IMC-CLINIC: Coupled Loss-Informed Newton Iterations for Clipping in Analog In-Memory Computing
cs.AI
Submitted: 2026-09-28
Updated: 2026-09-28
Code: https://github.com/bmurmann/ADC-survey
Sources
- PrefixQuant: Eliminating Outliers by Prefixed Tokens for Large Language Models Quantization
- PACT: Parameterized Clipping Activation for Quantized Neural Networks
- Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
- The Pile: An 800GB Dataset of Diverse Text for Language Modeling
- The Llama 3 Herd of Models
- HPTQ: Hardware-Friendly Post Training Quantization
- SpinQuant: LLM quantization with learned rotations
- Pointer Sentinel Mixture Models
- EasyQuant: Post-training Quantization via Scale Optimization
- Qwen3 Technical Report
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems