Imagen de Portada

S2D: Selective Spectral Decay for Quantization-Friendly Conditioning of Neural Activations

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Chavan, Arnav, Lele, Nahush, Bamba, Udbhav, Dayal, Sankalp, Raghunathan, Aditi, Gupta, Deepak
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Surgical Feature-Space Decomposition of LLMs: Why, When and How?
por: Chavan, Arnav, et al.
Publicado: (2024)

DOT-MoE: Differentiable Optimal Transport for MoEfication
por: Bamba, Udbhav, et al.
Publicado: (2026)

Beyond Uniform Scaling: Exploring Depth Heterogeneity in Neural Architectures
por: T, Akash Guna R., et al.
Publicado: (2024)

Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward
por: Chavan, Arnav, et al.
Publicado: (2024)

Mode-Conditioning Unlocks Superior Test-Time Scaling
por: Wu, Chen Henry, et al.
Publicado: (2025)

Turning LLM Activations Quantization-Friendly
por: Czakó, Patrik, et al.
Publicado: (2025)

Self-Trained Verification for Training- and Test-Time Self-Improvement
por: Wu, Chen Henry, et al.
Publicado: (2026)

Memorization Sinks: Isolating Memorization during LLM Training
por: Ghosal, Gaurav R., et al.
Publicado: (2025)

Reasoning as an Adaptive Defense for Safety
por: Kim, Taeyoun, et al.
Publicado: (2025)

Thermodynamic Diffusion Inference with Minimal Digital Conditioning
por: De, Aditi
Publicado: (2026)

GradientSpace: Unsupervised Data Clustering for Improved Instruction Tuning
por: Sridharan, Shrihari, et al.
Publicado: (2025)

XAMBA: Enabling Efficient State Space Models on Resource-Constrained Neural Processing Units
por: Das, Arghadip, et al.
Publicado: (2025)

GraNNite: Enabling High-Performance Execution of Graph Neural Networks on Resource-Constrained Neural Processing Units
por: Das, Arghadip, et al.
Publicado: (2025)

Weight Ensembling Improves Reasoning in Language Models
por: Dang, Xingyu, et al.
Publicado: (2025)

Pando: Do Interpretability Methods Work When Models Won't Explain Themselves?
por: Zhong, Ziqian, et al.
Publicado: (2026)

Hierarchical Sparse Plus Low Rank Compression of LLM
por: Kumar, Pawan, et al.
Publicado: (2025)

Ray-Tracing for Conditionally Activated Neural Networks
por: Gallicchio, Claudio, et al.
Publicado: (2025)

Test-Time Adaptation Induces Stronger Accuracy and Agreement-on-the-Line
por: Kim, Eungyeup, et al.
Publicado: (2023)

Roll the dice & look before you leap: Going beyond the creative limits of next-token prediction
por: Nagarajan, Vaishnavh, et al.
Publicado: (2025)

When Less is More: 8-bit Quantization Improves Continual Learning in Large Language Models
por: Zhang, Michael S., et al.
Publicado: (2025)

Hodoscope: Unsupervised Monitoring for AI Misbehaviors
por: Zhong, Ziqian, et al.
Publicado: (2026)

Deep Conditional Measure Quantization
por: Turinici, Gabriel
Publicado: (2023)

Base Models Look Human To AI Detectors
por: Xu, Yixuan Even, et al.
Publicado: (2026)

Persona-Conditioned Risk Behavior in Large Language Models: A Simulated Gambling Study with GPT-4.1
por: Dubedy, Sankalp
Publicado: (2026)

Structured Abductive-Deductive-Inductive Reasoning for LLMs via Algebraic Invariants
por: Gilda, Sankalp, et al.
Publicado: (2026)

Activation Sensitivity as a Unifying Principle for Post-Training Quantization
por: Xu, Bruce Changlong
Publicado: (2026)

SmoothRot: Combining Channel-Wise Scaling and Rotation for Quantization-Friendly LLMs
por: Czakó, Patrik, et al.
Publicado: (2025)

Proactively Detecting Threats: A Novel Approach Using LLMs
por: Chawla, Aniesh, et al.
Publicado: (2026)

A Decompilation-Driven Framework for Malware Detection with Large Language Models
por: Chawla, Aniesh, et al.
Publicado: (2026)

A Unified Evaluation of Learning-Based Similarity Techniques for Malware Detection
por: Prasad, Udbhav, et al.
Publicado: (2026)

Mitigating Modal Imbalance in Multimodal Reasoning
por: Wu, Chen Henry, et al.
Publicado: (2025)

Activation Outliers in Transformer Quantization: Reproduction, Statistical Analysis, and Deployment Tradeoffs
por: Kaliaperumal, Pranav Kumar
Publicado: (2026)

InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization
por: Li, Ke, et al.
Publicado: (2026)

ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization
por: Zhao, Weibo, et al.
Publicado: (2024)

SAP: Corrective Machine Unlearning with Scaled Activation Projection for Label Noise Robustness
por: Kodge, Sangamesh, et al.
Publicado: (2024)

Training Verification-Friendly Neural Networks via Neuron Behavior Consistency
por: Liu, Zongxin, et al.
Publicado: (2024)

Robust Calibration For Improved Weather Prediction Under Distributional Shift
por: Gilda, Sankalp, et al.
Publicado: (2024)

Beacon: Post-Training Quantization with Integrated Grid Selection
por: Zhang, Shihao, et al.
Publicado: (2025)

Symmetric Equilibrium Propagation for Thermodynamic Diffusion Training
por: De, Aditi
Publicado: (2026)

Gradient-Free Training of Quantized Neural Networks
por: Cohen, Noa, et al.
Publicado: (2024)