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Found 27 papers, 12 papers with code
On Transferability of Histological Tissue Labels in Computational Pathology
Deep learning tools in computational pathology, unlike natural vision tasks, face with limited histological tissue labels for classification.
Detector Collapse: Backdooring Object Detection to Catastrophic Overload or Blindness
This paper is dedicated to bridging this gap by introducing Detector Collapse} (DC), a brand-new backdoor attack paradigm tailored for object detection.
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes.
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection.
Does DetectGPT Fully Utilize Perturbation? Bridge Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better
Hence, we propose a novel fine-tuned detector, Pecola, bridging metric-based and fine-tuned detectors by contrastive learning on selective perturbation.
Improving Pacing in Long-Form Story Planning
Existing LLM-based systems for writing long-form stories or story outlines frequently suffer from unnatural pacing, whether glossing over important events or over-elaborating on insignificant details, resulting in a jarring experience for the reader.
Fuzzy-NMS: Improving 3D Object Detection with Fuzzy Classification in NMS
The proposed Fuzzy-NMS module combines the volume and clustering density of candidate bounding boxes, refining them with a fuzzy classification method and optimizing the appropriate suppression thresholds to reduce uncertainty in the NMS process.
Dual Radar: A Multi-modal Dataset with Dual 4D Radar for Autonomous Driving
Compared with commonly used 3D radars, the latest 4D radars have precise vertical resolution and higher point cloud density, making it a highly promising sensor for autonomous driving in complex environmental perception.
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design.
Dialogue for Prompting: a Policy-Gradient-Based Discrete Prompt Generation for Few-shot Learning
However, prior discrete prompt optimization methods require expert knowledge to design the base prompt set and identify high-quality prompts, which is costly, inefficient, and subjective.
Score Attack: A Lower Bound Technique for Optimal Differentially Private Learning
The score attack method is based on the tracing attack concept in differential privacy and can be applied to any statistical model with a well-defined score statistic.
Improved Differentially Private Regression via Gradient Boosting
We revisit the problem of differentially private squared error linear regression.
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive Learning
Machine-Generated Text (MGT) detection, a task that discriminates MGT from Human-Written Text (HWT), plays a crucial role in preventing misuse of text generative models, which excel in mimicking human writing style recently.
The Cost of Privacy in Generalized Linear Models: Algorithms and Minimax Lower Bounds
We propose differentially private algorithms for parameter estimation in both low-dimensional and high-dimensional sparse generalized linear models (GLMs) by constructing private versions of projected gradient descent.
Learning unbiased zero-shot semantic segmentation networks via transductive transfer
Our method assumes that both the source images with full pixel-level labels and unlabeled target images are available during training.
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Recommender systems that can learn from cross-session data to dynamically predict the next item a user will choose are crucial for online platforms.
The Cost of Privacy: Optimal Rates of Convergence for Parameter Estimation with Differential Privacy
By refining the "tracing adversary" technique for lower bounds in the theoretical computer science literature, we formulate a general lower bound argument for minimax risks with differential privacy constraints, and apply this argument to high-dimensional mean estimation and linear regression problems.
Learning to Optimize via Wasserstein Deep Inverse Optimal Control
We first propose a unified KL framework that generalizes existing maximum entropy inverse optimal control methods.
Predicting User Activity Level In Point Processes With Mass Transport Equation
Point processes are powerful tools to model user activities and have a plethora of applications in social sciences.
Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs
The occurrence of a fact (edge) is modeled as a multivariate point process whose intensity function is modulated by the score for that fact computed based on the learned entity embeddings.
Variational Policy for Guiding Point Processes
Temporal point processes have been widely applied to model event sequence data generated by online users.
Coevolutionary Latent Feature Processes for Continuous-Time User-Item Interactions
Matching users to the right items at the right time is a fundamental task in recommendation systems.
Deep Coevolutionary Network: Embedding User and Item Features for Recommendation
DeepCoevolve use recurrent neural network (RNN) over evolving networks to define the intensity function in point processes, which allows the model to capture complex mutual influence between users and items, and the feature evolution over time.
Fast and Simple Optimization for Poisson Likelihood Models
Since almost all gradient-based optimization algorithms rely on Lipschitz-continuity, optimizing Poisson likelihood models with a guarantee of convergence can be challenging, especially for large-scale problems.
Time-Sensitive Recommendation From Recurrent User Activities
By making personalized suggestions, a recommender system is playing a crucial role in improving the engagement of users in modern web-services.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it.
