- Baseline Defenses for Adversarial Attacks Against Aligned Language Models818
It is found that the weakness of existing discrete optimizers for text, combined with the relatively high costs of optimization, makes standard adaptive attacks more challenging for LLMs.
- Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models612
Test-time prompt tuning (TPT) is proposed, a method that can learn adaptive prompts on the fly with a single test sample and performs on par with the state-of-the-art approaches that use additional training data.
- Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models560
This work studies image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated, and identifies cases where diffusion models, including the popular Stable Diffusion model, blatantly copy from their training data.
- Universal Guidance for Diffusion Models550
This work proposes a universal guidance algorithm that enables diffusion models to be controlled by arbitrary guidance modalities without the need to retrain any use-specific components.
- Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction492
The primary purpose of this paper is to examine the effectiveness of “Split Bregman” techniques for solving image segmentation problems, and to compare this scheme with more conventional methods.
- Dataset Security for Machine Learning: Data Poisoning, Backdoor Attacks, and Defenses438
The goal of this work is to systematically categorize and discuss a wide range of dataset vulnerabilities and exploits, approaches for defending against these threats, and an array of open problems in this space.
- BLIP3-o: A Family of Fully Open Unified Multimodal Models-Architecture, Training and Dataset394
A novel approach that employs a diffusion transformer to generate semantically rich CLIP image features, in contrast to conventional VAE-based representations is introduced, which yields both higher training efficiency and improved generative quality.
- Quantized Precoding for Massive MU-MIMO367
This paper investigates the problem of downlink precoding for a narrowband massive MU-MIMO system with low-resolution digital-to-analog converters at the base station and proposes novel nonlinear precoding algorithms that significantly outperform linear precoders at the cost of an increased computational complexity.
- 355
- Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors334
A systematic study of adversarial attacks on state-of-the-art object detection frameworks, and a detailed study of physical world attacks using printed posters and wearable clothes, to quantify the performance of such attacks with different metrics.
- PhaseMax: Convex Phase Retrieval via Basis Pursuit295
This work formulate phase retrieval as a convex optimization problem, which it is shown that the dual problem to PhaseMax is basis pursuit, which implies that the phase retrieval can be performed using algorithms initially designed for sparse signal recovery.
- Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust279
This paper introduces a novel technique called Tree-Ring Watermarking that robustly fingerprints diffusion model outputs and demonstrates that this technique can be easily applied to arbitrary diffusion models, including text-conditioned Stable Diffusion, as a plug-in with negligible loss in FID.
- DCAN: Dual Channel-Wise Alignment Networks for Unsupervised Scene Adaptation276
Dual Channel-wise Alignment Networks (DCAN) are presented, a simple yet effective approach to reduce domain shift at both pixel-level and feature-level in deep neural networks for semantic segmentation.
- 268
- On the Reliability of Watermarks for Large Language Models251
This work finds that watermarks remain detectable even after human and machine paraphrasing, and considers a range of new detection schemes that are sensitive to short spans of watermarked text embedded inside a large document, and compares the robustness of watermarking to other kinds of detectors.
- Channel Charting: Locating Users Within the Radio Environment Using Channel State Information241
Channel charting (CC) is proposed, a novel framework in which a multi-antenna network element learns a chart of the radio geometry in its surrounding area that enables a range of new applications in UE localization, network planning, user scheduling, multipoint connectivity, hand-over, cell search, user grouping, and other cognitive tasks that rely on CSI and UE movement relative to the base station.
- 236
- Universal Adversarial Training215
This work proposes universal adversarial training, which models the problem of robust classifier generation as a two-player min-max game, and produces robust models with only 2× the cost of natural training.
- 184
- Towards Transferable Adversarial Attacks on Vision Transformers175
A dual attack framework is introduced, which contains a Pay No Attention (PNA) attack and a PatchOut attack, to improve the transferability of adversarial samples across different ViTs, and it is shown that skipping the gradients of attention during backpropagation can generate adversarial examples with high transferability.
- Strong Data Augmentation Sanitizes Poisoning and Backdoor Attacks Without an Accuracy Tradeoff160
It is found that strong data augmentations, such as mixup and CutMix, can significantly diminish the threat of poisoning and backdoor attacks without trading off performance.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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