Search Results for author:
Found 41 papers, 15 papers with code
Data-adaptive Safety Rules for Training Reward Models
Reinforcement Learning from Human Feedback (RLHF) is commonly employed to tailor models to human preferences, especially to improve the safety of outputs from large language models (LLMs).
ProjectedEx: Enhancing Generation in Explainable AI for Prostate Cancer
Additionally, we conduct comprehensive experiments on both the generator and classifier, demonstrating the clinical relevance and effectiveness of ProjectedEx in enhancing interpretability and supporting the adoption of AI in medical settings.
Analysis of Higher-Order Ising Hamiltonians
It is challenging to scale Ising machines for industrial-level problems due to algorithm or hardware limitations.
A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness
Large language models (LLMs) have demonstrated emergent abilities in text generation, question answering, and reasoning, facilitating various tasks and domains.
Foundations and Recent Trends in Multimodal Mobile Agents: A Survey
This survey provides a comprehensive review of mobile agent technologies, focusing on recent advancements that enhance real-time adaptability and multimodal interaction.
Catastrophic Failure of LLM Unlearning via Quantization
Machine unlearning has been introduced as a viable solution to remove the influence of such problematic content without the need for costly and time-consuming retraining.
Trojan Prompt Attacks on Graph Neural Networks
Graph Prompt Learning (GPL) has been introduced as a promising approach that uses prompts to adapt pre-trained GNN models to specific downstream tasks without requiring fine-tuning of the entire model.
Rule-based Data Selection for Large Language Models
Our approach includes an automated pipeline that first uses LLMs to generate a diverse set of rules, encompassing various rating dimensions to evaluate data quality.
Covariance-corrected Whitening Alleviates Network Degeneration on Imbalanced Classification
Class imbalance is a critical issue in image classification that significantly affects the performance of deep recognition models.
Robustness Inspired Graph Backdoor Defense
With this observation, we propose using random edge dropping to detect backdoors and theoretically show that it can efficiently distinguish poisoned nodes from clean ones.
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
To ensure a high attack success rate with ID triggers, we introduce novel modules designed to enhance trigger memorization by the victim model trained on poisoned graph.
GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision
Nevertheless, current models still encounter two main challenges: modeling depth accurately in the 2D-3D view transformation stage, and overcoming the lack of generalizability issues due to sparse LiDAR supervision.
First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia
We proposed a method to map the canopy height of the primeval forest within the world-level giant tree distribution area by using a spaceborne LiDAR fusion satellite imagery (Global Ecosystem Dynamics Investigation (GEDI), ICESat-2, and Sentinel-2) driven deep learning modeling.
HGS-Mapping: Online Dense Mapping Using Hybrid Gaussian Representation in Urban Scenes
However, integrating 3DGS into a street-view dense mapping framework still faces two challenges, including incomplete reconstruction due to the absence of geometric information beyond the LiDAR coverage area and extensive computation for reconstruction in large urban scenes.
TBI Image/Text (TBI-IT): Comprehensive Text and Image Datasets for Traumatic Brain Injury Research
In this paper, we introduce a new dataset in the medical field of Traumatic Brain Injury (TBI), called TBI-IT, which includes both electronic medical records (EMRs) and head CT images.
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community.
HICH Image/Text (HICH-IT): Comprehensive Text and Image Datasets for Hypertensive Intracerebral Hemorrhage Research
Index Terms-HICH, Deep learning, Intraparenchymal hemorrhage, named entity recognition, novel dataset
Test-Time Compensated Representation Learning for Extreme Traffic Forecasting
In this paper, we identify an underexplored problem in multivariate traffic series prediction: extreme events.
Massively Parallel Continuous Local Search for Hybrid SAT Solving on GPUs
Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause learning (CDCL) have achieved remarkable engineering success, their sequential nature limits the parallelism that may be extracted for acceleration on platforms such as the graphics processing unit (GPU).
LiDAR-Camera Panoptic Segmentation via Geometry-Consistent and Semantic-Aware Alignment
3D panoptic segmentation is a challenging perception task that requires both semantic segmentation and instance segmentation.
Fairness-aware Message Passing for Graph Neural Networks
Graph Neural Networks (GNNs) have shown great power in various domains.
Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
This stream is subsequently fed into the decoder-based transformer to generate visual re-creations and textual feedback in the second stage.
Solving Quantum-Inspired Perfect Matching Problems via Tutte's Theorem-Based Hybrid Boolean Constraints
While general-purpose hybrid constraint solvers can be powerful, we show that direct encodings of the constrained-matching problem as hybrid constraints scale poorly and special techniques are still needed.
DPMS: An ADD-Based Symbolic Approach for Generalized MaxSAT Solving
They lack, however, the ability to handle 1) (non-CNF) hybrid constraints, such as XORs and 2) generalized MaxSAT problems natively.
You Only Need End-to-End Training for Long-Tailed Recognition
The generalization gap on the long-tailed data sets is largely owing to most categories only occupying a few training samples.
CoMPS: Continual Meta Policy Search
Beyond simply transferring past experience to new tasks, our goal is to devise continual reinforcement learning algorithms that learn to learn, using their experience on previous tasks to learn new tasks more quickly.
FedProc: Prototypical Contrastive Federated Learning on Non-IID data
In this paper, we propose FedProc: prototypical contrastive federated learning, which is a simple and effective federated learning framework.
New Perspective on Progressive GANs Distillation for One-class Novelty Detection
One-class novelty detection is conducted to identify anomalous instances, with different distributions from the expected normal instances.
Abstract, Rationale, Stance: A Joint Model for Scientific Claim Verification
In addition, we enhance the information exchanges and constraints among tasks by proposing a regularization term between the sentence attention scores of abstract retrieval and the estimated outputs of rational selection.
A Cascaded Zoom-In Network for Patterned Fabric Defect Detection
With the observation that most fabrics are defect free in practice, a two-step Cascaded Zoom-In Network (CZI-Net) is proposed for patterned fabric defect detection.
Instance-weighted Central Similarity for Multi-label Image Retrieval
Deep hashing has been widely applied to large-scale image retrieval by encoding high-dimensional data points into binary codes for efficient retrieval.
Momentum-inspired Low-Rank Coordinate Descent for Diagonally Constrained SDPs
We present a novel, practical, and provable approach for solving diagonally constrained semi-definite programming (SDP) problems at scale using accelerated non-convex programming.
FAST-Dynamic-Vision: Detection and Tracking Dynamic Objects with Event and Depth Sensing
The bottleneck of solving this problem is the accurate perception of rapid dynamic objects.
Motion Compensation
Robust Object Detection
+1
Robotics
Embedded Knowledge Distillation in Depth-Level Dynamic Neural Network
In real applications, different computation-resource devices need different-depth networks (e. g., ResNet-18/34/50) with high-accuracy.
On Continuous Local BDD-Based Search for Hybrid SAT Solving
We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints.
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models
Computational learning theory states that many classes of boolean formulas are learnable in polynomial time.
P-KDGAN: Progressive Knowledge Distillation with GANs for One-class Novelty Detection
The progressive learning of knowledge distillation is a two-step approach that continuously improves the performance of the student GAN and achieves better performance than single step methods.
Ranked #6 on
Anomaly Detection
on MNIST
(using extra training data)
Joint Embedding in Named Entity Linking on Sentence Level
The named entity linking is challenging, given the fact that there are multiple candidate entities for a mention in a document.
FourierSAT: A Fourier Expansion-Based Algebraic Framework for Solving Hybrid Boolean Constraints
By such a reduction to continuous optimization, we propose an algebraic framework for solving systems consisting of different types of constraints.
Ranking Preserving Hashing for Fast Similarity Search
But in many real world applications, ranking measure is important for evaluating the quality of hashing codes. In this paper, we propose a novel Ranking Preserving Hashing (RPH) approach that directly optimizes a popular ranking measure, Normalized Discounted Cumulative Gain (NDCG), to obtain effective hashing codes with high ranking accuracy.
