All Stories

  1. Retrieval-Augmented Foundation Models for Water Level Prediction in the Everglades
  2. Exploring Robust Multi-Agent Workflows for Environmental Data Management
  3. Fragment: Efficient DNN Checkpoint with Relaxed Model Consistency
  4. In-depth Analysis of Privacy Threats in Federated Learning for Medical Data
  5. A Diversity-Optimized Deep Ensemble Approach for Accurate Plant Leaf Disease Detection
  6. Jailbreaking Large Vision Language Models in Intelligent Transportation Systems
  7. Augmenting Question Answering with A Hybrid RAG Approach
  8. EFT-LR: Benchmarking Learning Rate Policies in Parameter-Efficient Large Language Model Fine-tuning
  9. LATTICE: Efficient In-Memory DNN Model Versioning
  10. CE-CoLLM: Efficient and Adaptive Large Language Models Through Cloud-Edge Collaboration
  11. NeRF-APT: A New NeRF Framework for Wireless Channel Prediction
  12. Security and Privacy Challenges of Large Language Models: A Survey
  13. CLL-RetICL: Contrastive Linguistic Label Retrieval-based In-Context Learning for Text Classification via Large Language Models
  14. Accessible Health Screening Using Body Fat Estimation by Image Segmentation
  15. Boosting Imperceptibility of Stable Diffusion-based Adversarial Examples Generation with Momentum
  16. Effective Diversity Optimizations for High Accuracy Deep Ensembles
  17. On the Efficiency of Privacy Attacks in Federated Learning
  18. Backdoor Attacks Against Low-Earth Orbit Satellite Fingerprinting
  19. ZipZap: Efficient Training of Language Models for Large-Scale Fraud Detection on Blockchain
  20. Adaptive Deep Neural Network Inference Optimization with EENet
  21. Demystifying Data Poisoning Attacks in Distributed Learning as a Service
  22. Individual Fairness with Group Awareness Under Uncertainty
  23. Privacy Risks Analysis and Mitigation in Federated Learning for Medical Images
  24. Exploring Model Learning Heterogeneity for Boosting Ensemble Robustness
  25. Model Cloaking against Gradient Leakage
  26. Hierarchical Pruning of Deep Ensembles with Focal Diversity
  27. Amplifying Object Tracking Performance on Edge Devices
  28. Rethinking Learning Rate Tuning in the Era of Large Language Models
  29. Invisible Watermarking for Audio Generation Diffusion Models
  30. Securing Distributed SGD Against Gradient Leakage Threats
  31. STDLens: Model Hijacking-Resilient Federated Learning for Object Detection
  32. Selecting and Composing Learning Rate Policies for Deep Neural Networks
  33. Learning TFIDF Enhanced Joint Embedding for Recipe-Image Cross-Modal Retrieval Service
  34. Learning Text-image Joint Embedding for Efficient Cross-modal Retrieval with Deep Feature Engineering
  35. A Comparative Measurement Study of Deep Learning as a Service Framework
  36. Transparent Network Memory Storage for Efficient Container Execution in Big Data Clouds
  37. Boosting Deep Ensemble Performance with Hierarchical Pruning
  38. Parallel Detection for Efficient Video Analytics at the Edge
  39. RDMAbox: Optimizing RDMA for Memory Intensive Workload
  40. Gradient-Leakage Resilient Federated Learning
  41. Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
  42. Memory Abstraction and Optimization for Distributed Executors
  43. Adversarial Deception in Deep Learning: Analysis and Mitigation
  44. Adversarial Objectness Gradient Attacks in Real-time Object Detection Systems
  45. Cross-Modal Joint Embedding with Diverse Semantics
  46. Promoting High Diversity Ensemble Learning with EnsembleBench
  47. Efficient Orchestration of Host and Remote Shared Memory for Memory Intensive Workloads
  48. Cross-Layer Strategic Ensemble Defense Against Adversarial Examples
  49. A Framework for Evaluating Client Privacy Leakages in Federated Learning
  50. Understanding Object Detection Through an Adversarial Lens
  51. Demystifying Learning Rate Policies for High Accuracy Training of Deep Neural Networks
  52. Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks
  53. Deep Neural Network Ensembles Against Deception: Ensemble Diversity, Accuracy and Robustness
  54. Memory Disaggregation: Research Problems and Opportunities
  55. Experimental Characterizations and Analysis of Deep Learning Frameworks
  56. Benchmarking Deep Learning Frameworks: Design Considerations, Metrics and Beyond
  57. CCAligner