All Stories

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