169 results on '"D, Mullins"'
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2. Locking Machine Learning Models into Hardware.
3. Beyond Slow Signs in High-fidelity Model Extraction.
4. Unlocking the Global Synergies in Low-Rank Adapters.
5. Architectural Neural Backdoors from First Principles.
6. Optimised Grouped-Query Attention Mechanism for Transformers.
7. Dynamic Stashing Quantization for Efficient Transformer Training.
8. Architectural Backdoors in Neural Networks.
9. Revisiting Automated Prompting: Are We Actually Doing Better?
10. Revisiting Structured Dropout.
11. Trace-and-brace (TAB): bespoke software countermeasures against soft errors.
12. DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning.
13. Sim-D: A SIMD Accelerator for Hard Real-Time Systems.
14. LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation.
15. Human-Producible Adversarial Examples.
16. Sponge Examples: Energy-Latency Attacks on Neural Networks.
17. Blackbox Attacks on Reinforcement Learning Agents Using Approximated Temporal Information.
18. Towards Certifiable Adversarial Sample Detection.
19. Rapid Model Architecture Adaption for Meta-Learning.
20. Lane Compression: A Lightweight Lossless Compression Method for Machine Learning on Embedded Systems.
21. Inhibition of androgen/AR signaling inhibits diethylnitrosamine (DEN) induced tumour initiation and remodels liver immune cell networks
22. Focused Quantization for Sparse CNNs.
23. Automatic Generation of Multi-Precision Multi-Arithmetic CNN Accelerators for FPGAs.
24. Efficient Winograd or Cook-Toom Convolution Kernel Implementation on Widely Used Mobile CPUs.
25. Characterizing Sources of Ineffectual Computations in Deep Learning Networks.
26. Performance analysis of single board computer clusters.
27. Model Architecture Adaption for Bayesian Neural Networks.
28. Augmentation Backdoors.
29. Wide Attention Is The Way Forward For Transformers.
30. DARTFormer: Finding The Best Type Of Attention.
31. Efficient Adversarial Training With Data Pruning.
32. Revisiting Embeddings for Graph Neural Networks.
33. Revisiting Structured Dropout.
34. Architectural Backdoors in Neural Networks.
35. ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks.
36. Muntjac - Open Source Multicore RV64 Linux-capable SoC.
37. Mayo: A Framework for Auto-generating Hardware Friendly Deep Neural Networks.
38. Patients' Perspectives on Early Liver Transplantation in Alcohol‐Related Liver Disease
39. Rapid Model Architecture Adaption for Meta-Learning.
40. DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning.
41. 1D-FALCON: Accelerating Deep Convolutional Neural Network Inference by Co-optimization of Models and Underlying Arithmetic Implementation.
42. ADaPT: optimizing CNN inference on IoT and mobile devices using approximately separable 1-D kernels.
43. Next generation single board clusters.
44. Dynamic Channel Pruning: Feature Boosting and Suppression.
45. To Compress Or Not To Compress: Understanding The Interactions Between Adversarial Attacks And Neural Network Compression.
46. Accelerate Cycle-Level Full-System Simulation of Multi-Core RISC-V Systems with Binary Translation.
47. Nudge Attacks on Point-Cloud DNNs.
48. Probabilistic Dual Network Architecture Search on Graphs.
49. Learned Low Precision Graph Neural Networks.
50. Sponge Examples: Energy-Latency Attacks on Neural Networks.
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