Pierre Stock
9 papers · 2018–2024 · 5 venues · across top CS/AI venues
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Achievements
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π Conference Polyglot Β· Silver π Academic Marathon Β· Sprint π Interdisciplinary Bridge Β· Bronze πΊοΈ Taxonomy Completionist (18) π£ Hot Topic Early Bird
π
Conference Pioneer
Top co-authors
Research topics
Machine Learning
(7)
Artificial Intelligence
(6)
Deep Learning
(5)
Core AI Β· Artificial Intelligence
(5)
Optimization & Theory
(5)
Application Areas
(3)
Techniques
(3)
Security & Privacy
(2)
Architectures
(2)
Differential Privacy
(2)
Learning Paradigms
(2)
Learning Types
(2)
Computer Vision
(1)
Analysis
(1)
Core AI Β· Computer Vision
(1)
Keywords
weight quantization
(3)
differential privacy
(3)
model compression
(3)
convolutional neural network
(2)
federated learning
(2)
quantization-aware training
(2)
batch normalization
(1)
data-free distillation
(1)
neural network optimization
(1)
efficient computing
(1)
numerical optimization
(1)
mechanism design
(1)
communication efficiency
(1)
deep neural network
(1)
gradient compression
(1)
memory efficiency
(1)
scaling law
(1)
privacy-utility trade-off
(1)
weight decay
(1)
privacy budget
(1)
Papers
LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
ACL 2024
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning
ICLR 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design
ICML 2023
TAN Without a Burn: Scaling Laws of DP-SGD
ICML 2023
LeViT: A Vision Transformer in ConvNet's Clothing for Faster Inference
ICCV 2021
Training with Quantization Noise for Extreme Model Compression
ICLR 2021
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
ICLR 2020
Equi-normalization of Neural Networks
ICLR 2019
ConvNets and ImageNet Beyond Accuracy: Understanding Mistakes and Uncovering Biases
ECCV 2018